Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Inf. Model. 2016, 56, 9, 1762-1775

ArticleAugust 31, 2016

Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA
Click to copy article linkArticle link copied!

View Author Information

Open PDF Supporting Information (1)

Journal of Chemical Information and Modeling

Cite this: J. Chem. Inf. Model. 2016, 56, 9, 1762–1775

Click to copy citationCitation copied!

https://doi.org/10.1021/acs.jcim.6b00211

Published August 31, 2016

Request reuse permissions

Abstract

Click to copy section linkSection link copied!

The d-Ala:d-Lac ligase, VanA, plays a critical role in the resistance of vancomycin. Indeed, it is involved in the synthesis of a peptidoglycan precursor, to which vancomycin cannot bind. The reaction catalyzed by VanA requires the opening of the so-called “ω-loop”, so that the substrates can enter the active site. Here, the conformational landscape of VanA is explored by an enhanced sampling approach: the temperature-accelerated molecular dynamics (TAMD). Analysis of the molecular dynamics (MD) and TAMD trajectories recorded on VanA permits a graphical description of the structural and kinetics aspects of the conformational space of VanA, where the internal mobility and various opening modes of the ω-loop play a major role. The other important feature is the correlation of the ω-loop motion with the movements of the opposite domain, defined as containing the residues A149–Q208. Conformational and kinetic clusters have been determined and a path describing the ω-loop opening was extracted from these clusters. The determination of this opening path, as well as the relative importance of hydrogen bonds along the path, permit one to propose some key residue interactions for the kinetics of the ω-loop opening.

Subjects

1 Introduction

Click to copy section linkSection link copied!

The development of bioinformatics has been initially driven not only by the enormous quantity of data that the biologist community was able to produce during the last decades, but also by the necessity of finding approaches to organize and better analyze these huge datasets. Although the protein structures constitute small datasets with respect to many other data encountered in biology, they nevertheless represent a challenge for the data analysis, as the relative positions of atomic coordinates in a protein structure take values in the continuous three-dimensional (3D) space. The large variability of protein features is obvious from the variety of physicochemical properties among a given family of proteins. (1) Furthermore, the full understanding of a protein function requires, in addition of the knowledge of its structure, the knowledge of the internal dynamics and thus of the conformational landscape of the protein, which correspond to large datasets.

Graphs are traditionally used for modeling biological datasets, as for the analysis of protein–protein and molecular interaction networks, (2-9) for description of drug function, (10-16) for the description of interactions within a protein, (17-19) for the description of the hierarchy of local minima in the conformational space. (20-22) In the description of protein conformational space, the determination of such a graph is hampered by the need to (i) simplify the protein local geometry without loss of information and (ii) find a generic approach for graph determination, while preserving the specificity of each protein. In contrast, the description of protein structure and dynamics through graphs would allow one to (i) relate structure description, conformational variability, and protein function; (ii) unify the structural and dynamical representations; and (iii) obtain, for a given protein, a model that could be interfaced with the graphs described at the cellular level, as the interactome network. (23)

In order to investigate the points quoted above, we have been using several processing tools to describe the graphs underlying the structural and dynamical features of the d-Ala:d-Lac (VanA) ligase:

(i)	the self-organizing maps, (24) to convert the conformational space in a two-dimensional (2D) map;
(ii)	the Louvain greedy algorithm, (25) to determine kinetic clusters in the conformational space;
(iii)	the Girvan–Newmann algorithm, to determine contact communities within the protein structure, which was already used in other structural objects; (26, 27) and
(iv)	the analysis of hydrogen bonds within the protein structure, using a machine-learning approach (Random Forest (28)).

The conformational space has been explored using an enhanced sampling approach: the temperature-accelerated molecular dynamics (TAMD). (29-41)

The d-Ala:d-Lac ligase (VanA) is present in cases of resistance to the glycopeptide antibiotic vancomycin in Enterococcus faetium and Staphylococcus aureus. (42, 43) VanA synthesizes a modified precursor d-Ala-d-Lac instead of the usual d-Ala-d-Ala, synthesized by using a d-Ala:d-Ala ligase. (44) This depsipeptide is then fixed at the end of the N-acetyl-muramyl-l-Ala-d-Glu-l-Lys-d-Ala-d-Lac monomers involved in the building of the peptidoglycan, giving rise to a fully efficient cell wall while preventing the binding of vancomycin.

The X-ray crystallographic structure of VanA (45) (Figure 1a) includes the domains N-terminal (residues A2–G121 shown in blue), central (residues C122–S211 shown in red and yellow), and C-terminal (residues G212–A342 shown in black and green). The ω-loop (shown in green in Figure 1a, residues L236–A256) is part of the C-terminal domain and closes the binding site where the ligase enzymatic reaction occurs. The two-layer β-sandwich (residues A149–Q208) is a region opposite to the ω-loop in the structure and colored yellow in Figure 1a. It was called “opposite domain” in a previous work. (46) The binding site is located at the interface between N-terminal, central, and C-terminal domains. Concerted motions of the opposite domain and of the ω-loop allow the opening of the binding cavity to release the product of the catalytic reaction and accept new ligands. (46)

Figure 1. (a) Three-dimensional (3D) view of the X-ray crystallographic structure of VanA, colored according to its domains: the N-terminal [A2–G121] shown in blue, the C-terminal [G212–A342] shown in black, which includes the ω-loop [L236–A256] shown in green, and the central domain [C122–S211] shown in red, which includes the opposite domain [A149–Q208] shown in yellow. The disulfide bridge C52–C64, located in the N-terminal domain, is shown with magenta labels (bottom right). (b) Localization of the collective variables (CV) used for the different TAMD calculations on a cartoon view of VanA extracted at the end of a 10 ns MD trajectory. The three structural CV are shown in orange and the five CV obtained from contact communities calculations are shown in cyan.

The bioinformatics approaches described above have been applied to MD and TAMD trajectories recorded on VanA. Several graph models describing the structural architecture, internal dynamics, and the opening of the ω-loop, have been established. These models give an extended view of the structural and dynamical features of VanA and agree with the experimental knowledge available for the protein function.

2 Materials and Methods

Click to copy section linkSection link copied!

2.1 Molecular Dynamics Simulation

The starting point of the simulations was the X-ray crystallographic structure of the d-Ala:d-Lac ligase (VanA) from Enterococcus faecium BM4147 VanA (PDB ID: 1E4E). (45) The co-crystallized ligands, ADP and phosphinate (1(S)-aminoethyl-(2-carboxypropyl)phosphoryl-phosphinic acid), located in the active site were removed. The C52–C64 disulfide bridge, observed in the crystal was disrupted to be as close as possible to the physiological state of the d-Ala:d-Ala ligase. (47)

The force field CHARMM22 including the correction map (CMAP) (48, 49) was used. The system was neutralized with five Na⁺ counterions. Explicit TIP3P (50) solvent water molecules were added to the systems using a cutoff of 10 Å. The solvated system includes 13585 water molecules. The molecular dynamics (MD) and the temperature-accelerated molecular dynamics (TAMD) trajectories were recorded using NAMD 2.7b2. (51) A cutoff of 12 Å and a switching distance of 10 Å were defined for nonbonded interactions. Long-range electrostatic interactions were calculated with the Particule Mesh Ewald (PME) protocol. (52)

Before starting the initial MD trajectories, the system was initialized in the following way. It was first minimized using 1000 steps, then thermalized by heating the system from 0 to 300 K over 30 ps, with a time step of 1 fs. The system then is equilibrated in the NPT ensemble for 100 ps with a time step of 2 fs before a 40 ns MD simulation.

The analyzed trajectories were recorded in the NPT ensemble with periodic boundary conditions. The temperature was maintained at 300 K using a Langevin thermostat, (53) and the 1 atm pressure was regulated using the Langevin piston Nose–Hoover method. (54, 55) The SHAKE algorithm (56) kept all covalent bonds involving hydrogens rigid, so an integration time step of 2 fs was used for all MD simulations. Atomic coordinates were saved every picosecond.

2.2 TAMD Simulations

At the end of the first 10 ns of the MD trajectory, five independent 30-ns temperature-accelerated molecular dynamics (TAMD) simulations were launched (Table S2 in the Supporting Information). The TAMD approach is an enhanced sampling approach, based on the parallel evolution of the protein coordinates x in a classical MD simulation and of the target values z for the collective variables θ_α(x):

(1)where x are the physical variables (atomic coordinates) of the system, θ(x) are the collective variables, and z the instantaneous target values of the collective variables. M is the mass matrix, V(x) is the empirical classical potential of the system, η^x,z(t) denotes white noise (i.e., Gaussian processes with mean 0 and covariance of ⟨η_α^p(t)η_α′^p(t′)⟩ = δ_αα′δ(t – t′), with p = x,z), κ > 0 is the so-called spring force constant, γ and γ̅ > 0 are friction coefficients of the Langevin thermostats, β^–1 = k_BT, and β̅^–1 = k_BT̅, where k_B is the Boltzmann constant and T and T̅ represent the temperatures.

Equation 1 describes the motion of x and z under the extended potential

(2)It was shown in ref 29 that, by adjusting the parameter κ, so that z(t) ≈ θ(x(t)), and the friction coefficient γ̅ so that the value of z moves slower than that of x, one can generate a trajectory z(t) in z-space that effectively moves at the artificial temperature T̅ on the free-energy hyper-surface F(z), which is defined at the physical temperature T. Hence, by construction, the limiting equation for z(t) in eq 1 samples the distribution e^–β̅F(z). Then, using T̅ > T in eq 1) accelerates the exploration of the free-energy landscape by the z(t) trajectory, as energy barriers can be crossed more easily.

The value for the artificial friction γ̅ on the z variables can be determined following the principle that the separation of time scales between x and z must be such that the x have time to equilibrate before the z values move substantially. In practice, we proceeded as suggested in ref 57, i.e., we ran short standard MD trajectories with the collective variables restrained at θ(x) = z fixed, and monitored the mean force estimators G_j(N) defined for each collective variable j as

(3)where θ_j(x(t_i)) is the instantaneous value at time t_i of the collective variable. The time required for G_j(N) to reach a plateau (see Figure S1 in the Supporting Information) allows one to extract the characteristic time of relaxation of the Cartesian variables to a fixed value of the variables z, and hence an estimate of γ̅ to ensure the time-scales separation γ̅/γ. As the estimator (described in eq 3) converges in 5000 simulation time steps (0.002 ps), a friction γ of 50 ps^–1, corresponding to a characteristic time of 0.02 ps, is sufficient to allow system relaxation.

The TAMD approach was implemented in NAMD using a tcl script. (39, 57) In TAMD, the evolution of the usual MD equation, at 300 K, was coupled to the evolution of collective variables at a much higher temperature. Several sets of collective variables were used, which were all geometric centers located in different protein regions.

The friction coefficient, γ = 0.5 ps^–1, and the physical thermal energy, β^–1 = 0.6 kcal/mol, are the parameters of the conventional Langevin thermostat, which allow one to obtain a simulation temperature of 300 K. The restraint force constant is set to κ = 100 kcal/(mol Å²).

TAMD trajectories were run using a value of 20 kcal mol^–1 for the artificial thermal energy β̅^–1 of the Langevin thermostat attached to the collective variables. This thermal energy corresponds to an artificial temperature T̅ of 10 060 K. Despite the high temperature values used for the Langevin thermostat attached to the collective variables, it is not expected that the folded structure of VanA would be destabilized, as a large friction (γ̅ = 50 ps^–1) is used for this thermostat, along with the high force constant (κ = 100 kcal/(mol Å²) to restraint the collective variable coordinates to the collective variables. In that way, we reduce the risk of system instability due to large deviation of the collective variables θ(x) from their target values z.

2.3 Determination of Contact Communities

The following method has been used to determine the contact communities of VanA along each recorded trajectory. At each trajectory frame, a contact is set up for all α-carbon pairs closer than 12 Å, (58) and the frequency of contacts is calculated along the trajectory. The protein structure is then considered as a graph, where the residues C_α constitute the vertices and the edges are weighted by the frequency of contacts between C_α atoms along the trajectories. An absence of contact is modeled as a nonexisting edge. The Girvan–Newman algorithm, (59) as implemented in the program Python, allows one to divide, in an iterative way, the graph into contact communities. First, all possible shortest paths are calculated between the Cα and the betweenness of each edge, which is defined as the number of shortest paths crossing this edge, is computed. The algorithm then removes the edge exhibiting the most important betweenness and includes the two edge vertices into the same community. The betweenness of all edges affected by the removal is recalculated. Several runs of the algorithm are performed to remove the edge of highest betweenness until no edges remain. At the end of the process, the initial dynamic map of frequency of contacts has been split into contact communities of amino acids that are strongly connected.

2.4 Conformational Analysis of the Simulations Using SOM

The Self-Organizing Maps (SOM) approach (24, 60, 61) was used to cluster the conformations generated along MD and TAMD trajectories. The SOM algorithm allows the mapping of the conformational space on a periodic subspace of reduced dimensions: a 50 × 50 map. 341 × 341 pairwise square Euclidean distance matrices D were calculated for the 341 C_α atoms of VanA, for each frame of the trajectory. To compress the data, a covariance matrix C was computed from each D. Its four eigenvectors, corresponding to the first four significant eigenvalues N_i were kept. For each trajectory frame t, the resulting compressed 4 × 341 matrix D · V_i=1,...,4, stored as a vector V_t, contains the conformational descriptors and is used to cluster the protein conformations. (61)

The SOM was trained in two phases with the following parameters: (i) a map size of 50 × 50 with periodic boundaries, initialized randomly with a constant learning rate of 0.5 and a radius of 6.250 for the first phase (180 000 iterations), and (ii) an exponential decrease of learning rate (starting at 0.25) and radius (starting at 3.125) for the second phase (360 000 iterations). After the random initialization of the map, vectors of conformational descriptors V_t described above, were presented to the map in random order, (46) and the neuron closest to the presented V_t was updated, as well as the neighbor neurons to preserve the coherence of the clustering. At the end of the calculation, each neuron of the SOM contains a average vector ⟨V_t⟩ corresponding to a mixture of clustered protein conformations.

The Unified distance matrix (U-matrix) representation was computed to display the SOM topology on a bidimensional matrix. In the U-matrix, each node shows the local similarity between the corresponding neighboring SOM neurons, i.e., the mean distance between the node and its eight neighbors. A flooding algorithm was then used to aggregate the U-matrix basins, and to reject outside the regions corresponding to nonsimilar neurones, leading to a continuous map representation while preserving the inherent SOM topology. (61)

2.5 Graph Processing of the Self-Organizing Maps

The SOM were additionally processed in two ways in order to determine graphs describing (i) the kinetics of the conformational space sampled and (ii) the opening path between the closed and open conformations of VanA.

The graph related to the kinetics of the conformational space sampled was determined in the following way. A transition matrix is built from the SOM map. The SOM neurons define the microstates, and each structure along a given MD or TAMD trajectory is assigned to a given neuron. The element T_ij of the transition matrix, depicting the transition between neurons i and j, is defined as the number of i → j transitions divided by the number of starts from neuron i. The transition matrix can be represented as a weighted graph, with the weight of the vertex ij being given by T_ij.

The obtained graph is then partitioned using the greedy algorithm of Louvain, (25) in order to maximize the graph modularity. The modularity is a value between −1 and +1, measuring the density of edges inside the partitions, compared to the density of edges outside the partitions. The greedy algorithm of Louvain optimizes the modularity in two phases.

In the first phase, each SOM neuron is assigned to distinct kinetic clusters. Then, for each SOM neuron u, the variation of modularity is evaluated when u is removed from its cluster and placed to the cluster of each of its neighbors. If no gain of modularity is possible, u remains in its cluster. In the second phase, a new graph is built by merging the SOM neurons belonging to the same cluster. The weights of the resulting graph are computed by summing the weights of the links between nodes in the corresponding two clusters.

The opening path between the VanA states displaying open and closed ω-loops was determined in the following way. Edges between SOM neurons were weighted by the value of the corresponding element of the U-matrix, which measures the local similarity between protein conformations. The starting point was the SOM node u corresponding to the starting point of all trajectories, with closed ω-loop. The final point of the path was chosen as the medoid of the SOM kinetic cluster 15 which will be described in section 3.3. The medoid is the neuron whose average distance to all the neurons in the cluster is minimal.

The shortest path is computed using the Dijkstra algorithm, (62) using the similarity between neurons as a distance. Finally, the path defined from SOM neurons was converted to a series of VanA conformations by replacing each neuron by the VanA conformation exhibiting the smallest Euclidean distance between its vector of conformational descriptors V_t and the average of the neuron vector ⟨V_t⟩.

2.6 Analysis of Hydrogen Bonds within VanA

The path describing the ω opening has been analyzed to detect the most critical hydrogen bonds for the conformational change. For that purpose, along the opening path, a representative conformation was extracted from each kinetic cluster obtained above using the Louvain greedy algorithm. (25) This representative conformation was chosen as the medoid of the path conformations belonging to this kinetic cluster.

On each of these VanA conformations, hydrogen bonds have been detected using criteria based on a survey of small-molecule crystal structures. (63) This analysis was performed using the UCSF Chimera package, (64) producing 1623 hydrogen bonds. A hydrogen bond is supposed to be established if the donor–acceptor and the hydrogen–acceptor distances are respectively smaller than 4.0 and 3.0 Å.

A Random Forest (RF) (28) machine learning approach was used to calculate the importance of each hydrogen bond for predicting to which kinetic cluster the representative conformation belongs. The information on established and disrupted hydrogen bonds was encoded as a Boolean vector for each conformation populating the path. The hydrogen bonds were indexed by protein residue numbers. The Boolean vectors were used as descriptors to train the RF. The predicted value for each vector was the identifier of the kinetic cluster.

The RF calculation was performed using the Python package scikit-learn (scikit-learn.org). The number of trees in the forest was set to 10, with a Gini criterion (28) to measure the quality of a split. The number of features used when searching for the best split was set to 40, which is approximately the square root of the length of the Boolean vectors (

≈ 40). The trees are expanded until all leaves are pure. Once the training done, the importance of each hydrogen bond to define a kinetic cluster has been computed.

2.7 Ligand Docking Procedure and GBSA Scoring

The substrates, ATP, d-Ala, d-Lac, d-alanyl-phosphate (d-Ala(P)), the transition-state analogue phosphinate or PHY, the product of the reaction, d-Ala-d-Lac, and the allosteric binder, (65) were formatted in mol2 with Chimera 1.4 (64) and MarvinSketch 5.1 (www.chemaxon.com/products/marvin/marvinsketch) for docking.

UCSF DOCK 6.5 (66-68) was used to perform ligand docking VanA conformations along the opening path obtained as described at the end of the section 2.5.

Chimera (64) was used to add hydrogens, check atom assignment, and assign partial charges consistent with the AMBER-ff99SB force field. (69) Chimera was also used to produce mol2 format files for the ligands and the selected conformations of the receptor. The DMS software program (70, 71) generated the molecular surface of the receptor, using a radius probe of 1.4 Å. Spheres then were calculated around the receptor with the DOCK 6.5 command “sphgen” with radius probe values varying between 1.4 Å and 4 Å. (72) Spheres were selected within a radius of 10 Å around the geometric center defined by the residues E15, K170, R289, N303, E304, N306, which are close to positions observed for the ligands (ADP, phosphinate) in 1E4E. The grid encoding van der Waals and electrostatic interactions was precalculated with the “grid” tool (72) in a box containing the selected spheres. The DOCK program builds up to 500 flexible ligand docking orientations, on the precalculated “grid” interaction map. The ligand poses were then re-scored with the implementation of the Hawkins Molecular Mechanics Generalized Born Surface Area (MM-GBSA) score, (73-77) implemented in UCSF DOCK 6.5. The best scoring solution was kept for each protein–ligand pair.

3 Results

Click to copy section linkSection link copied!

3.1 Choice of Collective Variables from the Structural and Community Domains of VanA

The use of the enhanced sampling approach TAMD requires the definition of collective variables. In the present work, these variables were chosen as geometric centers of α-carbons located in various VanA regions. These regions were detected (Table 1) from an analysis of the X-ray crystallographic structure of VanA (PDB ID: 1E4E) or from the contact communities determined by the Girvan–Newman algorithm, as described in section 2. Starting from these regions, two sets of geometric centers were determined (see Table S1 in the Supporting Information): structural collective variables (CVN-Xr, CVO-Xr, and CVω-Xr) and dynamical collective variables (CVω-Com, CVE0-Com, CVE1-Com, CVM-Com, and CVO-Com). Five independent 30-ns temperature-accelerated molecular dynamics (TAMD) simulations were launched using various combinations of both sets of collective variables (see Table S2 in the Supporting Information).

Table 1. Definition of the Different Domains of Protein VanA^a

domain	residues	determination method
N-terminal-Xr	2–121	structural
C-terminal-Xr	212–342	structural
Central-Xr	122–211	structural
Opposite-Xr	149–208	structural
Omega-Xr	236–256	structural
Ends_0-Com	2–7, 30–39, 69–78, 88–95, 108–120, 330–342	communities
Ends_1-Com	8–29, 40–68, 79–87, 96–103, 310–313	communities
Middle-Com	104–107, 121–147, 220–226, 277–289, 303–309	communities
Opposite-Com	148–210	communities
ω-Com	211–219, 227–276, 290–302, 314–329	communities

The first five domain definitions are derived from the analysis of the X-ray (Xr) crystallographic structure (45)1E4E. The last five domain definitions are the communities obtained using the Girvan–Newman algorithm on the 30-ns MD trajectory.

The structural collective variables CVN-Xr, CVO-Xr, and CVω-Xr (Table S1 and Figure 1) were respectively defined on the N-terminal domain, opposite domain, and ω-loop, chosen from a direct observation of the PDB structure 1E4E. This choice is supported by several observations on X-ray crystallographic structures and MD trajectories. (45-47) First, the ω-loop, containing CVω-Xr, displays diverse orientations in X-ray crystallographic structures of d-Ala:d-Ala ligases. (45) Second, the opposite region (residues 149–208) was chosen to define CVO-Xr, as this region moves apart from the protein core, as published in a previous work. (46)

The dynamical collectives variables were derived from the contact communities calculated using the Girvan–Newman algorithm along a 30-ns MD trajectory: these communities are described in more detail below. The corresponding geometric centers are located in the ω-loop (CVω-Com), in the N-terminal and C-terminal domains (CVE0-Com, CVE1-Com), and in the middle (CVM-Com) and opposite (CVO-Com) domains (see Table S1 and Figure 1).

The contact community analysis based on the Girvan–Newman algorithm allowed one to divide VanA in five communities either in MD or in TAMD simulations, except in TAMD_ON, where four communities were observed (see Figure 2). These communities are variable from one simulation to another, but involve similar protein regions for all trajectories (see Table S3 in the Supporting Information), even though different sets of collective variables were used during each TAMD trajectory. The two Ends_0-Com and Ends_1-Com communities are interlaced in the protein sequence, and contain residues from the structural definition of the N- and C-terminal regions. The Opposite-Com community is located in the opposite domain, while the ω-Com community corresponds to the ω-loop and part of the C-terminal. The last community, Middle-Com (see Table S3), located in the middle of the protein and partially superimposed with the central structural domain Central-Xr (Table 1), is detected in all trajectories except TAMD_ON. The definition of contact communities are slightly different from the definitions of structural domains, except Opposite-Com, almost superimposed to the domain Opposite-Xr (Table 1). The good fit of Opposite-Com to Opposite-Xr is expected as the opposite domain was previously detected from an analysis of MD trajectories. (46)

Figure 2. Communities determined by the Girvan–Newman algorithm (59) along the MD and TAMD trajectories recorded on VanA. The same color code was kept for the communities both on the 3D structures and on the graphs: the communities mainly located in the N-terminal region (numbers 0 and 1) are shown in blue and red; the Middle (number 2) community is shown in magenta, if it exists; the Opposite region is shown in yellow (number 3); the ω-loop and the main part of the C-terminal are shown in green (number 4). Projection of the communities calculated on a 30-ns trajectory of VanA for (a) MD, (c) TAMD_ON, (e) TAMD_ωN, (g) TAMD_OωN, (i) TAMD_MD, and (k) TAMD_5CV. Also shown is a graph of the interconnectivity calculated between the different communities for (b) MD, (d) TAMD_ON, (f) TAMD_ωN, (h) TAMD_OωN, (j) TAMD_MD, and (l) TAMD_5CV. The collective variables (CV) used for TAMD trajectories are represented by orange balls when they were derived from structural calculations and cyan balls if they were obtained from the communities calculations.

The contact communities graph is connected by edges (Figure 2), which depict the frequency of contact between α-carbons belonging to two different communities. The larger the frequency, the thicker the edge. (26, 27) Thus, the edge thickness gives a qualitative indication of the relative influences that the communities have on each other. Overall, the same pattern of influences between communities is observed in all trajectories (Figure 2). The community corresponding to the ω-loop is always strongly linked with the opposite community, as reflected by the high betweenness. This communication is mostly mediated by the middle community (in purple). The opposite domain is itself connected to the Ends communities detected into the N- and C-terminal domains (shown in red and blue in Figure 2).

The definitions of structural, dynamical collective variables and of contact communities determined on the trajectory TAMD_ωN are depicted (Figure 3) using a color code. The definitions corresponding to the opposite domain (yellow) and to the ω-loop (green) are similar for the three sets of definition. Also, similar middle or central domains (magenta) are detected between dynamical collective variables and contact communities.

Figure 3. Definition of collective variables (CV) and of contact communities displayed on the VanA sequence. The first line contains the definition of structural collective variables (CVN-Xr, CVO-Xr, CVω-Xr: see Table S1) determined from an analysis of the structure 1E4E. The second line contains the definition of dynamical collective variables (CVE0-Com, CVE1-Com, CVM-Com, CVO-Com, CVω-Com: Table S1) determined from a community analysis using the Girvan–Newman algorithm over the 30-ns MD trajectory. The third line contains the definition of communities (Ends_Oc, Ends_1c, Middle_c, Opposite_c, ω_c: see Table S3) determined by the Girvan–Newman algorithm on the trajectory TAMD_ωN. The following color code is used. For the structural CV: CVN-Xr (blue), CVO-Xr (yellow), and CVω-Xr (green). For the dynamical CV: CVE0-Com (blue), CVE1-Com (red), CVM-Com (magenta), CVO-Com (yellow), and CVω-Com (green). For the TAMD_ωN communities: Ends_Oc (blue), Ends_1c (red), Middle_c (magenta), Opposite_c (yellow), and ω_c (green).

3.2 Conformational Clustering of the Conformational Landscape

The existence of α helices and β strands has been monitored along the MD and TAMD trajectories (see Table S4 in the Supporting Information). Most of the secondary structure elements are present more than 80% of the time, at the exception of 5 β-strands, which are destabilized in the MD as well as in the TAMD trajectories. Thus, the folded structure of VanA is not specifically altered by the use of the TAMD, as has been already noticed in section section 2.2.

The 180 000 frames of VanA generated either along the MD or TAMD trajectories were subjected to a SOM clustering. (46, 61) The analysis of SOM permits one to determine six clusters of conformations (see Figure 4). For each cluster, the average VanA conformation has been drawn in tube representation, where the tube width and color depend on the conformational local variability (root-mean-square fluctuation (RMSF), Å) within the cluster. The color varies from blue (RMSF close to 1 Å) to red, corresponding to the maximal fluctuation in a given cluster (e.g., cluster 1, 13 Å; cluster 2, 13.3 Å; cluster 3, 15.7 Å; cluster 4, 7.9 Å; cluster 5, 8.0 Å; cluster 6, 8.4 Å). A permanent feature of the entire conformational landscape of VanA is the large internal mobility of the ω-loop. This agrees with the apo form of VanA simulated: the ω-loop tendency to open is expected to play an important role in the substrate processing.

Figure 4. Clustering of VanA conformations sampled along MD and TAMD trajectories, using SOM. The root mean square deviation (RMSD) from the starting conformation of the trajectories is shown in a prune-green heat map (in Å). The conformation sets associated with the medoid of each cluster are depicted in putty cartoons. On the cartoons, the root-mean-square fluctuation (RMSF) of the backbone is represented by the width of the main chain and by a blue–green–red color scale corresponding to the RMSF values within the corresponding SOM cluster.

Cluster 4 contains the starting point of MD and TAMD trajectories. The average conformation of this cluster is characterized by three regions displaying large local RMSF: the ω-loop, the opposite domain, and three loops [residues I43–V48], [residues P71–H76], [residues N83–H84].

A first series of clusters, represented by clusters 1, 2, and 3, displays significant opening of the ω-loop, with the loop being the most open in clusters 1 and 3. In all of these clusters, the protein internal mobility remains concentrated on the ω-loop (with maximal RMSF values of 13 Å in cluster 1 and 15.7 Å in cluster 3) and the other regions are much less mobile, except the opposite domain (maximal RMSF value of 8.0 Å), the other maxima remaining ∼4–5 Å. Thus, after only 30 ns of simulation, the TAMD trajectories have been able to reach conformations displaying a wide opening of the ω-loop. These conformations are similar to the X-ray crystallographic structures published on the TtDdl d-Ala:d-Ala ligase (PDB ID: 2YZG). (47)

The second series of clusters, which is represented by clusters 5 and 6, displays conformations with semiopen or semiclosed ω-loop, similar to the X-ray crystallographic structure of the d-Ala:d-Ala ligase in ref 47 (PDB ID: 2ZDG). The averaged conformations of clusters 5 and 6 display large mobility of the ω-loop, as well as that of a few regions of the protein: the opposite domain and the three loops previously detected in cluster 4: [residues I43–V48], [residues P71–H76], [residues N83–H84].

The various trajectories explored the U-matrix differently (see Figure 5). The larger cluster, cluster 4, was sampled by the different trajectories, but each one sampled distinct areas. The MD trajectory explored mainly cluster 4, keeping the coordinate RMSD value as low as 2.5 Å, with respect to the starting point (Figure 4), and performing few incursions into cluster 6. This result agrees with the previously recorded MD trajectories in the absence of the disulfide bridge C52–C64. (46)

Figure 5. Detailed exploration of the SOM map by each trajectory. The starting points are shown in pink and the ending ones are shown in magenta. The blue–green–red color scale represents the local root-mean-square deviation (RMSD), from the starting structure for each structure (values shown are given in Å).

Although all TAMD trajectories started from the same conformation, the different choices for the collective variables, as well as the random evolution of MD simulations, induced distinct explorations of the conformational space. In that respect, three main behaviors were observed. The trajectories TAMD_ON and TAMD_5CV visited mainly cluster 4, containing the starting conformation. The trajectories TAMD_ωN and TAMD_OωN explored clusters 1, 2, and 3, corresponding to the opening of the ω-loop. The trajectory TAMD_MN explored regions 5 and 6. Therefore, it seems that the geometric center of the ω-loop is a required collective variable to obtain the loop opening. Frames extracted from TAMD_ΩN are plotted in Figure S2 in the Supporting Information, and reveals that, before the full opening, the ω-loop undergoes a sideways movement.

Overall, the cluster analysis of MD and TAMD trajectories provides an exploration of several possible models for ω-loop mobility. Indeed, protein conformations with fully open loop are obtained along with conformations displaying mobile closed ω-loop, corresponding to several conformational states explored by apo VanA.

3.3 Kinetic Clustering of the VanA Conformational Space

The opening of the VanA binding cavity was monitored by following the values of the angles

and

between the centers of mass of the entire protein VanA (C), of the opposite domain (O), of the N-terminal (N), and of ω-loop (ω) (Figure 6a). The values of

and

angles were projected on the U-matrix (see Figures 6b and 6c). An increased value for

corresponds to an opening of the ω-loop, while an increased value for

corresponds to a displacement of the opposite domain apart from the VanA structure core.

Figure 6. (a) Tube representation of VanA with the ω-loop in green and the opposite domain in yellow. Their own centers of mass is marked with a ball of the same color and respectively called ω and O. The center of mass of the entire protein VanA is called C (shown in red) and the center of mass of the N-terminal region, called N (shown in blue). (b, c) Projections of the angles on the SOM using a prune-green heat map: (panel (b)) and (panel (c)). The angles are expressed in degrees.

Some of the structural clusters previously determined from the SOM analysis (Figure 4) display homogeneous angle values while other clusters show much more heterogeneous values (see Figures 6b and 6c).

Cluster 3, which contains some of the most open conformations of VanA (Figure 4) is very homogeneous. It exhibits the widest opening (∼55°) for the angle

(Figure 6c), while

(Figure 6b) is shrunk with a value of ∼52°, showing the opposite domain moving apart, with respect to the protein core, while the ω-loop is still closed. Unlike cluster 3, clusters 1 and 2, containing open ω-loops, display quite heterogeneous angle values. The

and

are mostly mirrored, with large

values (green regions in Figure 6c) corresponding to small

values (violet regions in Figure 6b) and vice versa. This is the sign of an anticorrelation between the ω-loop and opposite domain displacements. Nevertheless, some regions of Figures 6b and 6c in clusters 1 and 2 display the same color, corresponding to simultaneous shrinkage or expansion of the two protein domains. For the conformations displaying the most closed ω-loop, sampled in clusters 4, 5, and 6, there is mainly little opening of the angles

and

To analyze the kinetics of the conformational exchange in VanA, the protein conformations were clustered by the Louvain greedy algorithm, taking into account the time order of the dynamic simulations, as described in section 2. In that way, 15 individual kinetic clusters were determined (see Figure 7). The conformations populating each kinetic cluster were sampled along the same trajectory, which is a sign that the different TAMD trajectories explored various aspects of the conformational kinetics. The division of SOM according to the kinetic clusters (Figure 7) display patterns quite similar to the ones observed for the projection of the angle

on the SOM (see Figures 6b and 6c), which proves that the overall system kinetics is mainly determined by these angle variations. However, the kinetics clustering brings additional information, with respect to the conformational clustering performed by SOM. Indeed, three clusters (1, 5, and 7) display nonconnected regions on the SOM, respectively labeled 1 and 1′, 5, 5′, and 5″, and 7, 7′, and 7″ on Figure 7, putting in evidence fast conformational equilibrium between distinct conformational regions. The representative conformations extracted from the nonconnected regions of each of three clusters, display conformational variability in precise regions of VanA, as the L and ω loops and the opposite domain (O). Different types of movements for these regions are observed within the three clusters, as shown by the superimposed representative conformations (Figure 7).

Figure 7. Kinetic clustering of the VanA conformation using the Louvain greedy algorithm on the SOM neurones. A given color is associated with each of the 15 obtained clusters. For the three clusters, including nonconnected regions (1, 5, and 7), the disconnected regions are labeled, respectively, as 1 and 1′, 5 to 5″, and 7 to 7′′. The representative conformations corresponding to each disconnected region are drawn superimposed in cartoons.

3.4 A Path Describing the ω-Loop Opening

Starting from the kinetic clustering of SOM map and using a procedure described in section 2, a path relating the conformations of VanA with closed and open ω-loop has been traced on the U-matrix (see Figure 8a). The opening path starts from the kinetic cluster 5′ (Figure 7), passes through clusters 7′, 2, and 3, and ends up in cluster 15. The conformations sampled along this path correspond to a slight translational move of the ω-loop (conformational cluster 2 in Figure 4) and then to a rotation of the loop on the side (conformational cluster 1 in Figure 4). Note that the path through conformational clusters 2 and 1 presents the advantage of permitting a large opening, which allows the substrates to easily enter into the active site.

Figure 8. (a) Opening path traced on the U-matrix. The medoids of each clusters, labeled from A to F, are shown in red and their minimum spanning link is shown in red. (b) GBSA score (in kcal/mol) for the molecules involved in the enzymatic reaction: the substrates d-Ala, d-Ala-(P), d-Lac; the reaction intermediate homologous, PHY; the product of the enzymatic reaction d-Ala-d-Lac; and an allosteric inhibitor. (65) The GBSA score is plotted along the conformations labeled from 0 to 60, extracted from the opening path.

Since the opening of the VanA binding site is directly related to the protein function, we analyzed the path with respect to the interaction of VanA with the substrates, inhibitors, and reaction intermediate. The relative importance of hydrogen bonds within VanA along the path then was statistically evaluated, and connected to experimental observations.

Several ligands (d-Ala, d-Ala(P)), d-Lac, PHY, d-Ala-d-Lac, and an allosteric inhibitor (65)) were docked into the VanA conformations extracted from the path and the poses scored using the GBSA interaction energy (Figure 8b), (75, 76) according to the procedure described in section 2. The score profile displayed by the allosteric inhibitor (green curve in Figure 8b) is quite negative and constant. Similarly, the score profile of d-Ala (red curve in Figure 8b) is also negative and does not display much variation along the path, which is in agreement with the fact that d-Ala is not specific of VanA, but rather binds to all proteins of the d-Ala:d-Lac ligase family. In contrast, the other ligands—d-Ala(P), d-Lac, PHY, and d-Ala-d-Lac—all display profiles, becoming mostly negative in cluster E of the path, after the ω-loop opening (see Figure 8b). Before this opening, the reaction product d-Ala-d-Lac (orange curve in Figure 8b) displays repulsion for VanA, which agrees with the release of the product after reaction. The intermediate of reaction, PHY, displays a behavior similar to that of the other compounds.

Six conformations, labeled A to F, were picked up in each of the kinetic clusters crossed by the path (Figure 8a). On these conformations, a Random Forest approach, described in section 2, was used to determine the relative importance of hydrogen bonds for the kinetic cluster prediction (Figure 9). The most important hydrogen bonds are mainly located in the N-terminal domain, in the opposite domain, and in the ω-loop, which reflects the displacements of these domains described above. In addition, some important hydrogen bonds are observed in the C-terminal region.

Figure 9. Most important hydrogen bonds for the prediction of the kinetic cluster along the opening path. The protein structure is displayed in trace, with the Opposite domain (residues [149–208]) colored orange and the ω-loop (residues [236–256]) colored green. The hydrogen bonds within the ω-loop and the opposite domain are colored cyan, and the hydrogen bonds between these protein domains and other protein regions are colored red. The other hydrogen bonds are gray.

The hydrogen bonds connecting residues from different regions have been colored red in Figure 9. From this outline, the breaking of interactions between protein domains can be followed along the opening path in order to give a description of the kinetic events. The two interactions E250–K22 (between ω-loop and N-terminal region) and E207-Y137 (between the opposite domain and the N-terminal region) are broken in the protein conformation labeled C (Figure 9). On the other hand, hydrogen bonds E207–Y137, K203–D132, R174–D105, and, to a lesser extent, R174–E104 are formed in the two conformations E and F at the end of the path. The change from the first set of hydrogen bonds to the second set gives a description of the opening, involving only few residues, and can be compared to the patterns of experimental mutations observed for VanA.

The E250A mutation induces a slight decrease in experimental catalytic efficiency, (78) which would agree with the importance of the E250–K22 interaction along the opening of the ω-loop. The only limited decrease experimentally observed could arise from a possible reorganization of the VanA structure, which would be due to the presence of residues compensating for the mutation effect. Besides, in the X-ray crystallographic structure of VanA, (45) it was observed that the residues E15, S177, and H244 are involved in a network of hydrogen bonds preventing the entrance of water molecules that could impair the catalytic reaction by hydrolyzing the ligands. The residues K22 and E250 detected in the present analysis, are located, respectively, in the vicinity of E15 and H244, and could play a similar role.

The analysis of the trajectories in the frame of graph theory has permitted the determination of an opening path of VanA, allowing the entrance of substrates in the binding site. The path found agrees with the interaction energy profiles observed for various VanA ligands. The relative importance of hydrogen bonds is supported by some experimental observations.

4 Discussion

Click to copy section linkSection link copied!

The d-Ala:d-Lac ligase VanA was analyzed by molecular modeling and various algorithmic tools, in order to obtain a phenomenological description of the protein internal dynamics and conformational landscape, based on graph models.

The comparison of MD and TAMD trajectories reveals the efficiency of TAMD to perform enhanced sampling of the protein conformational space. As expected, the regions of conformational space explored during TAMD trajectories are closely dependent on the collective variables used. In particular, the opening of the ω-loop seems to be favored if a geometric center of the ω-loop is included into the collective variables. The exploration of the conformational landscape has permitted us to describe two different modes of ω-loop opening: in one mode, ω opens through a translation, whereas in the other, a translation of ω is followed by a rotation.

The partial opening of the ω-loop has been previously (46) observed spontaneously in MD trajectories in the presence of the crystallographic disulfide bridge C52–C64. (45) The moving of the opposite domain, closely related to the opening of the active site, was also observed in these MD trajectories. One should notice that Roper et al. (45) mentioned that this disulfide bridge was unexpected, because VanA is a bacterial intracellular enzyme that should behave in a reducing environment incompatible with the formation of the bridge. The enhanced sampling approach taken here made it possible to observe the opening in the absence of disulfide bridge. The dynamics features observed along the opening path, as the mobility of the opposite domain, are similar to the observations previously made (46) in the presence of the disulfide bridge.

The protein internal dynamics along the opening of the active site seems to be closely related to the relative mobility of the ω-loop and of the opposite domain, as shown by the conformational clustering (Figure 4), by the importance of the angles

and

(Figure 6), to describe the protein kinetics (Figure 7), and by the analysis of hydrogen bonds along the opening path (Figures 8 and 9).

MD and TAMD trajectories of d-Ala:d-Lac ligase VanA have been analyzed using various algorithms. Graph models describe the protein architecture and behavior in the conformational landscape, as well as along the conformational change related to the opening of the ω-loop.

The contact communities detected by analysis of the contacts along the trajectories display a pattern of connections relating the ω-loop to the middle domain, which acts as a hub to establish connection to the opposite and the N- and C-terminal domains. This pattern is conserved in most of the trajectories, whereas contrasted internal dynamics are observed in these protein regions over the conformational space (Figure 4). Indeed, the ω-loop is always quite mobile whereas other protein regions display large (clusters 5 and 6) to small (clusters 1, 2 and 3) internal mobility (Figure 4).

The various graphs obtained on the contact communities, or on the SOM, display characteristics similar to those observed in other bioinformatics graphs obtained in different contexts, for example, in hub, Middle-Com, observed in the graph of contact communities (Figure 2). Such hubs have been also observed in protein–protein interaction networks. (79) The graph of hydrogen bonds along the opening path reveals that all residues establishing discriminating hydrogen bonds are connected to <4 other residues (Figure 9), a property of ”small world” also encountered in chemo-informatics networks based on the ligand-set similarities. (80)

Several approaches have been proposed in the literature to describe the conformational space of proteins as graph of local minima. The analysis performed in ref 22 is based on Principal Component Analysis (PCA) of protein motion. However, the PCA-based analysis detects only linear correlation, whereas SOM can capture nonlinear correlations. The method proposed here is related to the Conformational Space Network (CSN), which was proposed by Yin et al. (21) However, these authors used discrete structural class to cluster conformations. Similarly, in ref 20, the structures were clustered using an all-atom RMSD cutoff of 2.0 Å. In the present paper, we defined the so-called microstates as the elements of the SOM grid. This avoids having to define arbitrary structural classes to cluster the conformations. In addition, from an analysis of conformational transitions between SOM neurons, a method to detect the kinetics cluster is proposed, and put in evidence fast conformational exchange.

The graphs proposed here could be used in a systematic way in proteins for which structural information can be obtained, in order to insert these protein structural graphs into larger graphs as the ones observed in protein–protein interaction networks. Such model stacking would permit to relate directly phenotypic information to physicochemical interactions at the atomic level.

In the case of VanA, the graphs provide a model of the open/closed motion of the ω-loop, allowing one to perform the synthesis between various information. The influence of specific residues and/or conformations in such graphs provides candidates for directed mutagenesis studies.

The MD and TAMD trajectories allows an exploration of the VanA conformational space, which induces the observation of the ω-loop opening. As the closed loop blocks the entrance of the active site, understanding the way the loop is opening gives a qualitative view of the kinetics of the VanA enzymatic function. In the enhanced sampling approach, the time scale of opening events observed along TAMD trajectories is biased and cannot be used to give quantitative information on the opening kinetics. However, on the other hand, the conformations extracted along the opening path of the ω-loop, can be used for docking purposes. Indeed, during the ω-loop opening, the entire architecture of the VanA structure, as well as the active site geometry change. Docking ligands on the active site pocket modified by the ω-loop opening would block this site into an inactive conformation and would orient the docking prediction toward effective inhibitors of the VanA function. The protein conformations sampled during the opening path are available from the authors upon request.

5 Conclusion

Click to copy section linkSection link copied!

The d-Ala:d-Lac ligase VanA have been exhaustively investigated by molecular dynamics and enhanced sampling simulations, in order to propose outlines of (i) protein architecture and (ii) protein conformational landscape. These two types of analyses have been conducted in parallel and give consistent results. The conformational landscape of VanA is characterized by a large mobility of the ω-loop, which displays different translational and rotational motions, with respect to the remaining part of the protein. This conformational view of the landscape is completed by a slightly different kinetic view, which fully agrees with an angular description of the relative mobility of the opposite domain and ω-loop. The importance of the relative motions of the opposite domain and ω-loop is further enforced by the contact communities analysis of the protein structure, showing a large influence between these two regions. Overall, the numerical and statistical tools used here provide parallel descriptions of the protein structure and of the protein conformational landscape, which are in global agreement.

Supporting Information

Click to copy section linkSection link copied!

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.6b00211.

Variation of the mean force estimator G_j(N), used to determine the characteristic time of relaxation of the Cartesian variable, and hence to give an estimate of γ̅ in TAMD to ensure the time-scales separation γ̅/γ (Figure S1); conformations of VanA extracted along the trajectory TAMD_ΩN and displaying an opening of the loop ω (Figure S2); definition of the collectives variables used during the TAMD trajectories (Table S1); list of TAMD trajectories along with their corresponding sets of collective variables (Table S2); definition of the different communities calculated using the Girvan–Newman algorithm over the MD and TAMD trajectories (Table S3); and time percentage formation of the α helices and β strands along MD and TAMD trajectories (Table S4) (PDF)

ci6b00211_si_001.pdf (0.99 MB)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

Click to copy section linkSection link copied!

Corresponding Author
- Thérèse E. Malliavin - Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528, 25, rue du Dr Roux, 75015 Paris, France; Email: [email protected]
Authors
- Nathalie Duclert-Savatier - Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528, 25, rue du Dr Roux, 75015 Paris, France
- Guillaume Bouvier - Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528, 25, rue du Dr Roux, 75015 Paris, France
- Michael Nilges - Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528, 25, rue du Dr Roux, 75015 Paris, France
Author Contributions
These authors contributed equally to the work.
Notes
The authors declare no competing financial interest.

Acknowledgment

Click to copy section linkSection link copied!

This work was funded by the European Union (No. FP7-IDEAS-ERC 294809 to M.N.). CNRS and Institut Pasteur are acknowledged for funding.

References

Click to copy section linkSection link copied!

This article references 80 other publications.

1
Tóth-Petróczy, Á; Tawfik, D. S. The Robustness and Innovability of Protein Folds Curr. Opin. Struct. Biol. 2014, 26, 131– 138 DOI: 10.1016/j.sbi.2014.06.007
Google Scholar
1
The robustness and innovability of protein folds
Toth-Petroczy, Agnes; Tawfik, Dan S.
Current Opinion in Structural Biology (2014), 26 (), 131-138CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
A review. Assignment of protein folds to functions indicates that >60% of folds carry out one or two enzymic functions, while few folds, for example, the TIM-barrel and Rossmann folds, exhibit hundreds. Are there structural features that make a fold amenable to functional innovation (innovability). Do these features relate to robustness - the ability to readily accumulate sequence changes. We discuss several hypotheses regarding the relationship between the architecture of a protein and its evolutionary potential. We describe how, in a seemingly paradoxical manner, opposite properties, such as high stability and rigidity vs. conformational plasticity and structural order vs. disorder, promote robustness and/or innovability. We hypothesize that polarity - differentiation and low connectivity between a protein's scaffold and its active-site - is a key prerequisite for innovability.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXht1ylt7zP&md5=0ab133a3c623c884c82831f018dc983d
2
Winterbach, W.; Van Mieghem, P.; Reinders, M.; Wang, H.; de Ridder, D. Topology of Molecular Interaction Networks BMC Syst. Biol. 2013, 7, 90 DOI: 10.1186/1752-0509-7-90
Google Scholar
2
Topology of molecular interaction networks
Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick
BMC Systems Biology (2013), 7 (), 90/1-90/15CODEN: BSBMCC; ISSN:1752-0509. (BioMed Central Ltd.)
A review. Mol. interactions are often represented as network models which have become the common language of many areas of biol. Graphs serve as convenient math. representations of network models and have themselves become objects of study. Their topol. has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that mol. interaction network topol. is related to biol. function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of mol. interactions considered and themetrics used to study them. It is unclear whether global network topol. drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artifact of representing complex mol. interaction networks as graphs. Nevertheless, network biol. has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that mol. interaction networks can be naturally decompd. into subsystems (such as modules and pathways), topol. is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biol. network topol. to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of mol. interaction networks further.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXltlGnt7c%253D&md5=8e6a5e5d324d7891b7dd3fa6ebba09e6
3
Zhang, Y.; Gao, P.; Yuan, J. S. Plant Protein–Protein Interaction Network and Interactome Curr. Genomics 2010, 11, 40 DOI: 10.2174/138920210790218016
Google Scholar
3
Plant protein-protein interaction network and interactome
Zhang, Yixiang; Gao, Peng; Yuan, Joshua S.
Current Genomics (2010), 11 (1), 40-46CODEN: CGUEA8; ISSN:1389-2029. (Bentham Science Publishers Ltd.)
A review. Protein-protein interaction network represents an important aspect of systems biol. The understanding of the plant protein-protein interaction network and interactome will provide crucial insights into the regulation of plant developmental, physiol., and pathol. processes. In this review, we will first define the concept of plant interactome and the protein-protein interaction network. The significance of the plant interactome study will be discussed. We will then compare the pros and cons for different strategies for interactome mapping including yeast two-hybrid system (Y2H), affinity purifn. mass spectrometry (AP-MS), bimol. fluorescence complementation (BiFC), and in silico prediction. The application of these platforms on specific plant biol. questions will be further discussed. The recent advancements revealed the great potential for plant protein-protein interaction network and interactome to elucidate mol. mechanisms for signal transduction, stress responses, cell cycle control, pattern formation, and others. Mapping the plant interactome in model species will provide important guideline for the future study of plant biol.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjtFajtrw%253D&md5=03d57d4ebe2c4ea332b5dcadaecd4414
4
Rual, J.-F.; Venkatesan, K.; Hao, T.; Hirozane-Kishikawa, T.; Dricot, A.; Li, N.; Berriz, G. F.; Gibbons, F. D.; Dreze, M.; Ayivi-Guedehoussou, N.; Klitgord, N.; Simon, C.; Boxem, M.; Milstein, S.; Rosenberg, J.; Goldberg, D. S.; Zhang, L. V.; Wong, S. L.; Franklin, G.; Li, S.; Albala, J. S.; Lim, J.; Fraughton, C.; Llamosas, E.; Cevik, S.; Bex, C.; Lamesch, P.; Sikorski, R. S.; Vandenhaute, J.; Zoghbi, H. Y.; Smolyar, A.; Bosak, S.; Sequerra, R.; Doucette-Stamm, L.; Cusick, M. E.; Hill, D. E.; Roth, F. P.; Vidal, M. Towards a Proteome-Scale Map of the Human Protein–Protein Interaction Network Nature 2005, 437, 1173– 1178 DOI: 10.1038/nature04209
Google Scholar
4
Towards a proteome-scale map of the human protein-protein interaction network
Rual, Jean-Francois; Venkatesan, Kavitha; Hao, Tong; Hirozane-Kishikawa, Tomoko; Dricot, Amelie; Li, Ning; Berriz, Gabriel F.; Gibbons, Francis D.; Dreze, Matija; Ayivi-Guedehoussou, Nono; Klitgord, Niels; Simon, Christophe; Boxem, Mike; Milstein, Stuart; Rosenberg, Jennifer; Goldberg, Debra S.; Zhang, Lan V.; Wong, Sharyl L.; Franklin, Giovanni; Li, Siming; Albala, Joanna S.; Lim, Janghoo; Fraughton, Carlene; Llamosas, Estelle; Cevik, Sebiha; Bex, Camille; Lamesch, Philippe; Sikorski, Robert S.; Vandenhaute, Jean; Zoghbi, Huda Y.; Smolyar, Alex; Bosak, Stephanie; Sequerra, Reynaldo; Doucette-Stamm, Lynn; Cusick, Michael E.; Hill, David E.; Roth, Frederick P.; Vidal, Marc
Nature (London, United Kingdom) (2005), 437 (7062), 1173-1178CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Systematic mapping of protein-protein interactions, or interactome mapping, was initiated in model organisms, starting with defined biol. processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topol. and dynamic features of interactome networks that relate to known biol. properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of ∼8,100 currently available Gateway-cloned open reading frames and detected ∼2,800 interactions. This data set, called CCSB-HI1, has a verification rate of ∼78% as revealed by an independent co-affinity purifn. assay, and correlates significantly with other biol. attributes. The CCSB-HI1 data set increases by ∼70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-assocd. proteins. This work represents an important step toward a systematic and comprehensive human interactome project.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhtFahtLzP&md5=5485dfc7a711b2c7f649402475f1046d
5
Li, S.; Armstrong, C. M.; Bertin, N.; Ge, H.; Milstein, S.; Boxem, M.; Vidalain, P.-O.; Han, J.-D. J.; Chesneau, A.; Hao, T.; Goldberg, D. S.; Li, N.; Martinez, M.; Rual, J.-F.; Lamesch, P.; Xu, L.; Tewari, M.; Wong, S. L.; Zhang, L. V.; Berriz, G. F.; Jacotot, L.; Vaglio, P.; Reboul, J.; Hirozane-Kishikawa, T.; Li, Q.; Gabel, H. W.; Elewa, A.; Baumgartner, B.; Rose, D. J.; Yu, H.; Bosak, S.; Sequerra, R.; Fraser, A.; Mango, S. E.; Saxton, W. M.; Strome, S.; van den Heuvel, S.; Piano, F.; Vandenhaute, J.; Sardet, C.; Gerstein, M.; Doucette-Stamm, L.; Gunsalus, K. C.; Harper, J. W.; Cusick, M. E.; Roth, F. P.; Hill, D. E.; Vidal, M. A Map of the Interactome Network of the Metazoan C. Elegans Science 2004, 303, 540– 543 DOI: 10.1126/science.1091403
Google Scholar
5
A Map of the Interactome Network of the Metazoan C. elegans
Li, Siming; Armstrong, Christopher M.; Bertin, Nicolas; Ge, Hui; Milstein, Stuart; Boxem, Mike; Vidalain, Pierre-Olivier; Han, Jing-Dong J.; Chesneau, Alban; Hao, Tong; Goldberg, Debra S.; Li, Ning; Martinez, Monica; Rual, Jean-Francois; Lamesch, Philippe; Xu, Lai; Tewari, Muneesh; Wong, Sharyl L.; Zhang, Lan V.; Berriz, Gabriel F.; Jacotot, Laurent; Vaglio, Philippe; Reboul, Jerome; Hirozane-Kishikawa, Tomoko; Li, Qianru; Gabel, Harrison W.; Elewa, Ahmed; Baumgartner, Bridget; Rose, Debra J.; Yu, Haiyuan; Bosak, Stephanie; Sequerra, Reynaldo; Fraser, Andrew; Mango, Susan E.; Saxton, William M.; Strome, Susan; van den Heuvel, Sander; Piano, Fabio; Vandenhaute, Jean; Sardet, Claude; Gerstein, Mark; Doucette-Stamm, Lynn; Gunsalus, Kristin C.; Harper, J. Wade; Cusick, Michael E.; Roth, Frederick P.; Hill, David E.; Vidal, Marc
Science (Washington, DC, United States) (2004), 303 (5657), 540-544CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purifn. assays exptl. validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains ∼5500 interactions. Topol. and biol. features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biol. hypotheses.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXlt1emtA%253D%253D&md5=bdd8075e085c295f7aa4dafde0a046fb
6
Ho, Y.; Gruhler, A.; Heilbut, A.; Bader, G. D.; Moore, L.; Adams, S.-L.; Millar, A.; Taylor, P.; Bennett, K.; Boutilier, K.; Yang, L.; Wolting, C.; Donaldson, I.; Schandorff, S.; Shewnarane, J.; Vo, M.; Taggart, J.; Goudreault, M.; Muskat, B.; Alfarano, C.; Dewar, D.; Lin, Z.; Michalickova, K.; Willems, A. R.; Sassi, H.; Nielsen, P. A.; Rasmussen, K. J.; Andersen, J. R.; Johansen, L. E.; Hansen, L. H.; Jespersen, H.; Podtelejnikov, A.; Nielsen, E.; Crawford, J.; Poulsen, V.; Sørensen, B. D.; Matthiesen, J.; Hendrickson, R. C.; Gleeson, F.; Pawson, T.; Moran, M. F.; Durocher, D.; Mann, M.; Hogue, C. W. V.; Figeys, D.; Tyers, M. Systematic Identification of Protein Complexes in Saccharomyces Cerevisiae by Mass Spectrometry Nature 2002, 415, 180– 183 DOI: 10.1038/415180a
Google Scholar
6
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry
Ho, Yuen; Gruhler, Albrecht; Hellbut, Adrian; Bader, Gary D.; Moore, Lynda; Adams, Sally-Un; Millar, Anna; Taylor, Paul; Bennett, Kelryn; Boutiller, Kelly; Yang, Lingyun; Wolting, Cheryl; Donaldson, Ian; Schandorff, Soren; Shewnarane, Juanita; Vo, Mai; Taggartt, Joanne; Goudreault, Marilyn; Muskat, Brenda; Alfarano, Cris; Dewar, Danlelle; Lin, Zhen; Michallckova, Katerina; Willems, Andrew R.; Sassi, Holly; Nielsen, Peter A.; Rasmussen, Karina J.; Andersen, Jens R.; Johansen, Lens E.; Hansen, Lykke H.; Jespersen, Hans; Podtelejnikov, Alexandre; Nielsen, Eva; Crawford, Janne; Poulsen, Vibeke; Sorensen, Birgitte D.; Matthlesen, Jesper; Hendrickson, Ronald C.; Gleeson, Frank; Paweson, Tony; Moran, Michael F.; Durocher, Daniel; Mann, Matthias; Hogue, Christopher W. V.; Figeys, Daniel; Tyers, Mike
Nature (London, United Kingdom) (2002), 415 (6868), 180-183CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 assocd. proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signaling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an av. threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity obsd. in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38Xms1SnsA%253D%253D&md5=13ac165a0eb6cfeb2c4b5d17619a797d
7
Rain, J.-C.; Selig, L.; De Reuse, H.; Battaglia, V.; Reverdy, C.; Simon, S.; Lenzen, G.; Petel, F.; Wojcik, J.; Schächter, V.; Chemama, Y.; Labigne, A.; Legrain, P. The Protein–Protein Interaction Map of Helicobacter Pylori Nature 2001, 409, 211– 215 DOI: 10.1038/35051615
Google Scholar
7
The protein-protein interaction map of Helicobacter pylori
Rain, Jean-Christophe; Selig, Luc; De Reuse, Hilde; Battaglia, Veronique; Reverdy, Ciline; Simon, Stophane; Lenzen, Gerlinde; Petel, Fablen; Wojcik, Jerime; Schachter, Vincent; Chemama, Y.; Labigne, Agnes; Legrain, Pierre
Nature (London) (2001), 409 (6817), 211-215CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function. As elementary constituents of cellular protein complexes and pathways, protein-protein interactions are key determinants of protein function. Here we have built a large-scale protein-protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides. Over 1200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The detn. of a reliability score for every single protein-protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biol. pathways.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXlvFSrtA%253D%253D&md5=1ba7c0dce240ee469894f2374d9c5029
8
Shen, R.; Guda, C. Applied Graph-Mining Algorithms to study Biomolecular Interaction Networks BioMed Res. Int. 2014, 2014, 1 DOI: 10.1155/2014/439476
Google Scholar
There is no corresponding record for this reference.
9
Han, J.-D. J.; Bertin, N.; Hao, T.; Goldberg, D. S.; Berriz, G. F.; Zhang, L. V.; Dupuy, D.; Walhout, A. J.; Cusick, M. E.; Roth, F. P.; Vidal, M. Evidence for Dynamically Organized Modularity in the Yeast Protein-Protein Interaction Network Nature 2004, 430, 88– 93 DOI: 10.1038/nature02555
Google Scholar
9
Evidence for dynamically organized modularity in the yeast protein-protein interaction network
Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc
Nature (London, United Kingdom) (2004), 430 (6995), 88-93CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
In apparently scale-free protein-protein interaction networks, or 'interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the 'hubs', interact with many partners. Both biol. and non-biol. scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topol. of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approx. threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: 'party' hubs, which interact with most of their partners simultaneously, and date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biol. processes-or modules -to each other, whereas party hubs function inside modules.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXlt1Cqt7s%253D&md5=5df12bb50c608fe5023f9b0fb4efd503
10
Fliri, A. F.; Loging, W. T.; Volkmann, R. A. Cause-Effect Relationships in Medicine: A Protein Network Perspective Trends Pharmacol. Sci. 2010, 31, 547– 555 DOI: 10.1016/j.tips.2010.07.005
Google Scholar
There is no corresponding record for this reference.
11
Ma, X.; Gao, L. Biological Network Analysis: Insights into Structure and Functions Briefings Funct. Genomics 2012, 11, 434– 442 DOI: 10.1093/bfgp/els045
Google Scholar
11
Biological network analysis: insights into structure and functions
Ma Xiaoke; Gao Lin
Briefings in functional genomics (2012), 11 (6), 434-42 ISSN:.
In the past two decades, great efforts have been devoted to extract the dependence and interplay between structure and functions in biological networks because they have strong relevance to biological processes. In this article, we reviewed the recent development in the biological network analysis. In detail, we first reviewed the interactome topological properties of biological networks, the methods for structure and functional patterns.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s7ms1arsA%253D%253D&md5=5cbf86d6186022802aa1a99745624178
12
Zhou, T.-T. Network Systems Biology for Targeted Cancer Therapies Aizheng 2012, 31, 134 DOI: 10.5732/cjc.011.10282
Google Scholar
12
Network systems biology for targeted cancer therapies
Zhou, Tingting
Chinese Journal of Cancer (2012), 31 (3), 134-141CODEN: CJCHDJ ISSN:. (Sun Yat-sen University Cancer Center)
A review. The era of targeted cancer therapies has arrived. However, due to the complexity of biol. systems, the current progress is far from enough. From biol. network modeling to structural/dynamic network anal., network systems biol. provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells. It has also introduced great changes into the research paradigm of cancer-assocd. drug discovery and drug resistance.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XmtF2rsLc%253D&md5=944c914b377de2c0ce3a5f435a0f1cca
13
Sukumar, N.; Krein, M. P. Graphs and Networks in Chemical and Biological Informatics: Past, Present and Future Future Med. Chem. 2012, 4, 2039– 2047 DOI: 10.4155/fmc.12.128
Google Scholar
13
Graphs and networks in chemical and biological informatics: past, present and future
Sukumar, N.; Krein, Michael P.
Future Medicinal Chemistry (2012), 4 (16), 2039-2047CODEN: FMCUA7; ISSN:1756-8919. (Future Science Ltd.)
A review. Chem. and biol. network anal. has recently garnered intense interest from the perspective of drug design and discovery. While graph theoretic concepts have a long history in chem. - predating quantum mechanics - and graphical measures of chem. structures date back to the 1970s, it is only recently with the advent of public repositories of information and availability of high-throughput assays and computational resources that network anal. of large-scale chem. networks, such as protein-protein interaction networks, has become possible. Drug design and discovery are undergoing a paradigm shift, from the notion of one target, one drug' to a much more nuanced view that relies on multiple sources of information: genomic, proteomic, metabolomic and so on. This holistic view of drug design is an incredibly daunting undertaking still very much in its infancy. Here, we focus on current developments in graph- and network-centric approaches in chem. and biol. informatics, with particular ref. to applications in the fields of SAR modeling and drug design. Key insights from the past suggest a path forward via visualization and fusion of multiple sources of chem. network data.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xhs1ynsbnJ&md5=08f9925e749f8f9adb94188c25055619
14
Yim, K.; Cunningham, D. Targeted Drug Therapies and Cancer Recent Results Cancer Res. 2011, 185, 159– 171 DOI: 10.1007/978-3-642-03503-6_10
Google Scholar
14
Targeted drug therapies and cancer
Yim, K. L.; Cunningham, D.
Recent Results in Cancer Research (2011), 185 (Inflammation and Gastrointestinal Cancers), 159-171CODEN: RRCRBU; ISSN:0080-0015. (Springer GmbH)
A review. With the progress of research in mol. biol. and greater understanding of cell signalling systems emerge an increasing array of potential targets for the therapy of cancer. While traditional chemotherapy aims to elicit tumor cell death, it also produces undesirable side effects on physiol. proliferating cells. By isolating cell surface receptors which link specific intracellular secondary messenger pathways, researchers are increasingly able to define the biol. network which drives cellular function. Of importance are routes involved in malignant transformation, proliferation, survival and angiogenesis. Thus targeted therapy is directed to specific differential growth processes particular to malignant tumors. The principle mode of action generally involves the "lock-and-key" mechanism and identifying the "Achilles' heel" for drug action. Various targeted agents have been studied and many have translated into significant clin. benefit. This chapter will describe some examples which illustrate the role of this approach in gastrointestinal cancers.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xks1ejtbk%253D&md5=e39ff226e824264349bc0882c5ae26ea
15
Lee, S.; Park, K.; Kim, D. Building a Drug-Target Network and its Applications Expert Opin. Drug Discovery 2009, 4, 1177– 1189 DOI: 10.1517/17460440903322234
Google Scholar
There is no corresponding record for this reference.
16
Riccione, K. A.; Smith, R. P.; Lee, A. J.; You, L. A Synthetic Biology Approach to Understanding Cellular Information Processing ACS Synth. Biol. 2012, 1, 389– 402 DOI: 10.1021/sb300044r
Google Scholar
16
A Synthetic Biology Approach to Understanding Cellular Information Processing
Riccione, Katherine A.; Smith, Robert P.; Lee, Anna J.; You, Lingchong
ACS Synthetic Biology (2012), 1 (9), 389-402CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
A review. The survival of cells and organisms requires proper responses to environmental signals. These responses are governed by cellular networks, which serve to process diverse environmental cues. Biol. networks often contain recurring network topologies called 'motifs'. It has been recognized that the study of such motifs allows one to predict the response of a biol. network and thus cellular behavior. However, studying a single motif in complete isolation of all other network motifs in a natural setting is difficult. Synthetic biol. has emerged as a powerful approach to understanding the dynamic properties of network motifs. In addn. to testing existing theor. predictions, construction and anal. of synthetic gene circuits has led to the discovery of novel motif dynamics, such as how the combination of simple motifs can lead to autonomous dynamics or how noise in transcription and translation can affect the dynamics of a motif. Here, we review developments in synthetic biol. as they pertain to increasing our understanding of cellular information processing. We highlight several types of dynamic behaviors that diverse motifs can generate, including the control of input/output responses, the generation of autonomous spatial and temporal dynamics, as well as the influence of noise in motif dynamics and cellular behavior.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XnsFOmtbs%253D&md5=9f33791e17b6f435fbecba5e8959c7a3
17
Das, A.; Gur, M.; Cheng, M. H.; Jo, S.; Bahar, I.; Roux, B. Exploring the Conformational Transitions of Biomolecular Systems using a Simple Two-State Anisotropic Network Model PLoS Comput. Biol. 2014, 10, e1003521 DOI: 10.1371/journal.pcbi.1003521
Google Scholar
17
Exploring the conformational transitions of biomolecular systems using a simple two-state anisotropic network model
Das, Avisek; Gur, Mert; Cheng, Mary Hongying; Jo, Sunhwan; Bahar, Ivet; Roux, Benoit
PLoS Computational Biology (2014), 10 (4), e1003521/1-e1003521/17, 17 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
Biomol. conformational transitions are essential to biol. functions. Most exptl. methods report on the long-lived functional states of biomols., but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect exptl. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed exptl. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a phys. reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the exptl. structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the min. energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biol. interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom mol. dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides exptl. testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVGlsrfP&md5=28bb6ca702c66f6910e2472fcf2c6577
18
Chennubhotla, C.; Bahar, I. Signal Propagation in Proteins and Relation to Equilibrium Fluctuations PLoS Comput. Biol. 2007, 3, 1716– 1726 DOI: 10.1371/journal.pcbi.0030172
Google Scholar
18
Signal propagation in proteins and relation to equilibrium fluctuations
Chennubhotla, Chakra; Bahar, Ivet
PLoS Computational Biology (2007), 3 (9), 1716-1726CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
Elastic network (EN) models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biol. function. We posit that equil. motions also det. communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the no. of steps it takes on an av. to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topol. basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have phys. origins directly relevant to the equil. fluctuations of residues predicted by EN models.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtFKmtrvP&md5=567ec83c398eb428a88f9b1db83b382e
19
Maragakis, P.; Karplus, M. Large Amplitude Conformational Change in Proteins Explored with a Plastic Network Model: Adenylate Kinase J. Mol. Biol. 2005, 352, 807– 822 DOI: 10.1016/j.jmb.2005.07.031
Google Scholar
19
Large Amplitude Conformational Change in Proteins Explored with a Plastic Network Model: Adenylate Kinase
Maragakis, Paul; Karplus, Martin
Journal of Molecular Biology (2005), 352 (4), 807-822CODEN: JMOBAK; ISSN:0022-2836. (Elsevier B.V.)
The plastic network model (PNM) is used to generate a conformational change pathway for Escherichia coli adenylate kinase based on two crystal structures, namely that of an open and a closed conformer. In this model, the energy basins corresponding to known conformers are connected at their lowest common energies. The results are used to evaluate and analyze the minimal energy pathways between these basins. The open to closed transition anal. provides an identification of hinges that is in agreement with the existing definitions based on the available X-ray structures. The elastic energy distribution and the Cα pseudo-dihedral variation provide similar information on these hinges. The ensemble of the 45 published structures for this protein and closely related proteins is shown to always be within 3.0 Å of the pathway, which corresponds to a conformational change between two end structures that differ by a Cα-atom root-mean-squared deviation of 7.1 Å.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhtVWit7jL&md5=377c8baebdf4a43fb7cdf25d403b6d3b
20
Krivov, S. V.; Karplus, M. Hidden Complexity of Free Energy Surfaces for Peptide (Protein) Folding Proc. Natl. Acad. Sci. U. S. A. 2004, 101, 14766– 14770 DOI: 10.1073/pnas.0406234101
Google Scholar
20
Hidden complexity of free energy surfaces for peptide (protein) folding
Krivov, Sergei V.; Karplus, Martin
Proceedings of the National Academy of Sciences of the United States of America (2004), 101 (41), 14766-14770CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
An understanding of the thermodn. and kinetics of protein folding requires a knowledge of the free energy surface governing the motion of the polypeptide chain. Because of the many degrees of freedom involved, surfaces projected on only 1 or 2 progress variables are generally used in descriptions of the folding reaction. Such projections result in relatively smooth surfaces, but they could mask the complexity of the unprojected surface. Here, the authors introduce an approach to det. the actual (unprojected) free energy surface and apply it to a 16-residue peptide, the 2nd β-hairpin of streptococcal protein G, which has been used as a model system for protein folding. The surface was represented by a disconnectivity graph calcd. from a long equil. folding-unfolding trajectory. The denatured state was found to have multiple low free energy basins. Nevertheless, the peptide showed exponential kinetics in folding to the native basin. Projected surfaces obtained from the present anal. had a simple form in agreement with other studies of the β-hairpin. The hidden complexity found for the β-hairpin surface suggested that the std. funnel picture of protein folding should be revisited.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXovVyjurY%253D&md5=f41f0ebbae3b7e82a6ddba894df4e3f6
21
Yin, Y.; Maisuradze, G.; Liwo, A.; Scheraga, H. Hidden Protein Folding Pathways in Free-Energy Landscapes Uncovered by Network Analysis J. Chem. Theory Comput. 2012, 8, 1176– 1189 DOI: 10.1021/ct200806n
Google Scholar
21
Hidden protein folding pathways in free-energy landscapes uncovered by network analysis
Yin, Yanping; Maisuradze, Gia G.; Liwo, Adam; Scheraga, Harold A.
Journal of Chemical Theory and Computation (2012), 8 (4), 1176-1189CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
Network anal. is used to uncover hidden folding pathways in free energy landscapes usually defined in terms of such arbitrary order parameters as root-mean-square deviation from the native structure, radius of gyration, etc. The anal. was applied to mol. dynamics trajectories of the B-domain of staphylococcal protein A, generated with the coarse-grained united-residue force field in a broad range of temps. (270 K ≤ T ≤ 325 K). Thousands of folding pathways were identified at each temp. Out of these many folding pathways, several most probable ones were selected for investigation of the conformational transitions during protein folding. Unlike other conformational space network (CSN) methods, a node in the CSN variant implemented in this work was defined according to the native-likeness class of the structure, which defined the similarity of segments of the compared structures in terms of secondary structure, contact pattern, and local geometry as well as the overall geometric similarity of the conformation under consideration to that of the ref. (exptl.) structure. The authors' previous findings, regarding the folding model and conformations found at the folding-transition temp. for protein A, were confirmed by the conformational space network anal. In the methodol. and the anal. of the results, the shortest path identified by using the shortest-path algorithm corresponded to the most probable folding pathway in the conformational space network.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XjtVCrtrw%253D&md5=1d7189ca035a5582ce13db6b9afd9fc5
22
Golas, E.; Czaplewski, C.; Scheraga, H.; Liwo, A. Common functionally Important Motions of the Nucleotide-binding Domain of Hsp70 Proteins: Struct., Funct., Genet. 2015, 83, 282– 299 DOI: 10.1002/prot.24731
Google Scholar
There is no corresponding record for this reference.
23
Porras, P.; Duesbury, M.; Fabregat, A.; Ueffing, M.; Orchard, S.; Gloeckner, C. J.; Hermjakob, H. A Visual Review of the Interactome of LRRK2: Using Deep-Curated Molecular Interaction Data to Represent Biology Proteomics 2015, 15, 1390– 1404 DOI: 10.1002/pmic.201400390
Google Scholar
There is no corresponding record for this reference.
24
Kohonen, T. Self-Organized Formation of Topologically Correct Feature Maps Biol. Cybern. 1982, 43, 59– 69 DOI: 10.1007/BF00337288
Google Scholar
There is no corresponding record for this reference.
25
Blondel, V. D.; Guillaume, J.-L.; Lambiotte, R.; Lefebvre, E. Fast Unfolding of Communities in Large Networks J. Stat. Mech.: Theory Exp. 2008, 2008, P10008 DOI: 10.1088/1742-5468/2008/10/P10008
Google Scholar
There is no corresponding record for this reference.
26
Sethi, A.; Eargle, J.; Black, A. A.; Luthey-Schulten, Z. Dynamical Networks in tRNA: Protein Complexes Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 6620– 6625 DOI: 10.1073/pnas.0810961106
Google Scholar
26
Dynamical networks in tRNA:protein complexes
Sethi, Anurag; Eargle, John; Black, Alexis A.; Luthey-Schulten, Zaida
Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (16), 6620-6625CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Community network anal. derived from mol. dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNAGlu and an archaeal leucyl-tRNA synthetase (LeuRS):tRNALeu complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the phys. network whose overall topol. is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller no. of crit. edges or interactions. Consistent with this anal., there are a large no. of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXlsVOlu7o%253D&md5=5e3cfd80bf85ffe46c9c84960d176713
27
Fuglestad, B.; Gasper, P. M.; McCammon, J. A.; Markwick, P. R.; Komives, E. A. Correlated Motions and Residual Frustration in Thrombin J. Phys. Chem. B 2013, 117, 12857– 12863 DOI: 10.1021/jp402107u
Google Scholar
27
Correlated Motions and Residual Frustration in Thrombin
Fuglestad, Brian; Gasper, Paul M.; McCammon, J. Andrew; Markwick, Phineus R. L.; Komives, Elizabeth A.
Journal of Physical Chemistry B (2013), 117 (42), 12857-12863CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
Thrombin is the central protease in the cascade of blood coagulation proteases. The structure of thrombin consists of a double β-barrel core surrounded by connecting loops and helixes. Compared to chymotrypsin, thrombin has more extended loops that are thought to have arisen from insertions in the serine protease that evolved to impart greater specificity. Previous expts. showed thermodn. coupling between ligand binding at the active site and distal exosites. We present a combined approach of mol. dynamics (MD), accelerated mol. dynamics (AMD), and anal. of the residual local frustration of apo-thrombin and active-site-bound (PPACK-thrombin). Community anal. of the MD ensembles identified changes upon active site occupation in groups of residues linked through correlated motions and phys. contacts. AMD simulations, calibrated on measured residual dipolar couplings, reveal that upon active site ligation, correlated loop motions are quenched, but new ones connecting the active site with distal sites where allosteric regulators bind emerge. Residual local frustration anal. reveals a striking correlation between frustrated contacts and regions undergoing slow time scale dynamics. The results elucidate a motional network that probably evolved through retention of frustrated contacts to provide facile conversion between ensembles of states.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXms1Orur0%253D&md5=3ac270d27e438f468ead190ebfdf058b
28
Breiman, L. Random Forests Mach. Learn. 2001, 45, 5– 32 DOI: 10.1023/A:1010933404324
Google Scholar
There is no corresponding record for this reference.
29
Maragliano, L.; Vanden-Eijnden, E. A Temperature Accelerated Method for sampling Free Energy and determining Reaction Pathways in Rare Events Simulations Chem. Phys. Lett. 2006, 426, 168– 175 DOI: 10.1016/j.cplett.2006.05.062
Google Scholar
29
A temperature accelerated method for sampling free energy and determining reaction pathways in rare events simulations
Maragliano, Luca; Vanden-Eijnden, Eric
Chemical Physics Letters (2006), 426 (1-3), 168-175CODEN: CHPLBC; ISSN:0009-2614. (Elsevier B.V.)
A method for sampling efficiently the free energy landscape of a complex system with respect to some given collective variables is proposed. Inspired by metadynamics [A. Laio, M. Parrinello, Proc. Nat. Acad. Sci. USA 99 (2002) 12562], we introduce an extended system where the collective variables are treated as dynamical ones and show that this allows to sample the free energy landscape of these variables directly. The sampling is accelerated by using an artificially high temp. for the collective variables. The validity of the method is established using general results for systems with multiple time-scales, and its numerical efficiency is also discussed via error anal. We also show how the method can be modified in order to sample the reactive pathways in free energy space, and thereby analyze the mechanism of a reaction. Finally, we discuss how the method can be generalized and used as an alternative to the Kirkwood generalized thermodn. integration approach for the calcn. of free energy differences.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XmslGns7c%253D&md5=791e4865671fb36662b57f2342f8d95e
30
Maragliano, L.; Cottone, G.; Ciccotti, G.; Vanden-Eijnden, E. Mapping the Network of Pathways of CO Diffusion in Myoglobin J. Am. Chem. Soc. 2010, 132, 1010– 1017 DOI: 10.1021/ja905671x
Google Scholar
30
Mapping the Network of Pathways of CO Diffusion in Myoglobin
Maragliano, Luca; Cottone, Grazia; Ciccotti, Giovanni; Vanden-Eijnden, Eric
Journal of the American Chemical Society (2010), 132 (3), 1010-1017CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
The pathways of diffusion of a CO mol. inside a myoglobin protein and toward the solvent are investigated. Specifically, the three-dimensional potential of mean force (PMF or free energy) of the CO mol. position inside the protein is calcd. by using the single-sweep method in concert with fully resolved atomistic simulations in explicit solvent. The results are interpreted under the assumption that the diffusion of the ligand can be modeled as a navigation on the PMF in which the ligand hops between the PMF local min. following the min. free energy paths (MFEPs) with rates set by the free energy barriers that need to be crossed. Here, all the local min. of the PMF, the MFEPs, and the barriers along them are calcd. The positions of the local min. are in good agreement with all the known binding cavities inside the protein, which indicates that these cavities may indeed serve as dynamical traps inside the protein and thereby influence the binding process. In addn., the MFEPs connecting the local PMF min. show a complicated network of possible pathways of exit of the dissocd. CO starting from the primary docking site, in which the histidine gate is the closest exit from the binding site for the ligand but it is not the only possible one.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhs1alur%252FE&md5=30bc6f41e344a03db97a23a87267d6c7
31
Abrams, C.; Vanden-Eijnden, E. Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 4961– 4966 DOI: 10.1073/pnas.0914540107
Google Scholar
31
Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics
Abrams, Cameron F.; Vanden-Eijnden, Eric
Proceedings of the National Academy of Sciences of the United States of America (2010), 107 (11), 4961-4966, S4961/1-S4961/8CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
We show how to apply the method of temp.-accelerated mol. dynamics (TAMD) in collective variables to sample the conformational space of multidomain proteins in all-atom, explicitly solvated mol. dynamics simulations. The method allows the system to hyperthermally explore the free-energy surface in a set of collective variables computed at the phys. temp. As collective variables, we pick Cartesian coordinates of centers of contiguous subdomains. The method is applied to the GroEL subunit, a 55-kDa, three-domain protein, and HIV-1 gp120. For GroEL, the method induces in about 40 ns conformational changes that recapitulate the t → r'' transition and are not obsd. in unaccelerated mol. dynamics: The apical domain is displaced by 30 Å, with a twist of 90° relative to the equatorial domain, and the root-mean-squared deviation relative to the r'' conformer is reduced from 13 to 5 Å, representing fairly high predictive capability. For gp120, the method predicts both counter-rotation of inner and outer domains and disruption of the so-called bridging sheet. In particular, TAMD on gp120 initially in the CD4-bound conformation visits conformations that deviate by 3.6 Å from the gp120 conformer in complex with antibody F105, again reflecting good predictive capability. TAMD generates plausible all-atom models of the so-far structurally uncharacterized unliganded conformation of HIV-1 gp120, which may prove useful in the development of inhibitors and immunogens. The fictitious temp. employed also gives a rough est. of 10 kcal/mol for the free-energy barrier between conformers in both cases.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjvFGntrk%253D&md5=3217f5804cac528e40747cf6d236529c
32
Vashisth, H.; Maragliano, L.; Abrams, C. ”DFG-flip” in the Insulin Receptor Kinase is facilitated by a Helical Intermediate State of the Activation Loop Biophys. J. 2012, 102, 1979– 1987 DOI: 10.1016/j.bpj.2012.03.031
Google Scholar
32
"DFG-Flip" in the Insulin Receptor Kinase Is Facilitated by a Helical Intermediate State of the Activation Loop
Vashisth, Harish; Maragliano, Luca; Abrams, Cameron F.
Biophysical Journal (2012), 102 (8), 1979-1987CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
We have characterized a large-scale inactive-to-active conformational change in the activation-loop of the insulin receptor kinase domain at the atomistic level via untargeted temp.-accelerated mol. dynamics (TAMD) and free-energy calcns. using the string method. TAMD simulations consistently show folding of the A-loop into a helical conformation followed by unfolding to an active conformation, causing the highly conserved DFG-motif (Asp1150, Phe1151, and Gly1152) to switch from the inactive "D-out/F-in" to the nucleotide-binding-competent "D-in/F-out" conformation. The min. free-energy path computed from the string method preserves these helical intermediates along the inactive-to-active path, and the thermodn. free-energy differences are consistent with previous work on various other kinases. The mechanisms revealed by TAMD also suggest that the regulatory spine can be dynamically assembled/disassembled either by DFG-flip or by movement of the αC-helix. Together, these findings both broaden our understanding of kinase activation and point to intermediates as specific therapeutic targets.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XmtVeku7g%253D&md5=153899028b209aeec2a05274081e557e
33
Vashisth, H.; Brooks, C. Conformational Sampling of Maltose-transporter Components in Cartesian Collective Variables is governed by the Low-frequency Normal Modes J. Phys. Chem. Lett. 2012, 3, 3379– 3384 DOI: 10.1021/jz301650q
Google Scholar
33
Conformational Sampling of Maltose-Transporter Components in Cartesian Collective Variables Is Governed by the Low-Frequency Normal Modes
Vashisth, H.; Brooks, C. L., III
Journal of Physical Chemistry Letters (2012), 3 (22), 3379-3384CODEN: JPCLCD; ISSN:1948-7185. (American Chemical Society)
We have studied large-scale conformational transitions in the maltose-binding protein and the nucleotide binding domains of a maltose-transporter using enhanced conformational sampling in Cartesian collective variables with temp.-accelerated mol. dynamics (TAMD) and Cα-based elastic network normal-mode anal. Significantly, every functional displacement in the TAMD-generated pathways of each protein could be rationalized via a single low-frequency soft mode, whereas a combination of two to three low-frequency modes was found to describe the entire conformational change, suggesting that collective functional movement in TAMD trajectories is facilitated by the intrinsically accessible low-frequency normal modes. By applying a harmonic potential to facilitate functional motion in TAMD simulations, we also provide a recipe to reproducibly generate structural transitions in both proteins, which can be used to characterize large-scale conformational changes in other biomols.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsF2ktb7P&md5=79e81d0f24f7eadea7dfcb1e7dd45c4f
34
Nygaard, R.; Zou, Y.; Dror, R.; Mildorf, T.; Arlow, D.; Manglik, A.; Pan, A.; Liu, C.; Fung, J.; Bokoch, M.; Thian, F.; Kobilka, T.; Shaw, D.; Mueller, L.; Prosser, R.; Kobilka, B. The Dynamic Process of β(2)-adrenergic Receptor Activation Cell 2013, 152, 532– 542 DOI: 10.1016/j.cell.2013.01.008
Google Scholar
34
The Dynamic Process of β2-Adrenergic Receptor Activation
Nygaard, Rie; Zou, Yaozhong; Dror, Ron O.; Mildorf, Thomas J.; Arlow, Daniel H.; Manglik, Aashish; Pan, Albert C.; Liu, Corey W.; Fung, Juan Jose; Bokoch, Michael P.; Thian, Foon Sun; Kobilka, Tong Sun; Shaw, David E.; Mueller, Luciano; Prosser, R. Scott; Kobilka, Brian K.
Cell (Cambridge, MA, United States) (2013), 152 (3), 532-542CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
G-protein-coupled receptors (GPCRs) can modulate diverse signaling pathways, often in a ligand-specific manner. The full range of functionally relevant GPCR conformations is poorly understood. Here, the authors use NMR spectroscopy to characterize the conformational dynamics of the transmembrane core of the β2-adrenergic receptor (β2AR), a prototypical GPCR. The authors labeled β2AR with 13CH3ε-methionine and obtained HSQC spectra of unliganded receptor as well as receptor bound to an inverse agonist, an agonist, and a G-protein-mimetic nanobody. These studies provide evidence for conformational states not obsd. in crystal structures, as well as substantial conformational heterogeneity in agonist- and inverse-agonist-bound prepns. They also show that for β2AR, unlike rhodopsin, an agonist alone does not stabilize a fully active conformation, suggesting that the conformational link between the agonist-binding pocket and the G-protein-coupling surface is not rigid. The obsd. heterogeneity may be important for β2AR's ability to engage multiple signaling and regulatory proteins.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvFaiu7s%253D&md5=8f9aaa581027657b166b92ef617e950d
35
Lapelosa, M.; Abrams, C. A Computational Study of Water and CO Migration Sites and Channels Inside Myoglobin J. Chem. Theory Comput. 2013, 9, 1265– 1271 DOI: 10.1021/ct300862j
Google Scholar
35
A Computational Study of Water and CO Migration Sites and Channels Inside Myoglobin
Lapelosa, Mauro; Abrams, Cameron F.
Journal of Chemical Theory and Computation (2013), 9 (2), 1265-1271CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
Pathways are computed for transport of H2O and CO in myoglobin (Mb), using the single sweep and zero-temp. string methods in a fully atomistic, explicitly solvated model system. Our predictions of sites and barriers in the pathways for CO transport agree with previous studies. For H2O, we predict a binding site in the distal pocket (DP), in agreement with crystallog. observations, and another one close to Leu 29, which explains the importance of this residue in controlling the pocket's hydrophobicity, as well as disordered min. in the largely apolar xenon cavities. In particular, H2O can occupy and transition among the xenon cavities, Xe4, Xe2, and Xe3. Our results support the hypothesis that the thermodynamically most favorable entry/exit portal for H2O is the so-called histidine gate (HG), the same as for CO. This result, along with the observation of water occupation of both DP and apolar Xe cavities, suggest that water and small gas mols. like CO compete for access to the protein interior, and therefore models of gas mol. transport within proteins should also explicitly consider water transport.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFWlu74%253D&md5=329f0660b726da6505fda270a01c8883
36
Vashisth, H.; Abrams, C. All-atom Structural Models of Insulin Binding to the Insulin Receptor in the presence of a Tandem Hormone-binding Element Proteins: Struct., Funct., Genet. 2013, 81, 1017– 1030 DOI: 10.1002/prot.24255
Google Scholar
36
All-atom structural models of insulin binding to the insulin receptor in the presence of a tandem hormone-binding element
Vashisth, Harish; Abrams, Cameron F.
Proteins: Structure, Function, and Bioinformatics (2013), 81 (6), 1017-1030CODEN: PSFBAF ISSN:. (Wiley-Blackwell)
Insulin regulates blood glucose levels in higher organisms by binding to and activating insulin receptor (IR), a constitutively homodimeric glycoprotein of the receptor tyrosine kinase (RTK) superfamily. Therapeutic efforts in treating diabetes have been significantly impeded by the absence of structural information on the activated form of the insulin/IR complex. Mutagenesis and photo-crosslinking expts. and structural information on insulin and apo-IR strongly suggest that the dual-chain insulin mol., unlike the related single-chain insulin-like growth factors, binds to IR in a very different conformation than what is displayed in storage forms of the hormone. In particular, hydrophobic residues buried in the core of the folded insulin mol. engage the receptor. There is also the possibility of plasticity in the receptor structure based on these data, which may in part be due to rearrangement of the so-called CT-peptide, a tandem hormone-binding element of IR. These possibilities provide opportunity for large-scale mol. modeling to contribute to our understanding of this system. Using various atomistic simulation approaches, we have constructed all-atom structural models of hormone/receptor complexes in the presence of CT in its crystallog. position and a thermodynamically favorable displaced position. In the "displaced-CT" complex, many more insulin-receptor contacts suggested by expts. are satisfied, and our simulations also suggest that R-insulin potentially represents the receptor-bound form of hormone. The results presented in this work have further implications for the design of receptor-specific agonists/antagonists.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjtFWgtbY%253D&md5=b580372c187c210ba1dc24050fbeaf86
37
Scarpazza, D.; Ierardi, D.; Lerer, A.; Mackenzie, K.; Pan, A.; Bank, J. A.; Chow, E.; Dror, R.; Grossman, J.; Killebrew, D.; Moraes, M.; Predescu, C.; Salmon, J.; Shaw, D. Extending the Generality of Molecular Dynamics Simulations on a Special-Purpose Machine. In Proceedings of the 27th IEEE International Parallel and Distributed Processing Symposium, 2013; pp 933– 945 DOI: 10.1109/IPDPS.2013.93 .
Google Scholar
There is no corresponding record for this reference.
38
Vashisth, H.; Storaska, A.; Neubig, R.; Brooks, C. Conformational Dynamics of a Regulator of G-protein Signaling Protein reveals a Mechanism of Allosteric Inhibition by a small Molecule ACS Chem. Biol. 2013, 8, 2778– 2784 DOI: 10.1021/cb400568g
Google Scholar
There is no corresponding record for this reference.
39
Selwa, E.; Huynh, T.; Ciccotti, G.; Maragliano, L.; Malliavin, T. Temperature-accelerated Molecular Dynamics gives Insights into Globular Conformations Sampled in the Free State of the AC Catalytic Domain Proteins: Struct., Funct., Genet. 2014, 82, 2483– 2496 DOI: 10.1002/prot.24612
Google Scholar
There is no corresponding record for this reference.
40
Hosseini-Naveh, Z. M.; Malliavin, T.; Maragliano, L.; Cottone, G.; Ciccotti, G. Conformational changes in Acetylcholine binding protein Investigated by Temperature accelerated Molecular Dynamics PLoS One 2014, 9, e88555 DOI: 10.1371/journal.pone.0088555
Google Scholar
There is no corresponding record for this reference.
41
Cortes-Ciriano, I.; Bouvier, G.; Nilges, M.; Maragliano, L.; Malliavin, T. Temperature Accelerated Molecular Dynamics with Soft-ratcheting Criterion orients Enhanced Sampling by Low-resolution Information J. Chem. Theory Comput. 2015, 11, 3446– 3454 DOI: 10.1021/acs.jctc.5b00153
Google Scholar
41
Temperature Accelerated Molecular Dynamics with Soft-Ratcheting Criterion Orients Enhanced Sampling by Low-Resolution Information
Cortes-Ciriano, Isidro; Bouvier, Guillaume; Nilges, Michael; Maragliano, Luca; Malliavin, Therese E.
Journal of Chemical Theory and Computation (2015), 11 (7), 3446-3454CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
Many proteins exhibit an equil. between multiple conformations, some of them being characterized only by low-resoln. information. Visiting all conformations is a demanding task for computational techniques performing enhanced but unfocused exploration of collective variable (CV) space. Otherwise, pulling a structure toward a target condition biases the exploration in a way difficult to assess. To address this problem, we introduce here the soft-ratcheting temp.-accelerated mol. dynamics (sr-TAMD), where the exploration of CV space by TAMD is coupled to a soft-ratcheting algorithm that filters the evolving CV values according to a predefined criterion. Any low resoln. or even qual. information can be used to orient the exploration. We validate this technique by exploring the conformational space of the inactive state of the catalytic domain of the adenyl cyclase AC from Bordetella pertussis. The domain AC gets activated by assocn. with calmodulin (CaM), and the available crystal structure shows that in the complex the protein has an elongated shape. High-resoln. data are not available for the inactive, CaM-free protein state, but hydrodynamic measurements have shown that the inactive AC displays a more globular conformation. Here, using as CVs several geometric centers, we use sr-TAMD to enhance CV space sampling while filtering for CV values that correspond to centers moving close to each other, and we thus rapidly visit regions of conformational space that correspond to globular structures. The set of conformations sampled using sr-TAMD provides the most extensive description of the inactive state of AC up to now, consistent with available exptl. information.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXoslyltro%253D&md5=80c851ca909c95b4e7a7c2342d6185df
42
Arthur, M.; Reynolds, P.; Courvalin, P. Glycopeptide Resistance in Enterococci Trends Microbiol. 1996, 4, 401– 407 DOI: 10.1016/0966-842X(96)10063-9
Google Scholar
42
Glycopeptide resistance in enterococci
Arthur M; Reynolds P; Courvalin P
Trends in microbiology (1996), 4 (10), 401-7 ISSN:0966-842X.
Glycopeptide resistance in enterococci results from the production of peptidoglycan precursors with low affinity for these antibiotics. The mobility of the resistance genes by transposition and conjugation and the ability of the resistance proteins to interfere with synthesis of normal precursors in different hosts indicate that dissemination into other bacterial species should be anticipated.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaK2s%252FlsFKkuw%253D%253D&md5=890e80ff1e64c3cd6af936366cccf900
43
Courvalin, P. Vancomycin Resistance in Gram-Positive Cocci Clin. Infect. Dis. 2006, 42, 25– 34 DOI: 10.1086/491711
Google Scholar
43
Vancomycin resistance in Gram-positive cocci
Courvalin, Patrice
Clinical Infectious Diseases (2006), 42 (Suppl. 1), S25-S34CODEN: CIDIEL; ISSN:1058-4838. (University of Chicago Press)
A review. The first vancomycin-resistant clin. isolates of Enterococcus species were reported in Europe in 1988. Similar strains were later detected in hospitals on the East Coast of the United States. Since then, vancomycin-resistant enterococci have spread with unexpected rapidity and are now encountered in hospitals in most countries. This article reviews the mode of action and the mechanism of bacterial resistance to glycopeptides, as exemplified by the VanA type, which is mediated by transposon Tn1546 and is widely spread in enterococci. The diversity, regulation, evolution, and recent dissemination of methicillin-resistant Staphylococcus aureus are then discussed.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XmsVaguw%253D%253D&md5=4f74a113a99ebfc1bb96accefc299159
44
Arthur, M.; Molinas, C.; Bugg, T.; Wright, G.; Walsh, C.; Courvalin, P. Evidence for in vivo Incorporation of d-lactate into Peptidoglycan Precursors of Vancomycin-Resistant Enterococci Antimicrob. Agents Chemother. 1992, 36, 867– 869 DOI: 10.1128/AAC.36.4.867
Google Scholar
44
Evidence for in vivo incorporation of D-lactate into peptidoglycan precursors of vancomycin-resistant enterococci
Arthur, Michel; Molinas, Catherine; Bugg, Timothy D. H.; Wright, Gerard D.; Walsh, Christopher T.; Courvalin, Patrice
Antimicrobial Agents and Chemotherapy (1992), 36 (4), 867-9CODEN: AMACCQ; ISSN:0066-4804.
The VanA ligase encoded by the vancomycin resistance plasmid pIP816 of Enterococcus faecium BM4147 condenses D-alanine with various D-2-hydroxy and D-2-amino acids in vitro. D-Lactate added to the culture medium restored the vancomycin resistance of a strain that does not produce the VanH dehydrogenase and therefore appears to be a substrate of VanA in vivo.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38XitFWjtrk%253D&md5=5c71e639085a7265af95f0eccc42fdc0
45
Roper, D.; Huyton, T.; Vagin, A.; Dodson, G. The Molecular Basis of Vancomycin Resistance in clinically relevant Enterococci: Crystal Structure of d-alanyl–d-lactate Ligase (VanA) Proc. Natl. Acad. Sci. U. S. A. 2000, 97, 8921– 8925 DOI: 10.1073/pnas.150116497
Google Scholar
There is no corresponding record for this reference.
46
Bouvier, G.; Duclert-Savatier, N.; Desdouits, N.; Meziane-Cherif, D.; Blondel, A.; Courvalin, P.; Nilges, M.; Malliavin, T. E. Functional Motions Modulating VanA Ligand Binding unraveled by Self-organizing maps J. Chem. Inf. Model. 2014, 54, 289– 301 DOI: 10.1021/ci400354b
Google Scholar
46
Functional Motions Modulating VanA Ligand Binding Unraveled by Self-Organizing Maps
Bouvier, Guillaume; Duclert-Savatier, Nathalie; Desdouits, Nathan; Meziane-Cherif, Djalal; Blondel, Arnaud; Courvalin, Patrice; Nilges, Michael; Malliavin, Therese E.
Journal of Chemical Information and Modeling (2014), 54 (1), 289-301CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
The VanA D-Ala:D-Lac ligase is a key enzyme in the emergence of high level resistance to vancomycin in Enterococcus species and methicillin-resistant Staphylococcus aureus (MRSA). It catalyzes the formation of D-Ala-D-Lac instead of the vancomycin target, D-Ala-D-Ala, leading to the prodn. of modified, low vancomycin binding affinity peptidoglycan precursors. Therefore, VanA appears to be an attractive target for the design of new antibacterials to overcome resistance. The catalytic site of VanA is delimited by three domains and closed by an ω-loop upon enzymic reaction. The aim of the present work was (i) to investigate the conformational transition of VanA assocd. with the opening of its ω-loop and of a part of its central domain, and (ii) to relate this transition with the substrate or product binding propensities. Mol. dynamics trajectories of the VanA ligase of Enterococcus faecium with or without a disulfide bridge distant from the catalytic site revealed differences in the catalytic site conformations with a slight opening. Conformations were clustered with an original machine learning method, based on self-organizing maps (SOM), which revealed four distinct conformational basins. Several ligands related to substrates, intermediates, or products were docked to SOM representative conformations with the DOCK 6.5 program. Classification of ligand docking poses, also performed with SOM, clearly distinguished ligand functional classes: substrates, reaction intermediates, and product. This result illustrates the acuity of the SOM classification and supports the quality of the DOCK program poses. The protein-ligand interaction features for the different classes of poses will guide the search and design of novel inhibitors.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXktVOlsQ%253D%253D&md5=df3e503d422f22b5056948b748a9d256
47
Kitamura, Y.; Ebihara, A.; Agari, Y.; Shinkai, A.; Hirotsu, K.; Kuramitsu, S. Structure of d-alanine–d-alanine Ligase from Thermus Thermophilus HB8: Cumulative Conformational Change and Enzyme-ligand Interactions Acta Crystallogr., Sect. D: Biol. Crystallogr. 2009, 65, 1098– 1106 DOI: 10.1107/S0907444909029710
Google Scholar
There is no corresponding record for this reference.
48
MacKerell, A.; Bashford, D.; Bellott, M.; Dunbrack, R.; Evanseck, J.; Field, M.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D.; Prodhom, B.; Reiher, W.; Roux, B.; Schlenkrich, M.; Smith, J.; Stote, R.; Straub, J.; Watanabe, M.; Wiórkiewicz-Kuczera, J.; Yin, D.; Karplus, M. All-atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins J. Phys. Chem. B 1998, 102, 3586– 3616 DOI: 10.1021/jp973084f
Google Scholar
48
All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
MacKerell, A. D., Jr.; Bashford, D.; Bellott, M.; Dunbrack, R. L.; Evanseck, J. D.; Field, M. J.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F. T. K.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D. T.; Prodhom, B.; Reiher, W. E., III; Roux, B.; Schlenkrich, M.; Smith, J. C.; Stote, R.; Straub, J.; Watanabe, M.; Wiorkiewicz-Kuczera, J.; Yin, D.; Karplus, M.
Journal of Physical Chemistry B (1998), 102 (18), 3586-3616CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)
New protein parameters are reported for the all-atom empirical energy function in the CHARMM program. The parameter evaluation was based on a self-consistent approach designed to achieve a balance between the internal (bonding) and interaction (nonbonding) terms of the force field and among the solvent-solvent, solvent-solute, and solute-solute interactions. Optimization of the internal parameters used exptl. gas-phase geometries, vibrational spectra, and torsional energy surfaces supplemented with ab initio results. The peptide backbone bonding parameters were optimized with respect to data for N-methylacetamide and the alanine dipeptide. The interaction parameters, particularly the at. charges, were detd. by fitting ab initio interaction energies and geometries of complexes between water and model compds. that represented the backbone and the various side chains. In addn., dipole moments, exptl. heats and free energies of vaporization, solvation and sublimation, mol. vols., and crystal pressures and structures were used in the optimization. The resulting protein parameters were tested by applying them to noncyclic tripeptide crystals, cyclic peptide crystals, and the proteins crambin, bovine pancreatic trypsin inhibitor, and carbonmonoxy myoglobin in vacuo and in a crystal. A detailed anal. of the relationship between the alanine dipeptide potential energy surface and calcd. protein φ, χ angles was made and used in optimizing the peptide group torsional parameters. The results demonstrate that use of ab initio structural and energetic data by themselves are not sufficient to obtain an adequate backbone representation for peptides and proteins in soln. and in crystals. Extensive comparisons between mol. dynamics simulation and exptl. data for polypeptides and proteins were performed for both structural and dynamic properties. Calcd. data from energy minimization and dynamics simulations for crystals demonstrate that the latter are needed to obtain meaningful comparisons with exptl. crystal structures. The presented parameters, in combination with the previously published CHARMM all-atom parameters for nucleic acids and lipids, provide a consistent set for condensed-phase simulations of a wide variety of mols. of biol. interest.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXivVOlsb4%253D&md5=ebb5100dafd0daeee60ca2fa66c1324a
49
MacKerell, A. D.; Feig, M.; Brooks, C. L. Extending the Treatment of Backbone Energetics in Protein Force Fields: Limitations of gas-phase Quantum Mechanics in reproducing Protein Conformational Distributions in Molecular Dynamics Simulations J. Comput. Chem. 2004, 25, 1400– 1415 DOI: 10.1002/jcc.20065
Google Scholar
49
Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations
MacKerell, Alexander D., Jr.; Feig, Michael; Brooks, Charles L., III
Journal of Computational Chemistry (2004), 25 (11), 1400-1415CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
Computational studies of proteins based on empirical force fields represent a powerful tool to obtain structure-function relationships at an at. level, and are central in current efforts to solve the protein folding problem. The results from studies applying these tools are, however, dependent on the quality of the force fields used. In particular, accurate treatment of the peptide backbone is crucial to achieve representative conformational distributions in simulation studies. To improve the treatment of the peptide backbone, quantum mech. (QM) and mol. mech. (MM) calcns. were undertaken on the alanine, glycine, and proline dipeptides, and the results from these calcns. were combined with mol. dynamics (MD) simulations of proteins in crystal and aq. environments. QM potential energy maps of the alanine and glycine dipeptides at the LMP2/cc-pVxZ/MP2/6-31G* levels, where x = D, T, and Q, were detd., and are compared to available QM studies on these mols. The LMP2/cc pVQZ//MP2/6-31G* energy surfaces for all three dipeptides were then used to improve the MM treatment of the dipeptides. These improvements included addnl. parameter optimization via Monte Carlo simulated annealing and extension of the potential energy function to contain peptide backbone .vphi., ψ dihedral crossterms or a .vphi., ψ grid-based energy correction term. Simultaneously, MD simulations of up to seven proteins in their cryst. environments were used to validate the force field enhancements. Comparison with QM and crystallog. data showed that an addnl. optimization of the .vphi., ψ dihedral parameters along with the grid-based energy correction were required to yield significant improvements over the CHARMM22 force field. However, systematic deviations in the treatment of .vphi. and ψ in the helical and sheet regions were evident. Accordingly, empirical adjustments were made to the grid-based energy correction for alanine and glycine to account for these systematic differences. These adjustments lead to greater deviations from QM data for the two dipeptides but also yielded improved agreement with exptl. crystallog. data. These improvements enhance the quality of the CHARMM force field in treating proteins. This extension of the potential energy function is anticipated to facilitate improved treatment of biol. macromols. via MM approaches in general.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXlsVOgt7c%253D&md5=b2451bb5df548447f8b172a211bc1848
50
Jorgensen, W. Quantum and Statistical Mechanical Studies of Liquids. 10. Transferable Intermolecular Potential Functions for Water, Alcohols, and Ethers. Application to Liquid Water J. Am. Chem. Soc. 1981, 103, 335– 340 DOI: 10.1021/ja00392a016
Google Scholar
50
Quantum and statistical mechanical studies of liquids. 10. Transferable intermolecular potential functions for water, alcohols, and ethers. Application to liquid water
Jorgensen, William L.
Journal of the American Chemical Society (1981), 103 (2), 335-40CODEN: JACSAT; ISSN:0002-7863.
Transferable intermol. potential functions (TIPS) suitable for use in liq. simulations are reported for water, alcs., and ethers. Interaction sites are located on oxygens, hydroxyl hydrogens, and the carbons in alkyl groups. Each type of site has Coulomb and Lennard-Jones parameters chosen to yield reasonable structural and energetic results for both gas-phase dimers and pure liqs. A Monte Carlo simulation of liq. water at 25° using the TIP compares favorably with expt. or results from Clementi's CI potential except that the OO radial distribution function is calcd. to be too flat beyond the first solvent shell. Simulations of liq. methanol and ethanol have also been carried out as described in the accompanying papers. Overall, in view of the simplicity and transferability of the potentials, the initial results are most encouraging for the treatment of fluids with even more complex monomers and for extension to other types of interaction sites.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3MXotlCitA%253D%253D&md5=6160447b696ee61208228f2f542c4a74
51
Phillips, J. C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R. D.; Kale, L.; Schulten, K. Scalable Molecular Dynamics with NAMD J. Comput. Chem. 2005, 26, 1781– 1802 DOI: 10.1002/jcc.20289
Google Scholar
51
Scalable molecular dynamics with NAMD
Phillips, James C.; Braun, Rosemary; Wang, Wei; Gumbart, James; Tajkhorshid, Emad; Villa, Elizabeth; Chipot, Christophe; Skeel, Robert D.; Kale, Laxmikant; Schulten, Klaus
Journal of Computational Chemistry (2005), 26 (16), 1781-1802CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
NAMD is a parallel mol. dynamics code designed for high-performance simulation of large biomol. systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical mol. dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temp. and pressure controls used. Features for steering the simulation across barriers and for calcg. both alchem. and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomol. system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the mol. graphics/sequence anal. software VMD and the grid computing/collab. software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXht1SlsbbJ&md5=189051128443b547f4300a1b8fb0e034
52
Nam, K.; Gao, J.; York, D. M. An efficient Linear-scaling Ewald Method for Long-range Electrostatic Interactions in Combined QM/MM Calculations J. Chem. Theory Comput. 2005, 1, 2– 13 DOI: 10.1021/ct049941i
Google Scholar
52
An Efficient Linear-Scaling Ewald Method for Long-Range Electrostatic Interactions in Combined QM/MM Calculations
Nam, Kwangho; Gao, Jiali; York, Darrin M.
Journal of Chemical Theory and Computation (2005), 1 (1), 2-13CODEN: JCTCCE ISSN:. (American Chemical Society)
A method is presented for the efficient evaluation of long-range electrostatic forces in combined quantum mech. and mol. mech. (QM/MM) calcns. of periodic systems. The QM/MM-Ewald method is a linear-scaling electrostatic method that utilizes the particle mesh Ewald algorithm for calcn. of point charge interactions of mol. mech. atoms and a real-space multipolar expansion for the quantum mech. electrostatic terms plus a pairwise periodic correction factor for the QM and QM/MM interactions that does not need to be re-evaluated during the SCF procedure. The method is tested in a series of mol. dynamics simulations of the ion-ion assocn. of ammonium chloride and ammonium metaphosphate and the dissociative phosphoryl transfer of Me phosphate and acetyl phosphate. Results from periodic boundary mol. dynamics (PBMD) simulations employing the QM/MM-Ewald method are compared with corresponding PBMD simulations using electrostatic cutoffs and with results from nonperiodic stochastic boundary mol. dynamics (SBMD) simulations, with cutoffs and with full electrostatics (no cutoff). The present method allows extension of linear-scaling Ewald methods to mol. simulations of enzyme and ribozyme reactions that use combined QM/MM potentials.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXhtVOjt77L&md5=5e8d612ba0724cfd8f6073e85c630fde
53
Frenkel, D.; Smit, B. Understanding Molecular Simulation: from Algorithms to Applications, Vol. 1; Academic Press: New York, 2001.
Google Scholar
There is no corresponding record for this reference.
54
Martyna, G. J.; Tobias, D. J.; Klein, M. L. Constant Pressure Molecular Dynamics Algorithms J. Chem. Phys. 1994, 101, 4177– 4189 DOI: 10.1063/1.467468
Google Scholar
54
Constant pressure molecular dynamics algorithms
Martyna, Glenn J.; Tobias, Douglas J.; Klein, Michael L.
Journal of Chemical Physics (1994), 101 (5), 4177-89CODEN: JCPSA6; ISSN:0021-9606.
Modularly invariant equations of motion are derived that generate the isothermal-isobaric ensemble as their phase space avs. Isotropic vol. fluctuations and fully flexible simulation cells as well as a hybrid scheme that naturally combines the two motions are considered. The resulting methods are tested on two problems, a particle in a one-dimensional periodic potential and a spherical model of C60 in the solid/fluid phase.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXmtFeht7o%253D&md5=c14bd79c6398b0b30541e3cbe92851b0
55
Feller, S. E.; Zhang, Y.; Pastor, R. W.; Brooks, B. R. Constant Pressure Molecular Dynamics Simulation: the Langevin Piston Method J. Chem. Phys. 1995, 103, 4613– 4621 DOI: 10.1063/1.470648
Google Scholar
55
Constant pressure molecular dynamics simulation: the Langevin piston method
Feller, Scott E.; Zhang, Yuhong; Pastor, Richard W.; Brooks, Bernard R.
Journal of Chemical Physics (1995), 103 (11), 4613-21CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
A new method for performing mol. dynamics simulations under const. pressure is presented. In the method, which is based on the extended system formalism introduced by Andersen, the deterministic equations of motion for the piston degree of freedom are replaced by a Langevin equation; a suitable choice of collision frequency then eliminates the unphys. "ringing" of the vol. assocd. with the piston mass. In this way it is similar to the "weak coupling algorithm" developed by Berendsen and co-workers to perform mol. dynamics simulation without piston mass effects. It is shown, however, that the weak coupling algorithm induces artifacts into the simulation which can be quite severe for inhomogeneous systems such as aq. biopolymers or liq./liq. interfaces.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXotVentLo%253D&md5=219a4e0a48397a35fa2c62cf99bf225a
56
Ryckaert, J.; Ciccotti, G.; Berendsen, H. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes J. Comput. Phys. 1977, 23, 327– 341 DOI: 10.1016/0021-9991(77)90098-5
Google Scholar
56
Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes
Ryckaert, Jean Paul; Ciccotti, Giovanni; Berendsen, Herman J. C.
Journal of Computational Physics (1977), 23 (3), 327-41CODEN: JCTPAH; ISSN:0021-9991.
A numerical algorithm integrating the 3N Cartesian equation of motion of a system of N points subject to holonomic constraints is applied to mol. dynamics simulation of a liq. of 64 butane mols.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXktVGhsL4%253D&md5=b4aecddfde149117813a5ea4f5353ce2
57
Abrams, C. F.; Vanden-Eijnden, E. Large-scale Conformational Sampling of Proteins using Temperature-Accelerated Molecular Dynamics Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 4961– 4966 DOI: 10.1073/pnas.0914540107
Google Scholar
57
Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics
Abrams, Cameron F.; Vanden-Eijnden, Eric
Proceedings of the National Academy of Sciences of the United States of America (2010), 107 (11), 4961-4966, S4961/1-S4961/8CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
We show how to apply the method of temp.-accelerated mol. dynamics (TAMD) in collective variables to sample the conformational space of multidomain proteins in all-atom, explicitly solvated mol. dynamics simulations. The method allows the system to hyperthermally explore the free-energy surface in a set of collective variables computed at the phys. temp. As collective variables, we pick Cartesian coordinates of centers of contiguous subdomains. The method is applied to the GroEL subunit, a 55-kDa, three-domain protein, and HIV-1 gp120. For GroEL, the method induces in about 40 ns conformational changes that recapitulate the t → r'' transition and are not obsd. in unaccelerated mol. dynamics: The apical domain is displaced by 30 Å, with a twist of 90° relative to the equatorial domain, and the root-mean-squared deviation relative to the r'' conformer is reduced from 13 to 5 Å, representing fairly high predictive capability. For gp120, the method predicts both counter-rotation of inner and outer domains and disruption of the so-called bridging sheet. In particular, TAMD on gp120 initially in the CD4-bound conformation visits conformations that deviate by 3.6 Å from the gp120 conformer in complex with antibody F105, again reflecting good predictive capability. TAMD generates plausible all-atom models of the so-far structurally uncharacterized unliganded conformation of HIV-1 gp120, which may prove useful in the development of inhibitors and immunogens. The fictitious temp. employed also gives a rough est. of 10 kcal/mol for the free-energy barrier between conformers in both cases.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjvFGntrk%253D&md5=3217f5804cac528e40747cf6d236529c
58
Vassura, M.; Margara, L.; Medri, F.; di Lena, P.; Fariselli, P.; Casadio, R. Reconstruction of 3D Structures from Protein Contact Maps. In Bioinformatics Research and Applications; Springer: Berlin, Germany, 2007; pp 578– 589.
Google Scholar
There is no corresponding record for this reference.
59
Girvan, M.; Newman, M. E. Community Structure in Social and Biological Networks Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 7821– 7826 DOI: 10.1073/pnas.122653799
Google Scholar
59
Community structure in social and biological networks
Girvan, M.; Newman, M. E. J.
Proceedings of the National Academy of Sciences of the United States of America (2002), 99 (12), 7821-7826CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
A no. of recent studies have focused on the statistical properties of networked systems such as social networks and Worldwide Web. Researchers have concd. particularly on few properties that seem to be common to many networks: small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure in which network nodes are joined together in tightly knit group between which there are only looser connections. We propose method for detecting such communities, built around the idea using centrality indexes to find community boundaries. We test method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known-a collaboration network and a food web-and find that it detects significant and informative community divisions in both cases.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XkvVGjsL4%253D&md5=a0a1f47632a804f2a009425922fc8dfc
60
Kohonen, T. Self-Organizing Maps; Springer Series in Information Sciences: Heidelberg, Germany, 2001.
Google Scholar
There is no corresponding record for this reference.
61
Bouvier, G.; Desdouits, N.; Ferber, M.; Blondel, A.; Nilges, M. An Automatic Tool to Analyze and Cluster Macromolecular Conformations Based on Self-Organizing Maps Bioinformatics 2015, 31, 1490– 1492 DOI: 10.1093/bioinformatics/btu849
Google Scholar
61
An automatic tool to analyze and cluster macromolecular conformations based on self-organizing maps
Bouvier, Guillaume; Desdouits, Nathan; Ferber, Mathias; Blondel, Arnaud; Nilges, Michael
Bioinformatics (2015), 31 (9), 1490-1492CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
Motivation: Sampling the conformational space of biol. macromols. generates large sets of data with considerable complexity. Data-mining techniques, such as clustering, can ext. meaningful information. Among them, the self-organizing maps (SOMs) algorithm has shown great promise; in particular since its computation time rises only linearly with the size of the data set. Whereas SOMs are generally used with few neurons, we investigate here their behavior with large nos. of neurons. Results: We present here a python library implementing the full SOM anal. workflow. Large SOMs can readily be applied on heavy data sets. Coupled with visualization tools they have very interesting properties. Descriptors for each conformation of a trajectory are calcd. and mapped onto a 3D landscape, the U-matrix, reporting the distance between neighboring neurons. To delineate clusters, we developed the flooding algorithm, which hierarchically identifies local basins of the U-matrix from the global min. to the max.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1GntbrJ&md5=e569293d29512f33f57891989ace42a8
62
Dijkstra, E. A Note on Two Problems in Connexion with Graphs Numer. Math 1959, 1, 269– 271 DOI: 10.1007/BF01386390
Google Scholar
There is no corresponding record for this reference.
63
Mills, J.; Dean, P. M. Three-dimensional Hydrogen-bond Geometry and Probability Information from a Crystal Survey J. Comput.-Aided Mol. Des. 1996, 10, 607– 622 DOI: 10.1007/BF00134183
Google Scholar
63
Three-dimensional hydrogen-bond geometry and probability information from a crystal survey
Mills, J.E.J.; Dean, P.M.
Journal of Computer-Aided Molecular Design (1996), 10 (6), 607-622CODEN: JCADEQ; ISSN:0920-654X. (ESCOM)
An extensive crystal survey of the Cambridge Structural Database has been carried out to provide hydrogen-bond data for use in drug-design strategies. Previous crystal surveys have generated 1D frequency distributions of hydrogen-bond distances and angles, which are not sufficient to model the hydrogen bond as a ligand-receptor interaction. For each hydrogen-bonding group of interest to the drug designer, geometric hydrogen-bond criteria have been derived. The 3D distribution of complementary atoms about each hydrogen-bonding group has been ascertained by dividing the space about each group into bins of equal vol. and continuing the no. of obsd. hydrogen-bonding contacts in each bin. Finally, the propensity of each group to form a hydrogen bond has been calcd. Together, these data can be used to predict the potential site points with which a ligand could interact and therefore could be used in mol.-similarity studies, pharmacophore query searching of databases, or de novo design algorithms.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXlvVWitQ%253D%253D&md5=b533a996a701c76556cfd51d2737c820
64
Pettersen, E.; Goddard, T.; Huang, C.; Couch, G.; Greenblatt, D.; Meng, E.; Ferrin, T. UCSF Chimera—A Visualization System for Exploratory Research and Analysis J. Comput. Chem. 2004, 25, 1605– 1612 DOI: 10.1002/jcc.20084
Google Scholar
64
UCSF Chimera-A visualization system for exploratory research and analysis
Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C.; Couch, Gregory S.; Greenblatt, Daniel M.; Meng, Elaine C.; Ferrin, Thomas E.
Journal of Computational Chemistry (2004), 25 (13), 1605-1612CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale mol. assemblies such as viral coats, and Collab., which allows researchers to share a Chimera session interactively despite being at sep. locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and assocd. structures; ViewDock, for screening docked ligand orientations; Movie, for replaying mol. dynamics trajectories; and Vol. Viewer, for display and anal. of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXmvVOhsbs%253D&md5=944b175f440c1ff323705987cf937ee7
65
Liu, S.; Chang, J. S.; Herberg, J. T.; Horng, M.-M.; Tomich, P. K.; Lin, A. H.; Marotti, K. R. Allosteric Inhibition of Staphylococcus Aureus d-Alanine:d-Alanine Ligase revealed by Crystallographic Studies Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 15178– 15183 DOI: 10.1073/pnas.0604905103
Google Scholar
There is no corresponding record for this reference.
66
Shoichet, B.; Bodian, D.; Kuntz, I. Molecular Docking using Shape Descriptors J. Comput. Chem. 1992, 13, 380– 397 DOI: 10.1002/jcc.540130311
Google Scholar
66
Molecular docking using shape descriptors
Shoichet, Brian K.; Bodian, Dale L.; Kuntz, Irwin D.
Journal of Computational Chemistry (1992), 13 (3), 380-97CODEN: JCCHDD; ISSN:0192-8651.
Mol. docking explores the binding modes of two interacting mols. The technique is increasingly popular for studying protein-ligand interactions and for drug design. A fundamental problem with mol. docking is that orientation space is very large and grows combinatorially with the no. of degrees of freedom of the interacting mols. Here, algorithms are described and evaluated that improve the efficiency and accuracy of a shape-based docking method. Mol. organization and sampling techniques are used to remove the exponential time dependence on mol. size in docking calcns. The new techniques allow one to study systems that were prohibitively large for the original method. The new algorithms are tested in 10 different protein-ligand systems, including systems, including 7 systems where the ligand is itself a protein. In all cases, the new algorithms successfully reproduce the exptl. detd. configurations of the ligand in the protein.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38XitFSlu7g%253D&md5=a10e3291ad760e02c529a59f4b96ae1d
67
Meng, E.; Shoichet, B.; Kuntz, I. Automated Docking with Grid-Based Energy Evaluation J. Comput. Chem. 1992, 13, 505– 524 DOI: 10.1002/jcc.540130412
Google Scholar
67
Automated docking with grid-based energy evaluation
Meng, Elaine C.; Shoichet, Brian K.; Kuntz, Irwin D.
Journal of Computational Chemistry (1992), 13 (4), 505-24CODEN: JCCHDD; ISSN:0192-8651.
The ability to generate feasible binding orientations of a small mol. within a site of known structure is important for ligand design. The authors present a method that combines a rapid, geometric docking algorithm with the evaluation of mol. mechanics interaction energies. The computational costs of evaluation are minimal because the authors precalc. the receptor-dependent terms in the potential function at points on a three-dimensional grid. In four test cases where the components of crystallog. detd. complexes are redocked, the "force field" score correctly identifies the family of orientations closest to the exptl. binding geometry. Scoring functions that consider only steric factors or only electrostatic factors are less successful. The force field function will play an important role in efforts to search databases for potential lead compds.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38Xit1Omt7c%253D&md5=f472fffa0c9a61652b4c20e4dbbba69e
68
Lang, P.; Brozell, S.; Mukherjee, S.; Pettersen, E.; Meng, E.; Thomas, V.; Rizzo, R.; Case, D.; James, T.; Kuntz, I. DOCK 6: Combining Techniques to Model RNA-Small Molecule Complexes RNA 2009, 15, 1219– 1230 DOI: 10.1261/rna.1563609
Google Scholar
68
DOCK 6: combining techniques to model RNA-small molecule complexes
Lang, P. Therese; Brozell, Scott R.; Mukherjee, Sudipto; Pettersen, Eric F.; Meng, Elaine C.; Thomas, Veena; Rizzo, Robert C.; Case, David A.; James, Thomas L.; Kuntz, Irwin D.
RNA (2009), 15 (6), 1219-1230CODEN: RNARFU; ISSN:1355-8382. (Cold Spring Harbor Laboratory Press)
With an increasing interest in RNA therapeutics and for targeting RNA to treat disease, there is a need for the tools used in protein-based drug design, particularly DOCKing algorithms, to be extended or adapted for nucleic acids. Here, we have compiled a test set of RNA-ligand complexes to validate the ability of the DOCK suite of programs to successfully recreate exptl. detd. binding poses. With the optimized parameters and a minimal scoring function, 70% of the test set with less than seven rotatable ligand bonds and 26% of the test set with less than 13 rotatable bonds can be successfully recreated within 2 Å heavy-atom RMSD. When DOCKed conformations are rescored with the implicit solvent models AMBER generalized Born with solvent-accessible surface area (GB/SA) and Poisson-Boltzmann with solvent-accessible surface area (PB/SA) in combination with explicit water mols. and sodium counterions, the success rate increases to 80% with PB/SA for less than seven rotatable bonds and 58% with AMBER GB/SA and 47% with PB/SA for less than 13 rotatable bonds. These results indicate that DOCK can indeed be useful for structure-based drug design aimed at RNA. Our studies also suggest that RNA-directed ligands often differ from typical protein-ligand complexes in their electrostatic properties, but these differences can be accommodated through the choice of potential function. In addn., in the course of the study, we explore a variety of newly added DOCK functions, demonstrating the ease with which new functions can be added to address new scientific questions.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXmvFCntrY%253D&md5=1fc8e2043aea1fb513b504039ac37b58
69
Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of Multiple Amber Force Fields and Development of Improved Protein Backbone Parameters Proteins: Struct., Funct., Genet. 2006, 65, 712– 725 DOI: 10.1002/prot.21123
Google Scholar
69
Comparison of multiple Amber force fields and development of improved protein backbone parameters
Hornak, Viktor; Abel, Robert; Okur, Asim; Strockbine, Bentley; Roitberg, Adrian; Simmerling, Carlos
Proteins: Structure, Function, and Bioinformatics (2006), 65 (3), 712-725CODEN: PSFBAF ISSN:. (Wiley-Liss, Inc.)
The ff94 force field that is commonly assocd. with the Amber simulation package is one of the most widely used parameter sets for biomol. simulation. After a decade of extensive use and testing, limitations in this force field, such as over-stabilization of α-helixes, were reported by the authors and other researchers. This led to a no. of attempts to improve these parameters, resulting in a variety of "Amber" force fields and significant difficulty in detg. which should be used for a particular application. The authors show that several of these continue to suffer from inadequate balance between different secondary structure elements. In addn., the approach used in most of these studies neglected to account for the existence in Amber of two sets of backbone .vphi./ψ dihedral terms. This led to parameter sets that provide unreasonable conformational preferences for glycine. The authors report here an effort to improve the .vphi./ψ dihedral terms in the ff99 energy function. Dihedral term parameters are based on fitting the energies of multiple conformations of glycine and alanine tetrapeptides from high level ab initio quantum mech. calcns. The new parameters for backbone dihedrals replace those in the existing ff99 force field. This parameter set, which the authors denote ff99SB, achieves a better balance of secondary structure elements as judged by improved distribution of backbone dihedrals for glycine and alanine with respect to PDB survey data. It also accomplishes improved agreement with published exptl. data for conformational preferences of short alanine peptides and better accord with exptl. NMR relaxation data of test protein systems.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhtFWqt7fM&md5=de683a26eca9e83ae524726e97ac22fa
70
Richards, F. Areas, Volumes, Packing and Protein Structure Annu. Rev. Biophys. Bioeng. 1977, 6, 151– 176 DOI: 10.1146/annurev.bb.06.060177.001055
Google Scholar
70
Areas, volumes, packing, and protein structure
Richards, Frederic M.
Annual Review of Biophysics and Bioengineering (1977), 6 (), 151-76CODEN: ABPBBK; ISSN:0084-6589.
A review with 148 refs.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXks1CisrY%253D&md5=13c917ccbda3f36f1c6390a42c712818
71
Connolly, M. Solvent-Accessible Surfaces of Proteins and Nucleic Acids Science 1983, 221, 709– 713 DOI: 10.1126/science.6879170
Google Scholar
71
Solvent-accessible surfaces of proteins and nucleic acids
Connolly, Michael L.
Science (Washington, DC, United States) (1983), 221 (4612), 709-13CODEN: SCIEAS; ISSN:0036-8075.
A method is presented for anal. calcg. a smooth, 3-dimensional contour about a mol. The mol. surface envelope may be drawn on either color raster computer displays or real-time vector computer graphics systems. Mol. areas and vols. may be computed anal. from this surface representation. Unlike most previous computer graphics representations of mols., which imitate wire models or space-filling plastic spheres, this surface shows only the atoms that are accessible to solvent. This anal. method extends the earlier dot surface numerical algorithm, which was applied in enzymol., rational drug design, immunol., and understanding DNA base sequence recognition.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXlsFCkt74%253D&md5=371e91867872dcc45ff5d9c5cc142c95
72
Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin, T. E. A Geometric Approach to Macromolecule-Ligand Interactions J. Mol. Biol. 1982, 161, 269– 288 DOI: 10.1016/0022-2836(82)90153-X
Google Scholar
72
A geometric approach to macromolecule-ligand interactions
Kuntz, Irwin D.; Blaney, Jeffrey M.; Oatley, Stuart J.; Langridge, Robert; Ferrin, Thomas E.
Journal of Molecular Biology (1982), 161 (2), 269-88CODEN: JMOBAK; ISSN:0022-2836.
A method is described to explore geometrically feasible alignments of ligands and receptors of known structure. Algorithms are presented that examine many binding geometries and evaluate them in terms of steric overlap. The procedure uses specific mol. conformations. A method is included for finding putative binding sites on a macromol. surface. Results are reported for heme-myoglobin interaction and the binding of thyroid hormone analogs to prealbumin. In each case, the program finds structures within 1 Å of the x-ray results and also finds distinctly different geometries that provide good steric fits. The approach seems well-suited for generating conformations for energy refinement programs and interactive computer graphics routines.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL38XmtFajsbw%253D&md5=8a4234b24356ea5340f33d906cd71d3e
73
Srinivasan, J.; Cheatham, T.; Cieplak, P.; Kollman, P.; Case, D. A. Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate-DNA Helices J. Am. Chem. Soc. 1998, 120, 9401– 9409 DOI: 10.1021/ja981844+
Google Scholar
73
Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate-DNA Helixes
Srinivasan, Jayashree; Cheatham, Thomas E., III; Cieplak, Piotr; Kollman, Peter A.; Case, David A.
Journal of the American Chemical Society (1998), 120 (37), 9401-9409CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
We apply continuum solvent models to investigate the relative stability of A- and B-form helixes for three DNA sequences, d(CCAACGTTGG)2, d(ACCCGCGGGT)2, and d(CGCGAATTCGCG)2, a phosphoramidate-modified DNA duplex, p(CGCGAATTCGCG)2, in which the O3' atom in deoxyribose is replaced with NH, and an RNA duplex, r(CCAACGUUGG)2. Structures were taken as snapshots from multi-nanosecond mol. dynamics simulations computed in a consistent fashion using explicit solvent and with long-range electrostatics accounted for using the particle-mesh Ewald procedure. The electrostatic contribution to solvation energies were computed using both a finite-difference Poisson-Boltzmann (PB) model and a pairwise generalized Born model; nonelectrostatic contributions were estd. with a surface-area-dependent term. To these solvation free energies were added the mean solute internal energies (detd. from a mol. mechanics potential) and ests. of the solute entropy (from a harmonic anal.). Consistent with expt., the relative energies favor B-form helixes for DNA and A-form helixes for the NP-modified system and for RNA. Salt effects, modeled at the linear or nonlinear PB level, favor the A-form helixes by modest amts.; for d(ACCCGCGGGT)2, salt is nearly able to switch the conformational preference to "A". The results provide a phys. interpretation for the origins of the relative stabilities of A- and B-helixes and suggest that similar analyses might be useful in a variety of nucleic acid conformational problems.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXlsVyqtLo%253D&md5=f705ecbf7efdd5d42ab29f45a06c6e9d
74
Kollman, P.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, G.; Cieplak, P.; Srinivasan, J.; Case, D.; Cheatham, T. Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models Acc. Chem. Res. 2000, 33, 889– 897 DOI: 10.1021/ar000033j
Google Scholar
74
Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models
Kollman, Peter A.; Massova, Irina; Reyes, Carolina; Kuhn, Bernd; Huo, Shuanghong; Chong, Lillian; Lee, Matthew; Lee, Taisung; Duan, Yong; Wang, Wei; Donini, Oreola; Cieplak, Piotr; Srinivasan, Jaysharee; Case, David A.; Cheatham, Thomas E., III
Accounts of Chemical Research (2000), 33 (12), 889-897CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)
A review, with 63 refs. A historical perspective on the application of mol. dynamics (MD) to biol. macromols. is presented. Recent developments combining state-of-the-art force fields with continuum solvation calcns. have allowed us to reach the fourth era of MD applications in which one can often derive both accurate structure and accurate relative free energies from mol. dynamics trajectories. We illustrate such applications on nucleic acid duplexes, RNA hairpins, protein folding trajectories, and protein-ligand, protein-protein, and protein-nucleic acid interactions.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXmvFGiu7g%253D&md5=8436ee610ae145894428db1a1deff73c
75
Hawkins, G. D.; Cramer, C. J.; Truhlar, D. G. Pairwise Solute Descreening of Solute Charges from a Dielectric Medium Chem. Phys. Lett. 1995, 246, 122– 129 DOI: 10.1016/0009-2614(95)01082-K
Google Scholar
75
Pairwise solute descreening of solute charges from a dielectric medium
Hawkins, Gregory D.; Cramer, Christopher J.; Truhlar, Donald G.
Chemical Physics Letters (1995), 246 (1,2), 122-9CODEN: CHPLBC; ISSN:0009-2614. (Elsevier)
An algorithm is presented for incorporating a pairwise descreening approxn. into the calcn. of the electrostatic component of the polarization free energy of solvation within the generalized Born approxn. The method was tested on a set of 139 mols. contg. H, C, O, and N. The complexity of the descreening calcn. is greatly simplified by the pairwise approxn.; nevertheless, using the pairwise descreening method to parameterize a new version of a previous generalized Born solvation model, it was found that the rms error relative to expt. increased by only 0.2 kcal/mol.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXpsV2mt74%253D&md5=82ab77c3f7d9822bbd5e165701fcb6c4
76
Hawkins, G. D.; Cramer, C. J.; Truhlar, D. G. Parametrized Models of Aqueous Free Energies of Solvation Based on Pairwise Descreening of Solute Atomic Charges from a Dielectric Medium J. Phys. Chem. 1996, 100, 19824– 19839 DOI: 10.1021/jp961710n
Google Scholar
76
Parametrized Models of Aqueous Free Energies of Solvation Based on Pairwise Descreening of Solute Atomic Charges from a Dielectric Medium
Hawkins, Gregory D.; Cramer, Christopher J.; Truhlar, Donald G.
Journal of Physical Chemistry (1996), 100 (51), 19824-19839CODEN: JPCHAX; ISSN:0022-3654. (American Chemical Society)
The pairwise descreening approxn. provides a rapid computational algorithm for the evaluation of solute shape effects on electrostatic contributions to solvation energies. In this article the authors show that solvation models based on this algorithm are useful for predicting free energies of solvation across a wide range of solute functionalities, and six new general parametrizations of aq. free energies of solvation based on this approach are presented. The first new model is based on SM2-type at. surface tensions, the AM1 model for the solute, and Mulliken charges. The next two new models are based on SM5-type surface tensions, either the AM1 or the PM3 model for the solute, and Mulliken charges. The final three models are based on SM5-type at. surface tensions and are parametrized using the AM1 or the PM3 model for the solute and CM1 charges. The parametrizations are based on exptl. data for a set of 219 neutral solute mols. contg. a wide range of org. functional groups and the atom types H, C, N, O, F, P, S, Cl, Br, and I and on data for 42 ions contg. the same elements. The av. errors relative to expt. are slightly better than previous methods, but-more significantly-the computational cost is reduced for large mols., and the methods are well suited to using analytic derivs.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XntFSgsb8%253D&md5=b85ff8ba5589119be8f11360a6ea7d9e
77
Rizzo, R.; Aynechi, T.; Case, D. A.; Kuntz, I. Estimation of Absolute Free Energies of Hydration using Continuum Methods: Accuracy of Partial Charge Models and Optimization of Nonpolar Contributions J. Chem. Theory Comput. 2006, 2, 128– 139 DOI: 10.1021/ct050097l
Google Scholar
77
Estimation of Absolute Free Energies of Hydration Using Continuum Methods: Accuracy of Partial Charge Models and Optimization of Nonpolar Contributions
Rizzo, Robert C.; Aynechi, Tiba; Case, David A.; Kuntz, Irwin D.
Journal of Chemical Theory and Computation (2006), 2 (1), 128-139CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
Abs. free energies of hydration (ΔGhyd) for more than 500 neutral and charged compds. have been computed, using Poisson-Boltzmann (PB) and Generalized Born (GB) continuum methods plus a solvent-accessible surface area (SA) term, to evaluate the accuracy of eight simple point-charge models used in mol. modeling. The goal is to develop improved procedures and protocols for protein-ligand binding calcns. and virtual screening (docking). The best overall PBSA and GBSA results, in comparison with exptl. ΔGhyd values for small mols., were obtained using MSK, RESP, or ChelpG charges obtained from ab initio calcns. using 6-31G* wave functions. Correlations using semiempirical (AM1BCC, AM1CM2, and PM3CM2) or empirical (Gasteiger-Marsili and MMFF94) methods yielded mixed results, particularly for charged compds. For neutral compds., the AM1BCC method yielded the best agreement with exptl. results. In all cases, the PBSA and GBSA results are highly correlated (overall r2 = 0.94), which highlights the fact that various partial charge models influence the final results much more than which continuum method is used to compute hydration free energies. Overall improved agreement with exptl. results was demonstrated using atom-based consts. in place of a single surface area term. Sets of optimized SA consts., suitable for use with a given charge model, were derived by fitting to the difference in exptl. free energies and polar continuum results. The use of optimized atom-based SA consts. for the computation of ΔGhyd can fine-tune already reasonable agreement with exptl. results, ameliorate gross deficiencies in any particular charge model, account for non-optimal radii, or correct for systematic errors.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXht1SrsL3N&md5=0847c6f2749bad3b9196a4d3a2baa8cf
78
Lessard, I. A.; Healy, V. L.; Park, I.-S.; Walsh, C. T. Determinants for Differential Effects on d-Ala-d-lactate vs d-Ala-d-Ala Formation by the VanA Ligase from Vancomycin-resistant Enterococci Biochemistry 1999, 38, 14006– 14022 DOI: 10.1021/bi991384c
Google Scholar
There is no corresponding record for this reference.
79
Song, J.; Singh, M. From Hub Proteins to Hub Modules: the Relationship between Essentiality and Centrality in the Yeast Interactome at Different Scales of Organization PLoS Comput. Biol. 2013, 9, e1002910 DOI: 10.1371/journal.pcbi.1002910
Google Scholar
There is no corresponding record for this reference.
80
Hert, J.; Keiser, M. J.; Irwin, J. J.; Oprea, T. I.; Shoichet, B. K. Quantifying the Relationships among Drug Classes J. Chem. Inf. Model. 2008, 48, 755– 765 DOI: 10.1021/ci8000259
Google Scholar
80
Quantifying the Relationships among Drug Classes
Hert, Jerome; Keiser, Michael J.; Irwin, John J.; Oprea, Tudor I.; Shoichet, Brian K.
Journal of Chemical Information and Modeling (2008), 48 (4), 755-765CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacol. We calcd. the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calcd. with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calc. the ligand-set similarities and to the chem. representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXjt1WqsrY%253D&md5=628dde363e13134238c9de5dc27acd7e

Cited By

Click to copy section linkSection link copied!

This article is cited by 7 publications.

Stefano Motta, Lara Callea, Laura Bonati, Alessandro Pandini. PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations. Journal of Chemical Theory and Computation 2022, 18 (3) , 1957-1968. https://doi.org/10.1021/acs.jctc.1c01163
Sojeong Kim, Beom Gi Park, Hyunbin Jin, Daeyeol Lee, Jie Ying Teoh, Yung Jae Kim, Sak Lee, Soo-Jung Kim, Sang Hyun Moh, Dongwon Yoo, Wonwoo Choi, Ji-Sook Hahn. Efficient production of natural sunscreens shinorine, porphyra-334, and mycosporine-2-glycine in Saccharomyces cerevisiae. Metabolic Engineering 2023, 78 , 137-147. https://doi.org/10.1016/j.ymben.2023.05.009
Sneha B. Paymal, Sagar S. Barale, Shirishkumar V. Supanekar, Kailas D. Sonawane. Structure based virtual screening, molecular dynamic simulation to identify the oxadiazole derivatives as inhibitors of Enterococcus D-Ala-D-Ser ligase for combating vancomycin resistance. Computers in Biology and Medicine 2023, 159 , 106965. https://doi.org/10.1016/j.compbiomed.2023.106965
Eléa Paillares, Maud Marechal, Léa Swistak, Landry Tsoumtsa Meda, Emmanuel Lemichez, Thérèse E. Malliavin. Conformational Insights into the Control of CNF1 Toxin Activity by Peptidyl-Prolyl Isomerization: A Molecular Dynamics Perspective. International Journal of Molecular Sciences 2021, 22 (18) , 10129. https://doi.org/10.3390/ijms221810129
Po Wei Kang, Annie M. Westerlund, Jingyi Shi, Kelli McFarland White, Alex K. Dou, Amy H. Cui, Jonathan R. Silva, Lucie Delemotte, Jianmin Cui. Calmodulin acts as a state-dependent switch to control a cardiac potassium channel opening. Science Advances 2020, 6 (50) https://doi.org/10.1126/sciadv.abd6798
Annie M. Westerlund, Oliver Fleetwood, Sergio Pérez-Conesa, Lucie Delemotte. Network analysis reveals how lipids and other cofactors influence membrane protein allostery. The Journal of Chemical Physics 2020, 153 (14) https://doi.org/10.1063/5.0020974
Nathalie Duclert-Savatier, Guillaume Bouvier, Michael Nilges, Thérèse E. Malliavin, . Conformational sampling of CpxA: Connecting HAMP motions to the histidine kinase function. PLOS ONE 2018, 13 (11) , e0207899. https://doi.org/10.1371/journal.pone.0207899

Download PDF

Get e-Alerts

Journal of Chemical Information and Modeling

Cite this: J. Chem. Inf. Model. 2016, 56, 9, 1762–1775

Click to copy citationCitation copied!

https://doi.org/10.1021/acs.jcim.6b00211

Published August 31, 2016

Request reuse permissions

Article Views

Altmetric

Citations

Learn about these metrics

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

Abstract
High Resolution Image
Download MS PowerPoint Slide
Figure 1
Figure 1. (a) Three-dimensional (3D) view of the X-ray crystallographic structure of VanA, colored according to its domains: the N-terminal [A2–G121] shown in blue, the C-terminal [G212–A342] shown in black, which includes the ω-loop [L236–A256] shown in green, and the central domain [C122–S211] shown in red, which includes the opposite domain [A149–Q208] shown in yellow. The disulfide bridge C52–C64, located in the N-terminal domain, is shown with magenta labels (bottom right). (b) Localization of the collective variables (CV) used for the different TAMD calculations on a cartoon view of VanA extracted at the end of a 10 ns MD trajectory. The three structural CV are shown in orange and the five CV obtained from contact communities calculations are shown in cyan.
High Resolution Image
Download MS PowerPoint Slide
Figure 2
Figure 2. Communities determined by the Girvan–Newman algorithm (59) along the MD and TAMD trajectories recorded on VanA. The same color code was kept for the communities both on the 3D structures and on the graphs: the communities mainly located in the N-terminal region (numbers 0 and 1) are shown in blue and red; the Middle (number 2) community is shown in magenta, if it exists; the Opposite region is shown in yellow (number 3); the ω-loop and the main part of the C-terminal are shown in green (number 4). Projection of the communities calculated on a 30-ns trajectory of VanA for (a) MD, (c) TAMD_ON, (e) TAMD_ωN, (g) TAMD_OωN, (i) TAMD_MD, and (k) TAMD_5CV. Also shown is a graph of the interconnectivity calculated between the different communities for (b) MD, (d) TAMD_ON, (f) TAMD_ωN, (h) TAMD_OωN, (j) TAMD_MD, and (l) TAMD_5CV. The collective variables (CV) used for TAMD trajectories are represented by orange balls when they were derived from structural calculations and cyan balls if they were obtained from the communities calculations.
High Resolution Image
Download MS PowerPoint Slide
Figure 3
Figure 3. Definition of collective variables (CV) and of contact communities displayed on the VanA sequence. The first line contains the definition of structural collective variables (CVN-Xr, CVO-Xr, CVω-Xr: see Table S1) determined from an analysis of the structure 1E4E. The second line contains the definition of dynamical collective variables (CVE0-Com, CVE1-Com, CVM-Com, CVO-Com, CVω-Com: Table S1) determined from a community analysis using the Girvan–Newman algorithm over the 30-ns MD trajectory. The third line contains the definition of communities (Ends_Oc, Ends_1c, Middle_c, Opposite_c, ω_c: see Table S3) determined by the Girvan–Newman algorithm on the trajectory TAMD_ωN. The following color code is used. For the structural CV: CVN-Xr (blue), CVO-Xr (yellow), and CVω-Xr (green). For the dynamical CV: CVE0-Com (blue), CVE1-Com (red), CVM-Com (magenta), CVO-Com (yellow), and CVω-Com (green). For the TAMD_ωN communities: Ends_Oc (blue), Ends_1c (red), Middle_c (magenta), Opposite_c (yellow), and ω_c (green).
High Resolution Image
Download MS PowerPoint Slide
Figure 4
Figure 4. Clustering of VanA conformations sampled along MD and TAMD trajectories, using SOM. The root mean square deviation (RMSD) from the starting conformation of the trajectories is shown in a prune-green heat map (in Å). The conformation sets associated with the medoid of each cluster are depicted in putty cartoons. On the cartoons, the root-mean-square fluctuation (RMSF) of the backbone is represented by the width of the main chain and by a blue–green–red color scale corresponding to the RMSF values within the corresponding SOM cluster.
High Resolution Image
Download MS PowerPoint Slide
Figure 5
Figure 5. Detailed exploration of the SOM map by each trajectory. The starting points are shown in pink and the ending ones are shown in magenta. The blue–green–red color scale represents the local root-mean-square deviation (RMSD), from the starting structure for each structure (values shown are given in Å).
High Resolution Image
Download MS PowerPoint Slide
Figure 6
Figure 6. (a) Tube representation of VanA with the ω-loop in green and the opposite domain in yellow. Their own centers of mass is marked with a ball of the same color and respectively called ω and O. The center of mass of the entire protein VanA is called C (shown in red) and the center of mass of the N-terminal region, called N (shown in blue). (b, c) Projections of the angles on the SOM using a prune-green heat map: (panel (b)) and (panel (c)). The angles are expressed in degrees.
High Resolution Image
Download MS PowerPoint Slide
Figure 7
Figure 7. Kinetic clustering of the VanA conformation using the Louvain greedy algorithm on the SOM neurones. A given color is associated with each of the 15 obtained clusters. For the three clusters, including nonconnected regions (1, 5, and 7), the disconnected regions are labeled, respectively, as 1 and 1′, 5 to 5″, and 7 to 7′′. The representative conformations corresponding to each disconnected region are drawn superimposed in cartoons.
High Resolution Image
Download MS PowerPoint Slide
Figure 8
Figure 8. (a) Opening path traced on the U-matrix. The medoids of each clusters, labeled from A to F, are shown in red and their minimum spanning link is shown in red. (b) GBSA score (in kcal/mol) for the molecules involved in the enzymatic reaction: the substrates d-Ala, d-Ala-(P), d-Lac; the reaction intermediate homologous, PHY; the product of the enzymatic reaction d-Ala-d-Lac; and an allosteric inhibitor. (65) The GBSA score is plotted along the conformations labeled from 0 to 60, extracted from the opening path.
High Resolution Image
Download MS PowerPoint Slide
Figure 9
Figure 9. Most important hydrogen bonds for the prediction of the kinetic cluster along the opening path. The protein structure is displayed in trace, with the Opposite domain (residues [149–208]) colored orange and the ω-loop (residues [236–256]) colored green. The hydrogen bonds within the ω-loop and the opposite domain are colored cyan, and the hydrogen bonds between these protein domains and other protein regions are colored red. The other hydrogen bonds are gray.
High Resolution Image
Download MS PowerPoint Slide
References
This article references 80 other publications.
1. 1
  Tóth-Petróczy, Á; Tawfik, D. S. The Robustness and Innovability of Protein Folds Curr. Opin. Struct. Biol. 2014, 26, 131– 138 DOI: 10.1016/j.sbi.2014.06.007
  1
  The robustness and innovability of protein folds
  Toth-Petroczy, Agnes; Tawfik, Dan S.
  Current Opinion in Structural Biology (2014), 26 (), 131-138CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
  A review. Assignment of protein folds to functions indicates that >60% of folds carry out one or two enzymic functions, while few folds, for example, the TIM-barrel and Rossmann folds, exhibit hundreds. Are there structural features that make a fold amenable to functional innovation (innovability). Do these features relate to robustness - the ability to readily accumulate sequence changes. We discuss several hypotheses regarding the relationship between the architecture of a protein and its evolutionary potential. We describe how, in a seemingly paradoxical manner, opposite properties, such as high stability and rigidity vs. conformational plasticity and structural order vs. disorder, promote robustness and/or innovability. We hypothesize that polarity - differentiation and low connectivity between a protein's scaffold and its active-site - is a key prerequisite for innovability.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXht1ylt7zP&md5=0ab133a3c623c884c82831f018dc983d
2. 2
  Winterbach, W.; Van Mieghem, P.; Reinders, M.; Wang, H.; de Ridder, D. Topology of Molecular Interaction Networks BMC Syst. Biol. 2013, 7, 90 DOI: 10.1186/1752-0509-7-90
  2
  Topology of molecular interaction networks
  Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick
  BMC Systems Biology (2013), 7 (), 90/1-90/15CODEN: BSBMCC; ISSN:1752-0509. (BioMed Central Ltd.)
  A review. Mol. interactions are often represented as network models which have become the common language of many areas of biol. Graphs serve as convenient math. representations of network models and have themselves become objects of study. Their topol. has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that mol. interaction network topol. is related to biol. function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of mol. interactions considered and themetrics used to study them. It is unclear whether global network topol. drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artifact of representing complex mol. interaction networks as graphs. Nevertheless, network biol. has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that mol. interaction networks can be naturally decompd. into subsystems (such as modules and pathways), topol. is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biol. network topol. to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of mol. interaction networks further.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXltlGnt7c%253D&md5=8e6a5e5d324d7891b7dd3fa6ebba09e6
3. 3
  Zhang, Y.; Gao, P.; Yuan, J. S. Plant Protein–Protein Interaction Network and Interactome Curr. Genomics 2010, 11, 40 DOI: 10.2174/138920210790218016
  3
  Plant protein-protein interaction network and interactome
  Zhang, Yixiang; Gao, Peng; Yuan, Joshua S.
  Current Genomics (2010), 11 (1), 40-46CODEN: CGUEA8; ISSN:1389-2029. (Bentham Science Publishers Ltd.)
  A review. Protein-protein interaction network represents an important aspect of systems biol. The understanding of the plant protein-protein interaction network and interactome will provide crucial insights into the regulation of plant developmental, physiol., and pathol. processes. In this review, we will first define the concept of plant interactome and the protein-protein interaction network. The significance of the plant interactome study will be discussed. We will then compare the pros and cons for different strategies for interactome mapping including yeast two-hybrid system (Y2H), affinity purifn. mass spectrometry (AP-MS), bimol. fluorescence complementation (BiFC), and in silico prediction. The application of these platforms on specific plant biol. questions will be further discussed. The recent advancements revealed the great potential for plant protein-protein interaction network and interactome to elucidate mol. mechanisms for signal transduction, stress responses, cell cycle control, pattern formation, and others. Mapping the plant interactome in model species will provide important guideline for the future study of plant biol.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjtFajtrw%253D&md5=03d57d4ebe2c4ea332b5dcadaecd4414
4. 4
  Rual, J.-F.; Venkatesan, K.; Hao, T.; Hirozane-Kishikawa, T.; Dricot, A.; Li, N.; Berriz, G. F.; Gibbons, F. D.; Dreze, M.; Ayivi-Guedehoussou, N.; Klitgord, N.; Simon, C.; Boxem, M.; Milstein, S.; Rosenberg, J.; Goldberg, D. S.; Zhang, L. V.; Wong, S. L.; Franklin, G.; Li, S.; Albala, J. S.; Lim, J.; Fraughton, C.; Llamosas, E.; Cevik, S.; Bex, C.; Lamesch, P.; Sikorski, R. S.; Vandenhaute, J.; Zoghbi, H. Y.; Smolyar, A.; Bosak, S.; Sequerra, R.; Doucette-Stamm, L.; Cusick, M. E.; Hill, D. E.; Roth, F. P.; Vidal, M. Towards a Proteome-Scale Map of the Human Protein–Protein Interaction Network Nature 2005, 437, 1173– 1178 DOI: 10.1038/nature04209
  4
  Towards a proteome-scale map of the human protein-protein interaction network
  Rual, Jean-Francois; Venkatesan, Kavitha; Hao, Tong; Hirozane-Kishikawa, Tomoko; Dricot, Amelie; Li, Ning; Berriz, Gabriel F.; Gibbons, Francis D.; Dreze, Matija; Ayivi-Guedehoussou, Nono; Klitgord, Niels; Simon, Christophe; Boxem, Mike; Milstein, Stuart; Rosenberg, Jennifer; Goldberg, Debra S.; Zhang, Lan V.; Wong, Sharyl L.; Franklin, Giovanni; Li, Siming; Albala, Joanna S.; Lim, Janghoo; Fraughton, Carlene; Llamosas, Estelle; Cevik, Sebiha; Bex, Camille; Lamesch, Philippe; Sikorski, Robert S.; Vandenhaute, Jean; Zoghbi, Huda Y.; Smolyar, Alex; Bosak, Stephanie; Sequerra, Reynaldo; Doucette-Stamm, Lynn; Cusick, Michael E.; Hill, David E.; Roth, Frederick P.; Vidal, Marc
  Nature (London, United Kingdom) (2005), 437 (7062), 1173-1178CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  Systematic mapping of protein-protein interactions, or interactome mapping, was initiated in model organisms, starting with defined biol. processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topol. and dynamic features of interactome networks that relate to known biol. properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of ∼8,100 currently available Gateway-cloned open reading frames and detected ∼2,800 interactions. This data set, called CCSB-HI1, has a verification rate of ∼78% as revealed by an independent co-affinity purifn. assay, and correlates significantly with other biol. attributes. The CCSB-HI1 data set increases by ∼70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-assocd. proteins. This work represents an important step toward a systematic and comprehensive human interactome project.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhtFahtLzP&md5=5485dfc7a711b2c7f649402475f1046d
5. 5
  Li, S.; Armstrong, C. M.; Bertin, N.; Ge, H.; Milstein, S.; Boxem, M.; Vidalain, P.-O.; Han, J.-D. J.; Chesneau, A.; Hao, T.; Goldberg, D. S.; Li, N.; Martinez, M.; Rual, J.-F.; Lamesch, P.; Xu, L.; Tewari, M.; Wong, S. L.; Zhang, L. V.; Berriz, G. F.; Jacotot, L.; Vaglio, P.; Reboul, J.; Hirozane-Kishikawa, T.; Li, Q.; Gabel, H. W.; Elewa, A.; Baumgartner, B.; Rose, D. J.; Yu, H.; Bosak, S.; Sequerra, R.; Fraser, A.; Mango, S. E.; Saxton, W. M.; Strome, S.; van den Heuvel, S.; Piano, F.; Vandenhaute, J.; Sardet, C.; Gerstein, M.; Doucette-Stamm, L.; Gunsalus, K. C.; Harper, J. W.; Cusick, M. E.; Roth, F. P.; Hill, D. E.; Vidal, M. A Map of the Interactome Network of the Metazoan C. Elegans Science 2004, 303, 540– 543 DOI: 10.1126/science.1091403
  5
  A Map of the Interactome Network of the Metazoan C. elegans
  Li, Siming; Armstrong, Christopher M.; Bertin, Nicolas; Ge, Hui; Milstein, Stuart; Boxem, Mike; Vidalain, Pierre-Olivier; Han, Jing-Dong J.; Chesneau, Alban; Hao, Tong; Goldberg, Debra S.; Li, Ning; Martinez, Monica; Rual, Jean-Francois; Lamesch, Philippe; Xu, Lai; Tewari, Muneesh; Wong, Sharyl L.; Zhang, Lan V.; Berriz, Gabriel F.; Jacotot, Laurent; Vaglio, Philippe; Reboul, Jerome; Hirozane-Kishikawa, Tomoko; Li, Qianru; Gabel, Harrison W.; Elewa, Ahmed; Baumgartner, Bridget; Rose, Debra J.; Yu, Haiyuan; Bosak, Stephanie; Sequerra, Reynaldo; Fraser, Andrew; Mango, Susan E.; Saxton, William M.; Strome, Susan; van den Heuvel, Sander; Piano, Fabio; Vandenhaute, Jean; Sardet, Claude; Gerstein, Mark; Doucette-Stamm, Lynn; Gunsalus, Kristin C.; Harper, J. Wade; Cusick, Michael E.; Roth, Frederick P.; Hill, David E.; Vidal, Marc
  Science (Washington, DC, United States) (2004), 303 (5657), 540-544CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
  To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purifn. assays exptl. validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains ∼5500 interactions. Topol. and biol. features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biol. hypotheses.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXlt1emtA%253D%253D&md5=bdd8075e085c295f7aa4dafde0a046fb
6. 6
  Ho, Y.; Gruhler, A.; Heilbut, A.; Bader, G. D.; Moore, L.; Adams, S.-L.; Millar, A.; Taylor, P.; Bennett, K.; Boutilier, K.; Yang, L.; Wolting, C.; Donaldson, I.; Schandorff, S.; Shewnarane, J.; Vo, M.; Taggart, J.; Goudreault, M.; Muskat, B.; Alfarano, C.; Dewar, D.; Lin, Z.; Michalickova, K.; Willems, A. R.; Sassi, H.; Nielsen, P. A.; Rasmussen, K. J.; Andersen, J. R.; Johansen, L. E.; Hansen, L. H.; Jespersen, H.; Podtelejnikov, A.; Nielsen, E.; Crawford, J.; Poulsen, V.; Sørensen, B. D.; Matthiesen, J.; Hendrickson, R. C.; Gleeson, F.; Pawson, T.; Moran, M. F.; Durocher, D.; Mann, M.; Hogue, C. W. V.; Figeys, D.; Tyers, M. Systematic Identification of Protein Complexes in Saccharomyces Cerevisiae by Mass Spectrometry Nature 2002, 415, 180– 183 DOI: 10.1038/415180a
  6
  Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry
  Ho, Yuen; Gruhler, Albrecht; Hellbut, Adrian; Bader, Gary D.; Moore, Lynda; Adams, Sally-Un; Millar, Anna; Taylor, Paul; Bennett, Kelryn; Boutiller, Kelly; Yang, Lingyun; Wolting, Cheryl; Donaldson, Ian; Schandorff, Soren; Shewnarane, Juanita; Vo, Mai; Taggartt, Joanne; Goudreault, Marilyn; Muskat, Brenda; Alfarano, Cris; Dewar, Danlelle; Lin, Zhen; Michallckova, Katerina; Willems, Andrew R.; Sassi, Holly; Nielsen, Peter A.; Rasmussen, Karina J.; Andersen, Jens R.; Johansen, Lens E.; Hansen, Lykke H.; Jespersen, Hans; Podtelejnikov, Alexandre; Nielsen, Eva; Crawford, Janne; Poulsen, Vibeke; Sorensen, Birgitte D.; Matthlesen, Jesper; Hendrickson, Ronald C.; Gleeson, Frank; Paweson, Tony; Moran, Michael F.; Durocher, Daniel; Mann, Matthias; Hogue, Christopher W. V.; Figeys, Daniel; Tyers, Mike
  Nature (London, United Kingdom) (2002), 415 (6868), 180-183CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 assocd. proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signaling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an av. threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity obsd. in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38Xms1SnsA%253D%253D&md5=13ac165a0eb6cfeb2c4b5d17619a797d
7. 7
  Rain, J.-C.; Selig, L.; De Reuse, H.; Battaglia, V.; Reverdy, C.; Simon, S.; Lenzen, G.; Petel, F.; Wojcik, J.; Schächter, V.; Chemama, Y.; Labigne, A.; Legrain, P. The Protein–Protein Interaction Map of Helicobacter Pylori Nature 2001, 409, 211– 215 DOI: 10.1038/35051615
  7
  The protein-protein interaction map of Helicobacter pylori
  Rain, Jean-Christophe; Selig, Luc; De Reuse, Hilde; Battaglia, Veronique; Reverdy, Ciline; Simon, Stophane; Lenzen, Gerlinde; Petel, Fablen; Wojcik, Jerime; Schachter, Vincent; Chemama, Y.; Labigne, Agnes; Legrain, Pierre
  Nature (London) (2001), 409 (6817), 211-215CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function. As elementary constituents of cellular protein complexes and pathways, protein-protein interactions are key determinants of protein function. Here we have built a large-scale protein-protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides. Over 1200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The detn. of a reliability score for every single protein-protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biol. pathways.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXlvFSrtA%253D%253D&md5=1ba7c0dce240ee469894f2374d9c5029
8. 8
  Shen, R.; Guda, C. Applied Graph-Mining Algorithms to study Biomolecular Interaction Networks BioMed Res. Int. 2014, 2014, 1 DOI: 10.1155/2014/439476
  There is no corresponding record for this reference.
9. 9
  Han, J.-D. J.; Bertin, N.; Hao, T.; Goldberg, D. S.; Berriz, G. F.; Zhang, L. V.; Dupuy, D.; Walhout, A. J.; Cusick, M. E.; Roth, F. P.; Vidal, M. Evidence for Dynamically Organized Modularity in the Yeast Protein-Protein Interaction Network Nature 2004, 430, 88– 93 DOI: 10.1038/nature02555
  9
  Evidence for dynamically organized modularity in the yeast protein-protein interaction network
  Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc
  Nature (London, United Kingdom) (2004), 430 (6995), 88-93CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  In apparently scale-free protein-protein interaction networks, or 'interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the 'hubs', interact with many partners. Both biol. and non-biol. scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topol. of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approx. threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: 'party' hubs, which interact with most of their partners simultaneously, and date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biol. processes-or modules -to each other, whereas party hubs function inside modules.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXlt1Cqt7s%253D&md5=5df12bb50c608fe5023f9b0fb4efd503
10. 10
  Fliri, A. F.; Loging, W. T.; Volkmann, R. A. Cause-Effect Relationships in Medicine: A Protein Network Perspective Trends Pharmacol. Sci. 2010, 31, 547– 555 DOI: 10.1016/j.tips.2010.07.005
  There is no corresponding record for this reference.
11. 11
  Ma, X.; Gao, L. Biological Network Analysis: Insights into Structure and Functions Briefings Funct. Genomics 2012, 11, 434– 442 DOI: 10.1093/bfgp/els045
  11
  Biological network analysis: insights into structure and functions
  Ma Xiaoke; Gao Lin
  Briefings in functional genomics (2012), 11 (6), 434-42 ISSN:.
  In the past two decades, great efforts have been devoted to extract the dependence and interplay between structure and functions in biological networks because they have strong relevance to biological processes. In this article, we reviewed the recent development in the biological network analysis. In detail, we first reviewed the interactome topological properties of biological networks, the methods for structure and functional patterns.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s7ms1arsA%253D%253D&md5=5cbf86d6186022802aa1a99745624178
12. 12
  Zhou, T.-T. Network Systems Biology for Targeted Cancer Therapies Aizheng 2012, 31, 134 DOI: 10.5732/cjc.011.10282
  12
  Network systems biology for targeted cancer therapies
  Zhou, Tingting
  Chinese Journal of Cancer (2012), 31 (3), 134-141CODEN: CJCHDJ ISSN:. (Sun Yat-sen University Cancer Center)
  A review. The era of targeted cancer therapies has arrived. However, due to the complexity of biol. systems, the current progress is far from enough. From biol. network modeling to structural/dynamic network anal., network systems biol. provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells. It has also introduced great changes into the research paradigm of cancer-assocd. drug discovery and drug resistance.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XmtF2rsLc%253D&md5=944c914b377de2c0ce3a5f435a0f1cca
13. 13
  Sukumar, N.; Krein, M. P. Graphs and Networks in Chemical and Biological Informatics: Past, Present and Future Future Med. Chem. 2012, 4, 2039– 2047 DOI: 10.4155/fmc.12.128
  13
  Graphs and networks in chemical and biological informatics: past, present and future
  Sukumar, N.; Krein, Michael P.
  Future Medicinal Chemistry (2012), 4 (16), 2039-2047CODEN: FMCUA7; ISSN:1756-8919. (Future Science Ltd.)
  A review. Chem. and biol. network anal. has recently garnered intense interest from the perspective of drug design and discovery. While graph theoretic concepts have a long history in chem. - predating quantum mechanics - and graphical measures of chem. structures date back to the 1970s, it is only recently with the advent of public repositories of information and availability of high-throughput assays and computational resources that network anal. of large-scale chem. networks, such as protein-protein interaction networks, has become possible. Drug design and discovery are undergoing a paradigm shift, from the notion of one target, one drug' to a much more nuanced view that relies on multiple sources of information: genomic, proteomic, metabolomic and so on. This holistic view of drug design is an incredibly daunting undertaking still very much in its infancy. Here, we focus on current developments in graph- and network-centric approaches in chem. and biol. informatics, with particular ref. to applications in the fields of SAR modeling and drug design. Key insights from the past suggest a path forward via visualization and fusion of multiple sources of chem. network data.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xhs1ynsbnJ&md5=08f9925e749f8f9adb94188c25055619
14. 14
  Yim, K.; Cunningham, D. Targeted Drug Therapies and Cancer Recent Results Cancer Res. 2011, 185, 159– 171 DOI: 10.1007/978-3-642-03503-6_10
  14
  Targeted drug therapies and cancer
  Yim, K. L.; Cunningham, D.
  Recent Results in Cancer Research (2011), 185 (Inflammation and Gastrointestinal Cancers), 159-171CODEN: RRCRBU; ISSN:0080-0015. (Springer GmbH)
  A review. With the progress of research in mol. biol. and greater understanding of cell signalling systems emerge an increasing array of potential targets for the therapy of cancer. While traditional chemotherapy aims to elicit tumor cell death, it also produces undesirable side effects on physiol. proliferating cells. By isolating cell surface receptors which link specific intracellular secondary messenger pathways, researchers are increasingly able to define the biol. network which drives cellular function. Of importance are routes involved in malignant transformation, proliferation, survival and angiogenesis. Thus targeted therapy is directed to specific differential growth processes particular to malignant tumors. The principle mode of action generally involves the "lock-and-key" mechanism and identifying the "Achilles' heel" for drug action. Various targeted agents have been studied and many have translated into significant clin. benefit. This chapter will describe some examples which illustrate the role of this approach in gastrointestinal cancers.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xks1ejtbk%253D&md5=e39ff226e824264349bc0882c5ae26ea
15. 15
  Lee, S.; Park, K.; Kim, D. Building a Drug-Target Network and its Applications Expert Opin. Drug Discovery 2009, 4, 1177– 1189 DOI: 10.1517/17460440903322234
  There is no corresponding record for this reference.
16. 16
  Riccione, K. A.; Smith, R. P.; Lee, A. J.; You, L. A Synthetic Biology Approach to Understanding Cellular Information Processing ACS Synth. Biol. 2012, 1, 389– 402 DOI: 10.1021/sb300044r
  16
  A Synthetic Biology Approach to Understanding Cellular Information Processing
  Riccione, Katherine A.; Smith, Robert P.; Lee, Anna J.; You, Lingchong
  ACS Synthetic Biology (2012), 1 (9), 389-402CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
  A review. The survival of cells and organisms requires proper responses to environmental signals. These responses are governed by cellular networks, which serve to process diverse environmental cues. Biol. networks often contain recurring network topologies called 'motifs'. It has been recognized that the study of such motifs allows one to predict the response of a biol. network and thus cellular behavior. However, studying a single motif in complete isolation of all other network motifs in a natural setting is difficult. Synthetic biol. has emerged as a powerful approach to understanding the dynamic properties of network motifs. In addn. to testing existing theor. predictions, construction and anal. of synthetic gene circuits has led to the discovery of novel motif dynamics, such as how the combination of simple motifs can lead to autonomous dynamics or how noise in transcription and translation can affect the dynamics of a motif. Here, we review developments in synthetic biol. as they pertain to increasing our understanding of cellular information processing. We highlight several types of dynamic behaviors that diverse motifs can generate, including the control of input/output responses, the generation of autonomous spatial and temporal dynamics, as well as the influence of noise in motif dynamics and cellular behavior.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XnsFOmtbs%253D&md5=9f33791e17b6f435fbecba5e8959c7a3
17. 17
  Das, A.; Gur, M.; Cheng, M. H.; Jo, S.; Bahar, I.; Roux, B. Exploring the Conformational Transitions of Biomolecular Systems using a Simple Two-State Anisotropic Network Model PLoS Comput. Biol. 2014, 10, e1003521 DOI: 10.1371/journal.pcbi.1003521
  17
  Exploring the conformational transitions of biomolecular systems using a simple two-state anisotropic network model
  Das, Avisek; Gur, Mert; Cheng, Mary Hongying; Jo, Sunhwan; Bahar, Ivet; Roux, Benoit
  PLoS Computational Biology (2014), 10 (4), e1003521/1-e1003521/17, 17 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
  Biomol. conformational transitions are essential to biol. functions. Most exptl. methods report on the long-lived functional states of biomols., but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect exptl. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed exptl. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a phys. reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the exptl. structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the min. energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biol. interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom mol. dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides exptl. testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVGlsrfP&md5=28bb6ca702c66f6910e2472fcf2c6577
18. 18
  Chennubhotla, C.; Bahar, I. Signal Propagation in Proteins and Relation to Equilibrium Fluctuations PLoS Comput. Biol. 2007, 3, 1716– 1726 DOI: 10.1371/journal.pcbi.0030172
  18
  Signal propagation in proteins and relation to equilibrium fluctuations
  Chennubhotla, Chakra; Bahar, Ivet
  PLoS Computational Biology (2007), 3 (9), 1716-1726CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
  Elastic network (EN) models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biol. function. We posit that equil. motions also det. communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the no. of steps it takes on an av. to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topol. basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have phys. origins directly relevant to the equil. fluctuations of residues predicted by EN models.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtFKmtrvP&md5=567ec83c398eb428a88f9b1db83b382e
19. 19
  Maragakis, P.; Karplus, M. Large Amplitude Conformational Change in Proteins Explored with a Plastic Network Model: Adenylate Kinase J. Mol. Biol. 2005, 352, 807– 822 DOI: 10.1016/j.jmb.2005.07.031
  19
  Large Amplitude Conformational Change in Proteins Explored with a Plastic Network Model: Adenylate Kinase
  Maragakis, Paul; Karplus, Martin
  Journal of Molecular Biology (2005), 352 (4), 807-822CODEN: JMOBAK; ISSN:0022-2836. (Elsevier B.V.)
  The plastic network model (PNM) is used to generate a conformational change pathway for Escherichia coli adenylate kinase based on two crystal structures, namely that of an open and a closed conformer. In this model, the energy basins corresponding to known conformers are connected at their lowest common energies. The results are used to evaluate and analyze the minimal energy pathways between these basins. The open to closed transition anal. provides an identification of hinges that is in agreement with the existing definitions based on the available X-ray structures. The elastic energy distribution and the Cα pseudo-dihedral variation provide similar information on these hinges. The ensemble of the 45 published structures for this protein and closely related proteins is shown to always be within 3.0 Å of the pathway, which corresponds to a conformational change between two end structures that differ by a Cα-atom root-mean-squared deviation of 7.1 Å.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhtVWit7jL&md5=377c8baebdf4a43fb7cdf25d403b6d3b
20. 20
  Krivov, S. V.; Karplus, M. Hidden Complexity of Free Energy Surfaces for Peptide (Protein) Folding Proc. Natl. Acad. Sci. U. S. A. 2004, 101, 14766– 14770 DOI: 10.1073/pnas.0406234101
  20
  Hidden complexity of free energy surfaces for peptide (protein) folding
  Krivov, Sergei V.; Karplus, Martin
  Proceedings of the National Academy of Sciences of the United States of America (2004), 101 (41), 14766-14770CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  An understanding of the thermodn. and kinetics of protein folding requires a knowledge of the free energy surface governing the motion of the polypeptide chain. Because of the many degrees of freedom involved, surfaces projected on only 1 or 2 progress variables are generally used in descriptions of the folding reaction. Such projections result in relatively smooth surfaces, but they could mask the complexity of the unprojected surface. Here, the authors introduce an approach to det. the actual (unprojected) free energy surface and apply it to a 16-residue peptide, the 2nd β-hairpin of streptococcal protein G, which has been used as a model system for protein folding. The surface was represented by a disconnectivity graph calcd. from a long equil. folding-unfolding trajectory. The denatured state was found to have multiple low free energy basins. Nevertheless, the peptide showed exponential kinetics in folding to the native basin. Projected surfaces obtained from the present anal. had a simple form in agreement with other studies of the β-hairpin. The hidden complexity found for the β-hairpin surface suggested that the std. funnel picture of protein folding should be revisited.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXovVyjurY%253D&md5=f41f0ebbae3b7e82a6ddba894df4e3f6
21. 21
  Yin, Y.; Maisuradze, G.; Liwo, A.; Scheraga, H. Hidden Protein Folding Pathways in Free-Energy Landscapes Uncovered by Network Analysis J. Chem. Theory Comput. 2012, 8, 1176– 1189 DOI: 10.1021/ct200806n
  21
  Hidden protein folding pathways in free-energy landscapes uncovered by network analysis
  Yin, Yanping; Maisuradze, Gia G.; Liwo, Adam; Scheraga, Harold A.
  Journal of Chemical Theory and Computation (2012), 8 (4), 1176-1189CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  Network anal. is used to uncover hidden folding pathways in free energy landscapes usually defined in terms of such arbitrary order parameters as root-mean-square deviation from the native structure, radius of gyration, etc. The anal. was applied to mol. dynamics trajectories of the B-domain of staphylococcal protein A, generated with the coarse-grained united-residue force field in a broad range of temps. (270 K ≤ T ≤ 325 K). Thousands of folding pathways were identified at each temp. Out of these many folding pathways, several most probable ones were selected for investigation of the conformational transitions during protein folding. Unlike other conformational space network (CSN) methods, a node in the CSN variant implemented in this work was defined according to the native-likeness class of the structure, which defined the similarity of segments of the compared structures in terms of secondary structure, contact pattern, and local geometry as well as the overall geometric similarity of the conformation under consideration to that of the ref. (exptl.) structure. The authors' previous findings, regarding the folding model and conformations found at the folding-transition temp. for protein A, were confirmed by the conformational space network anal. In the methodol. and the anal. of the results, the shortest path identified by using the shortest-path algorithm corresponded to the most probable folding pathway in the conformational space network.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XjtVCrtrw%253D&md5=1d7189ca035a5582ce13db6b9afd9fc5
22. 22
  Golas, E.; Czaplewski, C.; Scheraga, H.; Liwo, A. Common functionally Important Motions of the Nucleotide-binding Domain of Hsp70 Proteins: Struct., Funct., Genet. 2015, 83, 282– 299 DOI: 10.1002/prot.24731
  There is no corresponding record for this reference.
23. 23
  Porras, P.; Duesbury, M.; Fabregat, A.; Ueffing, M.; Orchard, S.; Gloeckner, C. J.; Hermjakob, H. A Visual Review of the Interactome of LRRK2: Using Deep-Curated Molecular Interaction Data to Represent Biology Proteomics 2015, 15, 1390– 1404 DOI: 10.1002/pmic.201400390
  There is no corresponding record for this reference.
24. 24
  Kohonen, T. Self-Organized Formation of Topologically Correct Feature Maps Biol. Cybern. 1982, 43, 59– 69 DOI: 10.1007/BF00337288
  There is no corresponding record for this reference.
25. 25
  Blondel, V. D.; Guillaume, J.-L.; Lambiotte, R.; Lefebvre, E. Fast Unfolding of Communities in Large Networks J. Stat. Mech.: Theory Exp. 2008, 2008, P10008 DOI: 10.1088/1742-5468/2008/10/P10008
  There is no corresponding record for this reference.
26. 26
  Sethi, A.; Eargle, J.; Black, A. A.; Luthey-Schulten, Z. Dynamical Networks in tRNA: Protein Complexes Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 6620– 6625 DOI: 10.1073/pnas.0810961106
  26
  Dynamical networks in tRNA:protein complexes
  Sethi, Anurag; Eargle, John; Black, Alexis A.; Luthey-Schulten, Zaida
  Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (16), 6620-6625CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  Community network anal. derived from mol. dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNAGlu and an archaeal leucyl-tRNA synthetase (LeuRS):tRNALeu complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the phys. network whose overall topol. is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller no. of crit. edges or interactions. Consistent with this anal., there are a large no. of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXlsVOlu7o%253D&md5=5e3cfd80bf85ffe46c9c84960d176713
27. 27
  Fuglestad, B.; Gasper, P. M.; McCammon, J. A.; Markwick, P. R.; Komives, E. A. Correlated Motions and Residual Frustration in Thrombin J. Phys. Chem. B 2013, 117, 12857– 12863 DOI: 10.1021/jp402107u
  27
  Correlated Motions and Residual Frustration in Thrombin
  Fuglestad, Brian; Gasper, Paul M.; McCammon, J. Andrew; Markwick, Phineus R. L.; Komives, Elizabeth A.
  Journal of Physical Chemistry B (2013), 117 (42), 12857-12863CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
  Thrombin is the central protease in the cascade of blood coagulation proteases. The structure of thrombin consists of a double β-barrel core surrounded by connecting loops and helixes. Compared to chymotrypsin, thrombin has more extended loops that are thought to have arisen from insertions in the serine protease that evolved to impart greater specificity. Previous expts. showed thermodn. coupling between ligand binding at the active site and distal exosites. We present a combined approach of mol. dynamics (MD), accelerated mol. dynamics (AMD), and anal. of the residual local frustration of apo-thrombin and active-site-bound (PPACK-thrombin). Community anal. of the MD ensembles identified changes upon active site occupation in groups of residues linked through correlated motions and phys. contacts. AMD simulations, calibrated on measured residual dipolar couplings, reveal that upon active site ligation, correlated loop motions are quenched, but new ones connecting the active site with distal sites where allosteric regulators bind emerge. Residual local frustration anal. reveals a striking correlation between frustrated contacts and regions undergoing slow time scale dynamics. The results elucidate a motional network that probably evolved through retention of frustrated contacts to provide facile conversion between ensembles of states.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXms1Orur0%253D&md5=3ac270d27e438f468ead190ebfdf058b
28. 28
  Breiman, L. Random Forests Mach. Learn. 2001, 45, 5– 32 DOI: 10.1023/A:1010933404324
  There is no corresponding record for this reference.
29. 29
  Maragliano, L.; Vanden-Eijnden, E. A Temperature Accelerated Method for sampling Free Energy and determining Reaction Pathways in Rare Events Simulations Chem. Phys. Lett. 2006, 426, 168– 175 DOI: 10.1016/j.cplett.2006.05.062
  29
  A temperature accelerated method for sampling free energy and determining reaction pathways in rare events simulations
  Maragliano, Luca; Vanden-Eijnden, Eric
  Chemical Physics Letters (2006), 426 (1-3), 168-175CODEN: CHPLBC; ISSN:0009-2614. (Elsevier B.V.)
  A method for sampling efficiently the free energy landscape of a complex system with respect to some given collective variables is proposed. Inspired by metadynamics [A. Laio, M. Parrinello, Proc. Nat. Acad. Sci. USA 99 (2002) 12562], we introduce an extended system where the collective variables are treated as dynamical ones and show that this allows to sample the free energy landscape of these variables directly. The sampling is accelerated by using an artificially high temp. for the collective variables. The validity of the method is established using general results for systems with multiple time-scales, and its numerical efficiency is also discussed via error anal. We also show how the method can be modified in order to sample the reactive pathways in free energy space, and thereby analyze the mechanism of a reaction. Finally, we discuss how the method can be generalized and used as an alternative to the Kirkwood generalized thermodn. integration approach for the calcn. of free energy differences.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XmslGns7c%253D&md5=791e4865671fb36662b57f2342f8d95e
30. 30
  Maragliano, L.; Cottone, G.; Ciccotti, G.; Vanden-Eijnden, E. Mapping the Network of Pathways of CO Diffusion in Myoglobin J. Am. Chem. Soc. 2010, 132, 1010– 1017 DOI: 10.1021/ja905671x
  30
  Mapping the Network of Pathways of CO Diffusion in Myoglobin
  Maragliano, Luca; Cottone, Grazia; Ciccotti, Giovanni; Vanden-Eijnden, Eric
  Journal of the American Chemical Society (2010), 132 (3), 1010-1017CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  The pathways of diffusion of a CO mol. inside a myoglobin protein and toward the solvent are investigated. Specifically, the three-dimensional potential of mean force (PMF or free energy) of the CO mol. position inside the protein is calcd. by using the single-sweep method in concert with fully resolved atomistic simulations in explicit solvent. The results are interpreted under the assumption that the diffusion of the ligand can be modeled as a navigation on the PMF in which the ligand hops between the PMF local min. following the min. free energy paths (MFEPs) with rates set by the free energy barriers that need to be crossed. Here, all the local min. of the PMF, the MFEPs, and the barriers along them are calcd. The positions of the local min. are in good agreement with all the known binding cavities inside the protein, which indicates that these cavities may indeed serve as dynamical traps inside the protein and thereby influence the binding process. In addn., the MFEPs connecting the local PMF min. show a complicated network of possible pathways of exit of the dissocd. CO starting from the primary docking site, in which the histidine gate is the closest exit from the binding site for the ligand but it is not the only possible one.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhs1alur%252FE&md5=30bc6f41e344a03db97a23a87267d6c7
31. 31
  Abrams, C.; Vanden-Eijnden, E. Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 4961– 4966 DOI: 10.1073/pnas.0914540107
  31
  Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics
  Abrams, Cameron F.; Vanden-Eijnden, Eric
  Proceedings of the National Academy of Sciences of the United States of America (2010), 107 (11), 4961-4966, S4961/1-S4961/8CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  We show how to apply the method of temp.-accelerated mol. dynamics (TAMD) in collective variables to sample the conformational space of multidomain proteins in all-atom, explicitly solvated mol. dynamics simulations. The method allows the system to hyperthermally explore the free-energy surface in a set of collective variables computed at the phys. temp. As collective variables, we pick Cartesian coordinates of centers of contiguous subdomains. The method is applied to the GroEL subunit, a 55-kDa, three-domain protein, and HIV-1 gp120. For GroEL, the method induces in about 40 ns conformational changes that recapitulate the t → r'' transition and are not obsd. in unaccelerated mol. dynamics: The apical domain is displaced by 30 Å, with a twist of 90° relative to the equatorial domain, and the root-mean-squared deviation relative to the r'' conformer is reduced from 13 to 5 Å, representing fairly high predictive capability. For gp120, the method predicts both counter-rotation of inner and outer domains and disruption of the so-called bridging sheet. In particular, TAMD on gp120 initially in the CD4-bound conformation visits conformations that deviate by 3.6 Å from the gp120 conformer in complex with antibody F105, again reflecting good predictive capability. TAMD generates plausible all-atom models of the so-far structurally uncharacterized unliganded conformation of HIV-1 gp120, which may prove useful in the development of inhibitors and immunogens. The fictitious temp. employed also gives a rough est. of 10 kcal/mol for the free-energy barrier between conformers in both cases.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjvFGntrk%253D&md5=3217f5804cac528e40747cf6d236529c
32. 32
  Vashisth, H.; Maragliano, L.; Abrams, C. ”DFG-flip” in the Insulin Receptor Kinase is facilitated by a Helical Intermediate State of the Activation Loop Biophys. J. 2012, 102, 1979– 1987 DOI: 10.1016/j.bpj.2012.03.031
  32
  "DFG-Flip" in the Insulin Receptor Kinase Is Facilitated by a Helical Intermediate State of the Activation Loop
  Vashisth, Harish; Maragliano, Luca; Abrams, Cameron F.
  Biophysical Journal (2012), 102 (8), 1979-1987CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
  We have characterized a large-scale inactive-to-active conformational change in the activation-loop of the insulin receptor kinase domain at the atomistic level via untargeted temp.-accelerated mol. dynamics (TAMD) and free-energy calcns. using the string method. TAMD simulations consistently show folding of the A-loop into a helical conformation followed by unfolding to an active conformation, causing the highly conserved DFG-motif (Asp1150, Phe1151, and Gly1152) to switch from the inactive "D-out/F-in" to the nucleotide-binding-competent "D-in/F-out" conformation. The min. free-energy path computed from the string method preserves these helical intermediates along the inactive-to-active path, and the thermodn. free-energy differences are consistent with previous work on various other kinases. The mechanisms revealed by TAMD also suggest that the regulatory spine can be dynamically assembled/disassembled either by DFG-flip or by movement of the αC-helix. Together, these findings both broaden our understanding of kinase activation and point to intermediates as specific therapeutic targets.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XmtVeku7g%253D&md5=153899028b209aeec2a05274081e557e
33. 33
  Vashisth, H.; Brooks, C. Conformational Sampling of Maltose-transporter Components in Cartesian Collective Variables is governed by the Low-frequency Normal Modes J. Phys. Chem. Lett. 2012, 3, 3379– 3384 DOI: 10.1021/jz301650q
  33
  Conformational Sampling of Maltose-Transporter Components in Cartesian Collective Variables Is Governed by the Low-Frequency Normal Modes
  Vashisth, H.; Brooks, C. L., III
  Journal of Physical Chemistry Letters (2012), 3 (22), 3379-3384CODEN: JPCLCD; ISSN:1948-7185. (American Chemical Society)
  We have studied large-scale conformational transitions in the maltose-binding protein and the nucleotide binding domains of a maltose-transporter using enhanced conformational sampling in Cartesian collective variables with temp.-accelerated mol. dynamics (TAMD) and Cα-based elastic network normal-mode anal. Significantly, every functional displacement in the TAMD-generated pathways of each protein could be rationalized via a single low-frequency soft mode, whereas a combination of two to three low-frequency modes was found to describe the entire conformational change, suggesting that collective functional movement in TAMD trajectories is facilitated by the intrinsically accessible low-frequency normal modes. By applying a harmonic potential to facilitate functional motion in TAMD simulations, we also provide a recipe to reproducibly generate structural transitions in both proteins, which can be used to characterize large-scale conformational changes in other biomols.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsF2ktb7P&md5=79e81d0f24f7eadea7dfcb1e7dd45c4f
34. 34
  Nygaard, R.; Zou, Y.; Dror, R.; Mildorf, T.; Arlow, D.; Manglik, A.; Pan, A.; Liu, C.; Fung, J.; Bokoch, M.; Thian, F.; Kobilka, T.; Shaw, D.; Mueller, L.; Prosser, R.; Kobilka, B. The Dynamic Process of β(2)-adrenergic Receptor Activation Cell 2013, 152, 532– 542 DOI: 10.1016/j.cell.2013.01.008
  34
  The Dynamic Process of β2-Adrenergic Receptor Activation
  Nygaard, Rie; Zou, Yaozhong; Dror, Ron O.; Mildorf, Thomas J.; Arlow, Daniel H.; Manglik, Aashish; Pan, Albert C.; Liu, Corey W.; Fung, Juan Jose; Bokoch, Michael P.; Thian, Foon Sun; Kobilka, Tong Sun; Shaw, David E.; Mueller, Luciano; Prosser, R. Scott; Kobilka, Brian K.
  Cell (Cambridge, MA, United States) (2013), 152 (3), 532-542CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
  G-protein-coupled receptors (GPCRs) can modulate diverse signaling pathways, often in a ligand-specific manner. The full range of functionally relevant GPCR conformations is poorly understood. Here, the authors use NMR spectroscopy to characterize the conformational dynamics of the transmembrane core of the β2-adrenergic receptor (β2AR), a prototypical GPCR. The authors labeled β2AR with 13CH3ε-methionine and obtained HSQC spectra of unliganded receptor as well as receptor bound to an inverse agonist, an agonist, and a G-protein-mimetic nanobody. These studies provide evidence for conformational states not obsd. in crystal structures, as well as substantial conformational heterogeneity in agonist- and inverse-agonist-bound prepns. They also show that for β2AR, unlike rhodopsin, an agonist alone does not stabilize a fully active conformation, suggesting that the conformational link between the agonist-binding pocket and the G-protein-coupling surface is not rigid. The obsd. heterogeneity may be important for β2AR's ability to engage multiple signaling and regulatory proteins.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvFaiu7s%253D&md5=8f9aaa581027657b166b92ef617e950d
35. 35
  Lapelosa, M.; Abrams, C. A Computational Study of Water and CO Migration Sites and Channels Inside Myoglobin J. Chem. Theory Comput. 2013, 9, 1265– 1271 DOI: 10.1021/ct300862j
  35
  A Computational Study of Water and CO Migration Sites and Channels Inside Myoglobin
  Lapelosa, Mauro; Abrams, Cameron F.
  Journal of Chemical Theory and Computation (2013), 9 (2), 1265-1271CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  Pathways are computed for transport of H2O and CO in myoglobin (Mb), using the single sweep and zero-temp. string methods in a fully atomistic, explicitly solvated model system. Our predictions of sites and barriers in the pathways for CO transport agree with previous studies. For H2O, we predict a binding site in the distal pocket (DP), in agreement with crystallog. observations, and another one close to Leu 29, which explains the importance of this residue in controlling the pocket's hydrophobicity, as well as disordered min. in the largely apolar xenon cavities. In particular, H2O can occupy and transition among the xenon cavities, Xe4, Xe2, and Xe3. Our results support the hypothesis that the thermodynamically most favorable entry/exit portal for H2O is the so-called histidine gate (HG), the same as for CO. This result, along with the observation of water occupation of both DP and apolar Xe cavities, suggest that water and small gas mols. like CO compete for access to the protein interior, and therefore models of gas mol. transport within proteins should also explicitly consider water transport.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFWlu74%253D&md5=329f0660b726da6505fda270a01c8883
36. 36
  Vashisth, H.; Abrams, C. All-atom Structural Models of Insulin Binding to the Insulin Receptor in the presence of a Tandem Hormone-binding Element Proteins: Struct., Funct., Genet. 2013, 81, 1017– 1030 DOI: 10.1002/prot.24255
  36
  All-atom structural models of insulin binding to the insulin receptor in the presence of a tandem hormone-binding element
  Vashisth, Harish; Abrams, Cameron F.
  Proteins: Structure, Function, and Bioinformatics (2013), 81 (6), 1017-1030CODEN: PSFBAF ISSN:. (Wiley-Blackwell)
  Insulin regulates blood glucose levels in higher organisms by binding to and activating insulin receptor (IR), a constitutively homodimeric glycoprotein of the receptor tyrosine kinase (RTK) superfamily. Therapeutic efforts in treating diabetes have been significantly impeded by the absence of structural information on the activated form of the insulin/IR complex. Mutagenesis and photo-crosslinking expts. and structural information on insulin and apo-IR strongly suggest that the dual-chain insulin mol., unlike the related single-chain insulin-like growth factors, binds to IR in a very different conformation than what is displayed in storage forms of the hormone. In particular, hydrophobic residues buried in the core of the folded insulin mol. engage the receptor. There is also the possibility of plasticity in the receptor structure based on these data, which may in part be due to rearrangement of the so-called CT-peptide, a tandem hormone-binding element of IR. These possibilities provide opportunity for large-scale mol. modeling to contribute to our understanding of this system. Using various atomistic simulation approaches, we have constructed all-atom structural models of hormone/receptor complexes in the presence of CT in its crystallog. position and a thermodynamically favorable displaced position. In the "displaced-CT" complex, many more insulin-receptor contacts suggested by expts. are satisfied, and our simulations also suggest that R-insulin potentially represents the receptor-bound form of hormone. The results presented in this work have further implications for the design of receptor-specific agonists/antagonists.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjtFWgtbY%253D&md5=b580372c187c210ba1dc24050fbeaf86
37. 37
  Scarpazza, D.; Ierardi, D.; Lerer, A.; Mackenzie, K.; Pan, A.; Bank, J. A.; Chow, E.; Dror, R.; Grossman, J.; Killebrew, D.; Moraes, M.; Predescu, C.; Salmon, J.; Shaw, D. Extending the Generality of Molecular Dynamics Simulations on a Special-Purpose Machine. In Proceedings of the 27th IEEE International Parallel and Distributed Processing Symposium, 2013; pp 933– 945 DOI: 10.1109/IPDPS.2013.93 .
  There is no corresponding record for this reference.
38. 38
  Vashisth, H.; Storaska, A.; Neubig, R.; Brooks, C. Conformational Dynamics of a Regulator of G-protein Signaling Protein reveals a Mechanism of Allosteric Inhibition by a small Molecule ACS Chem. Biol. 2013, 8, 2778– 2784 DOI: 10.1021/cb400568g
  There is no corresponding record for this reference.
39. 39
  Selwa, E.; Huynh, T.; Ciccotti, G.; Maragliano, L.; Malliavin, T. Temperature-accelerated Molecular Dynamics gives Insights into Globular Conformations Sampled in the Free State of the AC Catalytic Domain Proteins: Struct., Funct., Genet. 2014, 82, 2483– 2496 DOI: 10.1002/prot.24612
  There is no corresponding record for this reference.
40. 40
  Hosseini-Naveh, Z. M.; Malliavin, T.; Maragliano, L.; Cottone, G.; Ciccotti, G. Conformational changes in Acetylcholine binding protein Investigated by Temperature accelerated Molecular Dynamics PLoS One 2014, 9, e88555 DOI: 10.1371/journal.pone.0088555
  There is no corresponding record for this reference.
41. 41
  Cortes-Ciriano, I.; Bouvier, G.; Nilges, M.; Maragliano, L.; Malliavin, T. Temperature Accelerated Molecular Dynamics with Soft-ratcheting Criterion orients Enhanced Sampling by Low-resolution Information J. Chem. Theory Comput. 2015, 11, 3446– 3454 DOI: 10.1021/acs.jctc.5b00153
  41
  Temperature Accelerated Molecular Dynamics with Soft-Ratcheting Criterion Orients Enhanced Sampling by Low-Resolution Information
  Cortes-Ciriano, Isidro; Bouvier, Guillaume; Nilges, Michael; Maragliano, Luca; Malliavin, Therese E.
  Journal of Chemical Theory and Computation (2015), 11 (7), 3446-3454CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  Many proteins exhibit an equil. between multiple conformations, some of them being characterized only by low-resoln. information. Visiting all conformations is a demanding task for computational techniques performing enhanced but unfocused exploration of collective variable (CV) space. Otherwise, pulling a structure toward a target condition biases the exploration in a way difficult to assess. To address this problem, we introduce here the soft-ratcheting temp.-accelerated mol. dynamics (sr-TAMD), where the exploration of CV space by TAMD is coupled to a soft-ratcheting algorithm that filters the evolving CV values according to a predefined criterion. Any low resoln. or even qual. information can be used to orient the exploration. We validate this technique by exploring the conformational space of the inactive state of the catalytic domain of the adenyl cyclase AC from Bordetella pertussis. The domain AC gets activated by assocn. with calmodulin (CaM), and the available crystal structure shows that in the complex the protein has an elongated shape. High-resoln. data are not available for the inactive, CaM-free protein state, but hydrodynamic measurements have shown that the inactive AC displays a more globular conformation. Here, using as CVs several geometric centers, we use sr-TAMD to enhance CV space sampling while filtering for CV values that correspond to centers moving close to each other, and we thus rapidly visit regions of conformational space that correspond to globular structures. The set of conformations sampled using sr-TAMD provides the most extensive description of the inactive state of AC up to now, consistent with available exptl. information.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXoslyltro%253D&md5=80c851ca909c95b4e7a7c2342d6185df
42. 42
  Arthur, M.; Reynolds, P.; Courvalin, P. Glycopeptide Resistance in Enterococci Trends Microbiol. 1996, 4, 401– 407 DOI: 10.1016/0966-842X(96)10063-9
  42
  Glycopeptide resistance in enterococci
  Arthur M; Reynolds P; Courvalin P
  Trends in microbiology (1996), 4 (10), 401-7 ISSN:0966-842X.
  Glycopeptide resistance in enterococci results from the production of peptidoglycan precursors with low affinity for these antibiotics. The mobility of the resistance genes by transposition and conjugation and the ability of the resistance proteins to interfere with synthesis of normal precursors in different hosts indicate that dissemination into other bacterial species should be anticipated.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaK2s%252FlsFKkuw%253D%253D&md5=890e80ff1e64c3cd6af936366cccf900
43. 43
  Courvalin, P. Vancomycin Resistance in Gram-Positive Cocci Clin. Infect. Dis. 2006, 42, 25– 34 DOI: 10.1086/491711
  43
  Vancomycin resistance in Gram-positive cocci
  Courvalin, Patrice
  Clinical Infectious Diseases (2006), 42 (Suppl. 1), S25-S34CODEN: CIDIEL; ISSN:1058-4838. (University of Chicago Press)
  A review. The first vancomycin-resistant clin. isolates of Enterococcus species were reported in Europe in 1988. Similar strains were later detected in hospitals on the East Coast of the United States. Since then, vancomycin-resistant enterococci have spread with unexpected rapidity and are now encountered in hospitals in most countries. This article reviews the mode of action and the mechanism of bacterial resistance to glycopeptides, as exemplified by the VanA type, which is mediated by transposon Tn1546 and is widely spread in enterococci. The diversity, regulation, evolution, and recent dissemination of methicillin-resistant Staphylococcus aureus are then discussed.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XmsVaguw%253D%253D&md5=4f74a113a99ebfc1bb96accefc299159
44. 44
  Arthur, M.; Molinas, C.; Bugg, T.; Wright, G.; Walsh, C.; Courvalin, P. Evidence for in vivo Incorporation of d-lactate into Peptidoglycan Precursors of Vancomycin-Resistant Enterococci Antimicrob. Agents Chemother. 1992, 36, 867– 869 DOI: 10.1128/AAC.36.4.867
  44
  Evidence for in vivo incorporation of D-lactate into peptidoglycan precursors of vancomycin-resistant enterococci
  Arthur, Michel; Molinas, Catherine; Bugg, Timothy D. H.; Wright, Gerard D.; Walsh, Christopher T.; Courvalin, Patrice
  Antimicrobial Agents and Chemotherapy (1992), 36 (4), 867-9CODEN: AMACCQ; ISSN:0066-4804.
  The VanA ligase encoded by the vancomycin resistance plasmid pIP816 of Enterococcus faecium BM4147 condenses D-alanine with various D-2-hydroxy and D-2-amino acids in vitro. D-Lactate added to the culture medium restored the vancomycin resistance of a strain that does not produce the VanH dehydrogenase and therefore appears to be a substrate of VanA in vivo.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38XitFWjtrk%253D&md5=5c71e639085a7265af95f0eccc42fdc0
45. 45
  Roper, D.; Huyton, T.; Vagin, A.; Dodson, G. The Molecular Basis of Vancomycin Resistance in clinically relevant Enterococci: Crystal Structure of d-alanyl–d-lactate Ligase (VanA) Proc. Natl. Acad. Sci. U. S. A. 2000, 97, 8921– 8925 DOI: 10.1073/pnas.150116497
  There is no corresponding record for this reference.
46. 46
  Bouvier, G.; Duclert-Savatier, N.; Desdouits, N.; Meziane-Cherif, D.; Blondel, A.; Courvalin, P.; Nilges, M.; Malliavin, T. E. Functional Motions Modulating VanA Ligand Binding unraveled by Self-organizing maps J. Chem. Inf. Model. 2014, 54, 289– 301 DOI: 10.1021/ci400354b
  46
  Functional Motions Modulating VanA Ligand Binding Unraveled by Self-Organizing Maps
  Bouvier, Guillaume; Duclert-Savatier, Nathalie; Desdouits, Nathan; Meziane-Cherif, Djalal; Blondel, Arnaud; Courvalin, Patrice; Nilges, Michael; Malliavin, Therese E.
  Journal of Chemical Information and Modeling (2014), 54 (1), 289-301CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  The VanA D-Ala:D-Lac ligase is a key enzyme in the emergence of high level resistance to vancomycin in Enterococcus species and methicillin-resistant Staphylococcus aureus (MRSA). It catalyzes the formation of D-Ala-D-Lac instead of the vancomycin target, D-Ala-D-Ala, leading to the prodn. of modified, low vancomycin binding affinity peptidoglycan precursors. Therefore, VanA appears to be an attractive target for the design of new antibacterials to overcome resistance. The catalytic site of VanA is delimited by three domains and closed by an ω-loop upon enzymic reaction. The aim of the present work was (i) to investigate the conformational transition of VanA assocd. with the opening of its ω-loop and of a part of its central domain, and (ii) to relate this transition with the substrate or product binding propensities. Mol. dynamics trajectories of the VanA ligase of Enterococcus faecium with or without a disulfide bridge distant from the catalytic site revealed differences in the catalytic site conformations with a slight opening. Conformations were clustered with an original machine learning method, based on self-organizing maps (SOM), which revealed four distinct conformational basins. Several ligands related to substrates, intermediates, or products were docked to SOM representative conformations with the DOCK 6.5 program. Classification of ligand docking poses, also performed with SOM, clearly distinguished ligand functional classes: substrates, reaction intermediates, and product. This result illustrates the acuity of the SOM classification and supports the quality of the DOCK program poses. The protein-ligand interaction features for the different classes of poses will guide the search and design of novel inhibitors.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXktVOlsQ%253D%253D&md5=df3e503d422f22b5056948b748a9d256
47. 47
  Kitamura, Y.; Ebihara, A.; Agari, Y.; Shinkai, A.; Hirotsu, K.; Kuramitsu, S. Structure of d-alanine–d-alanine Ligase from Thermus Thermophilus HB8: Cumulative Conformational Change and Enzyme-ligand Interactions Acta Crystallogr., Sect. D: Biol. Crystallogr. 2009, 65, 1098– 1106 DOI: 10.1107/S0907444909029710
  There is no corresponding record for this reference.
48. 48
  MacKerell, A.; Bashford, D.; Bellott, M.; Dunbrack, R.; Evanseck, J.; Field, M.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D.; Prodhom, B.; Reiher, W.; Roux, B.; Schlenkrich, M.; Smith, J.; Stote, R.; Straub, J.; Watanabe, M.; Wiórkiewicz-Kuczera, J.; Yin, D.; Karplus, M. All-atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins J. Phys. Chem. B 1998, 102, 3586– 3616 DOI: 10.1021/jp973084f
  48
  All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
  MacKerell, A. D., Jr.; Bashford, D.; Bellott, M.; Dunbrack, R. L.; Evanseck, J. D.; Field, M. J.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F. T. K.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D. T.; Prodhom, B.; Reiher, W. E., III; Roux, B.; Schlenkrich, M.; Smith, J. C.; Stote, R.; Straub, J.; Watanabe, M.; Wiorkiewicz-Kuczera, J.; Yin, D.; Karplus, M.
  Journal of Physical Chemistry B (1998), 102 (18), 3586-3616CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)
  New protein parameters are reported for the all-atom empirical energy function in the CHARMM program. The parameter evaluation was based on a self-consistent approach designed to achieve a balance between the internal (bonding) and interaction (nonbonding) terms of the force field and among the solvent-solvent, solvent-solute, and solute-solute interactions. Optimization of the internal parameters used exptl. gas-phase geometries, vibrational spectra, and torsional energy surfaces supplemented with ab initio results. The peptide backbone bonding parameters were optimized with respect to data for N-methylacetamide and the alanine dipeptide. The interaction parameters, particularly the at. charges, were detd. by fitting ab initio interaction energies and geometries of complexes between water and model compds. that represented the backbone and the various side chains. In addn., dipole moments, exptl. heats and free energies of vaporization, solvation and sublimation, mol. vols., and crystal pressures and structures were used in the optimization. The resulting protein parameters were tested by applying them to noncyclic tripeptide crystals, cyclic peptide crystals, and the proteins crambin, bovine pancreatic trypsin inhibitor, and carbonmonoxy myoglobin in vacuo and in a crystal. A detailed anal. of the relationship between the alanine dipeptide potential energy surface and calcd. protein φ, χ angles was made and used in optimizing the peptide group torsional parameters. The results demonstrate that use of ab initio structural and energetic data by themselves are not sufficient to obtain an adequate backbone representation for peptides and proteins in soln. and in crystals. Extensive comparisons between mol. dynamics simulation and exptl. data for polypeptides and proteins were performed for both structural and dynamic properties. Calcd. data from energy minimization and dynamics simulations for crystals demonstrate that the latter are needed to obtain meaningful comparisons with exptl. crystal structures. The presented parameters, in combination with the previously published CHARMM all-atom parameters for nucleic acids and lipids, provide a consistent set for condensed-phase simulations of a wide variety of mols. of biol. interest.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXivVOlsb4%253D&md5=ebb5100dafd0daeee60ca2fa66c1324a
49. 49
  MacKerell, A. D.; Feig, M.; Brooks, C. L. Extending the Treatment of Backbone Energetics in Protein Force Fields: Limitations of gas-phase Quantum Mechanics in reproducing Protein Conformational Distributions in Molecular Dynamics Simulations J. Comput. Chem. 2004, 25, 1400– 1415 DOI: 10.1002/jcc.20065
  49
  Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations
  MacKerell, Alexander D., Jr.; Feig, Michael; Brooks, Charles L., III
  Journal of Computational Chemistry (2004), 25 (11), 1400-1415CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  Computational studies of proteins based on empirical force fields represent a powerful tool to obtain structure-function relationships at an at. level, and are central in current efforts to solve the protein folding problem. The results from studies applying these tools are, however, dependent on the quality of the force fields used. In particular, accurate treatment of the peptide backbone is crucial to achieve representative conformational distributions in simulation studies. To improve the treatment of the peptide backbone, quantum mech. (QM) and mol. mech. (MM) calcns. were undertaken on the alanine, glycine, and proline dipeptides, and the results from these calcns. were combined with mol. dynamics (MD) simulations of proteins in crystal and aq. environments. QM potential energy maps of the alanine and glycine dipeptides at the LMP2/cc-pVxZ/MP2/6-31G* levels, where x = D, T, and Q, were detd., and are compared to available QM studies on these mols. The LMP2/cc pVQZ//MP2/6-31G* energy surfaces for all three dipeptides were then used to improve the MM treatment of the dipeptides. These improvements included addnl. parameter optimization via Monte Carlo simulated annealing and extension of the potential energy function to contain peptide backbone .vphi., ψ dihedral crossterms or a .vphi., ψ grid-based energy correction term. Simultaneously, MD simulations of up to seven proteins in their cryst. environments were used to validate the force field enhancements. Comparison with QM and crystallog. data showed that an addnl. optimization of the .vphi., ψ dihedral parameters along with the grid-based energy correction were required to yield significant improvements over the CHARMM22 force field. However, systematic deviations in the treatment of .vphi. and ψ in the helical and sheet regions were evident. Accordingly, empirical adjustments were made to the grid-based energy correction for alanine and glycine to account for these systematic differences. These adjustments lead to greater deviations from QM data for the two dipeptides but also yielded improved agreement with exptl. crystallog. data. These improvements enhance the quality of the CHARMM force field in treating proteins. This extension of the potential energy function is anticipated to facilitate improved treatment of biol. macromols. via MM approaches in general.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXlsVOgt7c%253D&md5=b2451bb5df548447f8b172a211bc1848
50. 50
  Jorgensen, W. Quantum and Statistical Mechanical Studies of Liquids. 10. Transferable Intermolecular Potential Functions for Water, Alcohols, and Ethers. Application to Liquid Water J. Am. Chem. Soc. 1981, 103, 335– 340 DOI: 10.1021/ja00392a016
  50
  Quantum and statistical mechanical studies of liquids. 10. Transferable intermolecular potential functions for water, alcohols, and ethers. Application to liquid water
  Jorgensen, William L.
  Journal of the American Chemical Society (1981), 103 (2), 335-40CODEN: JACSAT; ISSN:0002-7863.
  Transferable intermol. potential functions (TIPS) suitable for use in liq. simulations are reported for water, alcs., and ethers. Interaction sites are located on oxygens, hydroxyl hydrogens, and the carbons in alkyl groups. Each type of site has Coulomb and Lennard-Jones parameters chosen to yield reasonable structural and energetic results for both gas-phase dimers and pure liqs. A Monte Carlo simulation of liq. water at 25° using the TIP compares favorably with expt. or results from Clementi's CI potential except that the OO radial distribution function is calcd. to be too flat beyond the first solvent shell. Simulations of liq. methanol and ethanol have also been carried out as described in the accompanying papers. Overall, in view of the simplicity and transferability of the potentials, the initial results are most encouraging for the treatment of fluids with even more complex monomers and for extension to other types of interaction sites.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3MXotlCitA%253D%253D&md5=6160447b696ee61208228f2f542c4a74
51. 51
  Phillips, J. C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R. D.; Kale, L.; Schulten, K. Scalable Molecular Dynamics with NAMD J. Comput. Chem. 2005, 26, 1781– 1802 DOI: 10.1002/jcc.20289
  51
  Scalable molecular dynamics with NAMD
  Phillips, James C.; Braun, Rosemary; Wang, Wei; Gumbart, James; Tajkhorshid, Emad; Villa, Elizabeth; Chipot, Christophe; Skeel, Robert D.; Kale, Laxmikant; Schulten, Klaus
  Journal of Computational Chemistry (2005), 26 (16), 1781-1802CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  NAMD is a parallel mol. dynamics code designed for high-performance simulation of large biomol. systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical mol. dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temp. and pressure controls used. Features for steering the simulation across barriers and for calcg. both alchem. and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomol. system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the mol. graphics/sequence anal. software VMD and the grid computing/collab. software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXht1SlsbbJ&md5=189051128443b547f4300a1b8fb0e034
52. 52
  Nam, K.; Gao, J.; York, D. M. An efficient Linear-scaling Ewald Method for Long-range Electrostatic Interactions in Combined QM/MM Calculations J. Chem. Theory Comput. 2005, 1, 2– 13 DOI: 10.1021/ct049941i
  52
  An Efficient Linear-Scaling Ewald Method for Long-Range Electrostatic Interactions in Combined QM/MM Calculations
  Nam, Kwangho; Gao, Jiali; York, Darrin M.
  Journal of Chemical Theory and Computation (2005), 1 (1), 2-13CODEN: JCTCCE ISSN:. (American Chemical Society)
  A method is presented for the efficient evaluation of long-range electrostatic forces in combined quantum mech. and mol. mech. (QM/MM) calcns. of periodic systems. The QM/MM-Ewald method is a linear-scaling electrostatic method that utilizes the particle mesh Ewald algorithm for calcn. of point charge interactions of mol. mech. atoms and a real-space multipolar expansion for the quantum mech. electrostatic terms plus a pairwise periodic correction factor for the QM and QM/MM interactions that does not need to be re-evaluated during the SCF procedure. The method is tested in a series of mol. dynamics simulations of the ion-ion assocn. of ammonium chloride and ammonium metaphosphate and the dissociative phosphoryl transfer of Me phosphate and acetyl phosphate. Results from periodic boundary mol. dynamics (PBMD) simulations employing the QM/MM-Ewald method are compared with corresponding PBMD simulations using electrostatic cutoffs and with results from nonperiodic stochastic boundary mol. dynamics (SBMD) simulations, with cutoffs and with full electrostatics (no cutoff). The present method allows extension of linear-scaling Ewald methods to mol. simulations of enzyme and ribozyme reactions that use combined QM/MM potentials.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXhtVOjt77L&md5=5e8d612ba0724cfd8f6073e85c630fde
53. 53
  Frenkel, D.; Smit, B. Understanding Molecular Simulation: from Algorithms to Applications, Vol. 1; Academic Press: New York, 2001.
  There is no corresponding record for this reference.
54. 54
  Martyna, G. J.; Tobias, D. J.; Klein, M. L. Constant Pressure Molecular Dynamics Algorithms J. Chem. Phys. 1994, 101, 4177– 4189 DOI: 10.1063/1.467468
  54
  Constant pressure molecular dynamics algorithms
  Martyna, Glenn J.; Tobias, Douglas J.; Klein, Michael L.
  Journal of Chemical Physics (1994), 101 (5), 4177-89CODEN: JCPSA6; ISSN:0021-9606.
  Modularly invariant equations of motion are derived that generate the isothermal-isobaric ensemble as their phase space avs. Isotropic vol. fluctuations and fully flexible simulation cells as well as a hybrid scheme that naturally combines the two motions are considered. The resulting methods are tested on two problems, a particle in a one-dimensional periodic potential and a spherical model of C60 in the solid/fluid phase.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXmtFeht7o%253D&md5=c14bd79c6398b0b30541e3cbe92851b0
55. 55
  Feller, S. E.; Zhang, Y.; Pastor, R. W.; Brooks, B. R. Constant Pressure Molecular Dynamics Simulation: the Langevin Piston Method J. Chem. Phys. 1995, 103, 4613– 4621 DOI: 10.1063/1.470648
  55
  Constant pressure molecular dynamics simulation: the Langevin piston method
  Feller, Scott E.; Zhang, Yuhong; Pastor, Richard W.; Brooks, Bernard R.
  Journal of Chemical Physics (1995), 103 (11), 4613-21CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  A new method for performing mol. dynamics simulations under const. pressure is presented. In the method, which is based on the extended system formalism introduced by Andersen, the deterministic equations of motion for the piston degree of freedom are replaced by a Langevin equation; a suitable choice of collision frequency then eliminates the unphys. "ringing" of the vol. assocd. with the piston mass. In this way it is similar to the "weak coupling algorithm" developed by Berendsen and co-workers to perform mol. dynamics simulation without piston mass effects. It is shown, however, that the weak coupling algorithm induces artifacts into the simulation which can be quite severe for inhomogeneous systems such as aq. biopolymers or liq./liq. interfaces.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXotVentLo%253D&md5=219a4e0a48397a35fa2c62cf99bf225a
56. 56
  Ryckaert, J.; Ciccotti, G.; Berendsen, H. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes J. Comput. Phys. 1977, 23, 327– 341 DOI: 10.1016/0021-9991(77)90098-5
  56
  Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes
  Ryckaert, Jean Paul; Ciccotti, Giovanni; Berendsen, Herman J. C.
  Journal of Computational Physics (1977), 23 (3), 327-41CODEN: JCTPAH; ISSN:0021-9991.
  A numerical algorithm integrating the 3N Cartesian equation of motion of a system of N points subject to holonomic constraints is applied to mol. dynamics simulation of a liq. of 64 butane mols.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXktVGhsL4%253D&md5=b4aecddfde149117813a5ea4f5353ce2
57. 57
  Abrams, C. F.; Vanden-Eijnden, E. Large-scale Conformational Sampling of Proteins using Temperature-Accelerated Molecular Dynamics Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 4961– 4966 DOI: 10.1073/pnas.0914540107
  57
  Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics
  Abrams, Cameron F.; Vanden-Eijnden, Eric
  Proceedings of the National Academy of Sciences of the United States of America (2010), 107 (11), 4961-4966, S4961/1-S4961/8CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  We show how to apply the method of temp.-accelerated mol. dynamics (TAMD) in collective variables to sample the conformational space of multidomain proteins in all-atom, explicitly solvated mol. dynamics simulations. The method allows the system to hyperthermally explore the free-energy surface in a set of collective variables computed at the phys. temp. As collective variables, we pick Cartesian coordinates of centers of contiguous subdomains. The method is applied to the GroEL subunit, a 55-kDa, three-domain protein, and HIV-1 gp120. For GroEL, the method induces in about 40 ns conformational changes that recapitulate the t → r'' transition and are not obsd. in unaccelerated mol. dynamics: The apical domain is displaced by 30 Å, with a twist of 90° relative to the equatorial domain, and the root-mean-squared deviation relative to the r'' conformer is reduced from 13 to 5 Å, representing fairly high predictive capability. For gp120, the method predicts both counter-rotation of inner and outer domains and disruption of the so-called bridging sheet. In particular, TAMD on gp120 initially in the CD4-bound conformation visits conformations that deviate by 3.6 Å from the gp120 conformer in complex with antibody F105, again reflecting good predictive capability. TAMD generates plausible all-atom models of the so-far structurally uncharacterized unliganded conformation of HIV-1 gp120, which may prove useful in the development of inhibitors and immunogens. The fictitious temp. employed also gives a rough est. of 10 kcal/mol for the free-energy barrier between conformers in both cases.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjvFGntrk%253D&md5=3217f5804cac528e40747cf6d236529c
58. 58
  Vassura, M.; Margara, L.; Medri, F.; di Lena, P.; Fariselli, P.; Casadio, R. Reconstruction of 3D Structures from Protein Contact Maps. In Bioinformatics Research and Applications; Springer: Berlin, Germany, 2007; pp 578– 589.
  There is no corresponding record for this reference.
59. 59
  Girvan, M.; Newman, M. E. Community Structure in Social and Biological Networks Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 7821– 7826 DOI: 10.1073/pnas.122653799
  59
  Community structure in social and biological networks
  Girvan, M.; Newman, M. E. J.
  Proceedings of the National Academy of Sciences of the United States of America (2002), 99 (12), 7821-7826CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  A no. of recent studies have focused on the statistical properties of networked systems such as social networks and Worldwide Web. Researchers have concd. particularly on few properties that seem to be common to many networks: small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure in which network nodes are joined together in tightly knit group between which there are only looser connections. We propose method for detecting such communities, built around the idea using centrality indexes to find community boundaries. We test method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known-a collaboration network and a food web-and find that it detects significant and informative community divisions in both cases.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XkvVGjsL4%253D&md5=a0a1f47632a804f2a009425922fc8dfc
60. 60
  Kohonen, T. Self-Organizing Maps; Springer Series in Information Sciences: Heidelberg, Germany, 2001.
  There is no corresponding record for this reference.
61. 61
  Bouvier, G.; Desdouits, N.; Ferber, M.; Blondel, A.; Nilges, M. An Automatic Tool to Analyze and Cluster Macromolecular Conformations Based on Self-Organizing Maps Bioinformatics 2015, 31, 1490– 1492 DOI: 10.1093/bioinformatics/btu849
  61
  An automatic tool to analyze and cluster macromolecular conformations based on self-organizing maps
  Bouvier, Guillaume; Desdouits, Nathan; Ferber, Mathias; Blondel, Arnaud; Nilges, Michael
  Bioinformatics (2015), 31 (9), 1490-1492CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
  Motivation: Sampling the conformational space of biol. macromols. generates large sets of data with considerable complexity. Data-mining techniques, such as clustering, can ext. meaningful information. Among them, the self-organizing maps (SOMs) algorithm has shown great promise; in particular since its computation time rises only linearly with the size of the data set. Whereas SOMs are generally used with few neurons, we investigate here their behavior with large nos. of neurons. Results: We present here a python library implementing the full SOM anal. workflow. Large SOMs can readily be applied on heavy data sets. Coupled with visualization tools they have very interesting properties. Descriptors for each conformation of a trajectory are calcd. and mapped onto a 3D landscape, the U-matrix, reporting the distance between neighboring neurons. To delineate clusters, we developed the flooding algorithm, which hierarchically identifies local basins of the U-matrix from the global min. to the max.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1GntbrJ&md5=e569293d29512f33f57891989ace42a8
62. 62
  Dijkstra, E. A Note on Two Problems in Connexion with Graphs Numer. Math 1959, 1, 269– 271 DOI: 10.1007/BF01386390
  There is no corresponding record for this reference.
63. 63
  Mills, J.; Dean, P. M. Three-dimensional Hydrogen-bond Geometry and Probability Information from a Crystal Survey J. Comput.-Aided Mol. Des. 1996, 10, 607– 622 DOI: 10.1007/BF00134183
  63
  Three-dimensional hydrogen-bond geometry and probability information from a crystal survey
  Mills, J.E.J.; Dean, P.M.
  Journal of Computer-Aided Molecular Design (1996), 10 (6), 607-622CODEN: JCADEQ; ISSN:0920-654X. (ESCOM)
  An extensive crystal survey of the Cambridge Structural Database has been carried out to provide hydrogen-bond data for use in drug-design strategies. Previous crystal surveys have generated 1D frequency distributions of hydrogen-bond distances and angles, which are not sufficient to model the hydrogen bond as a ligand-receptor interaction. For each hydrogen-bonding group of interest to the drug designer, geometric hydrogen-bond criteria have been derived. The 3D distribution of complementary atoms about each hydrogen-bonding group has been ascertained by dividing the space about each group into bins of equal vol. and continuing the no. of obsd. hydrogen-bonding contacts in each bin. Finally, the propensity of each group to form a hydrogen bond has been calcd. Together, these data can be used to predict the potential site points with which a ligand could interact and therefore could be used in mol.-similarity studies, pharmacophore query searching of databases, or de novo design algorithms.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXlvVWitQ%253D%253D&md5=b533a996a701c76556cfd51d2737c820
64. 64
  Pettersen, E.; Goddard, T.; Huang, C.; Couch, G.; Greenblatt, D.; Meng, E.; Ferrin, T. UCSF Chimera—A Visualization System for Exploratory Research and Analysis J. Comput. Chem. 2004, 25, 1605– 1612 DOI: 10.1002/jcc.20084
  64
  UCSF Chimera-A visualization system for exploratory research and analysis
  Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C.; Couch, Gregory S.; Greenblatt, Daniel M.; Meng, Elaine C.; Ferrin, Thomas E.
  Journal of Computational Chemistry (2004), 25 (13), 1605-1612CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale mol. assemblies such as viral coats, and Collab., which allows researchers to share a Chimera session interactively despite being at sep. locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and assocd. structures; ViewDock, for screening docked ligand orientations; Movie, for replaying mol. dynamics trajectories; and Vol. Viewer, for display and anal. of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXmvVOhsbs%253D&md5=944b175f440c1ff323705987cf937ee7
65. 65
  Liu, S.; Chang, J. S.; Herberg, J. T.; Horng, M.-M.; Tomich, P. K.; Lin, A. H.; Marotti, K. R. Allosteric Inhibition of Staphylococcus Aureus d-Alanine:d-Alanine Ligase revealed by Crystallographic Studies Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 15178– 15183 DOI: 10.1073/pnas.0604905103
  There is no corresponding record for this reference.
66. 66
  Shoichet, B.; Bodian, D.; Kuntz, I. Molecular Docking using Shape Descriptors J. Comput. Chem. 1992, 13, 380– 397 DOI: 10.1002/jcc.540130311
  66
  Molecular docking using shape descriptors
  Shoichet, Brian K.; Bodian, Dale L.; Kuntz, Irwin D.
  Journal of Computational Chemistry (1992), 13 (3), 380-97CODEN: JCCHDD; ISSN:0192-8651.
  Mol. docking explores the binding modes of two interacting mols. The technique is increasingly popular for studying protein-ligand interactions and for drug design. A fundamental problem with mol. docking is that orientation space is very large and grows combinatorially with the no. of degrees of freedom of the interacting mols. Here, algorithms are described and evaluated that improve the efficiency and accuracy of a shape-based docking method. Mol. organization and sampling techniques are used to remove the exponential time dependence on mol. size in docking calcns. The new techniques allow one to study systems that were prohibitively large for the original method. The new algorithms are tested in 10 different protein-ligand systems, including systems, including 7 systems where the ligand is itself a protein. In all cases, the new algorithms successfully reproduce the exptl. detd. configurations of the ligand in the protein.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38XitFSlu7g%253D&md5=a10e3291ad760e02c529a59f4b96ae1d
67. 67
  Meng, E.; Shoichet, B.; Kuntz, I. Automated Docking with Grid-Based Energy Evaluation J. Comput. Chem. 1992, 13, 505– 524 DOI: 10.1002/jcc.540130412
  67
  Automated docking with grid-based energy evaluation
  Meng, Elaine C.; Shoichet, Brian K.; Kuntz, Irwin D.
  Journal of Computational Chemistry (1992), 13 (4), 505-24CODEN: JCCHDD; ISSN:0192-8651.
  The ability to generate feasible binding orientations of a small mol. within a site of known structure is important for ligand design. The authors present a method that combines a rapid, geometric docking algorithm with the evaluation of mol. mechanics interaction energies. The computational costs of evaluation are minimal because the authors precalc. the receptor-dependent terms in the potential function at points on a three-dimensional grid. In four test cases where the components of crystallog. detd. complexes are redocked, the "force field" score correctly identifies the family of orientations closest to the exptl. binding geometry. Scoring functions that consider only steric factors or only electrostatic factors are less successful. The force field function will play an important role in efforts to search databases for potential lead compds.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38Xit1Omt7c%253D&md5=f472fffa0c9a61652b4c20e4dbbba69e
68. 68
  Lang, P.; Brozell, S.; Mukherjee, S.; Pettersen, E.; Meng, E.; Thomas, V.; Rizzo, R.; Case, D.; James, T.; Kuntz, I. DOCK 6: Combining Techniques to Model RNA-Small Molecule Complexes RNA 2009, 15, 1219– 1230 DOI: 10.1261/rna.1563609
  68
  DOCK 6: combining techniques to model RNA-small molecule complexes
  Lang, P. Therese; Brozell, Scott R.; Mukherjee, Sudipto; Pettersen, Eric F.; Meng, Elaine C.; Thomas, Veena; Rizzo, Robert C.; Case, David A.; James, Thomas L.; Kuntz, Irwin D.
  RNA (2009), 15 (6), 1219-1230CODEN: RNARFU; ISSN:1355-8382. (Cold Spring Harbor Laboratory Press)
  With an increasing interest in RNA therapeutics and for targeting RNA to treat disease, there is a need for the tools used in protein-based drug design, particularly DOCKing algorithms, to be extended or adapted for nucleic acids. Here, we have compiled a test set of RNA-ligand complexes to validate the ability of the DOCK suite of programs to successfully recreate exptl. detd. binding poses. With the optimized parameters and a minimal scoring function, 70% of the test set with less than seven rotatable ligand bonds and 26% of the test set with less than 13 rotatable bonds can be successfully recreated within 2 Å heavy-atom RMSD. When DOCKed conformations are rescored with the implicit solvent models AMBER generalized Born with solvent-accessible surface area (GB/SA) and Poisson-Boltzmann with solvent-accessible surface area (PB/SA) in combination with explicit water mols. and sodium counterions, the success rate increases to 80% with PB/SA for less than seven rotatable bonds and 58% with AMBER GB/SA and 47% with PB/SA for less than 13 rotatable bonds. These results indicate that DOCK can indeed be useful for structure-based drug design aimed at RNA. Our studies also suggest that RNA-directed ligands often differ from typical protein-ligand complexes in their electrostatic properties, but these differences can be accommodated through the choice of potential function. In addn., in the course of the study, we explore a variety of newly added DOCK functions, demonstrating the ease with which new functions can be added to address new scientific questions.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXmvFCntrY%253D&md5=1fc8e2043aea1fb513b504039ac37b58
69. 69
  Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of Multiple Amber Force Fields and Development of Improved Protein Backbone Parameters Proteins: Struct., Funct., Genet. 2006, 65, 712– 725 DOI: 10.1002/prot.21123
  69
  Comparison of multiple Amber force fields and development of improved protein backbone parameters
  Hornak, Viktor; Abel, Robert; Okur, Asim; Strockbine, Bentley; Roitberg, Adrian; Simmerling, Carlos
  Proteins: Structure, Function, and Bioinformatics (2006), 65 (3), 712-725CODEN: PSFBAF ISSN:. (Wiley-Liss, Inc.)
  The ff94 force field that is commonly assocd. with the Amber simulation package is one of the most widely used parameter sets for biomol. simulation. After a decade of extensive use and testing, limitations in this force field, such as over-stabilization of α-helixes, were reported by the authors and other researchers. This led to a no. of attempts to improve these parameters, resulting in a variety of "Amber" force fields and significant difficulty in detg. which should be used for a particular application. The authors show that several of these continue to suffer from inadequate balance between different secondary structure elements. In addn., the approach used in most of these studies neglected to account for the existence in Amber of two sets of backbone .vphi./ψ dihedral terms. This led to parameter sets that provide unreasonable conformational preferences for glycine. The authors report here an effort to improve the .vphi./ψ dihedral terms in the ff99 energy function. Dihedral term parameters are based on fitting the energies of multiple conformations of glycine and alanine tetrapeptides from high level ab initio quantum mech. calcns. The new parameters for backbone dihedrals replace those in the existing ff99 force field. This parameter set, which the authors denote ff99SB, achieves a better balance of secondary structure elements as judged by improved distribution of backbone dihedrals for glycine and alanine with respect to PDB survey data. It also accomplishes improved agreement with published exptl. data for conformational preferences of short alanine peptides and better accord with exptl. NMR relaxation data of test protein systems.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhtFWqt7fM&md5=de683a26eca9e83ae524726e97ac22fa
70. 70
  Richards, F. Areas, Volumes, Packing and Protein Structure Annu. Rev. Biophys. Bioeng. 1977, 6, 151– 176 DOI: 10.1146/annurev.bb.06.060177.001055
  70
  Areas, volumes, packing, and protein structure
  Richards, Frederic M.
  Annual Review of Biophysics and Bioengineering (1977), 6 (), 151-76CODEN: ABPBBK; ISSN:0084-6589.
  A review with 148 refs.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXks1CisrY%253D&md5=13c917ccbda3f36f1c6390a42c712818
71. 71
  Connolly, M. Solvent-Accessible Surfaces of Proteins and Nucleic Acids Science 1983, 221, 709– 713 DOI: 10.1126/science.6879170
  71
  Solvent-accessible surfaces of proteins and nucleic acids
  Connolly, Michael L.
  Science (Washington, DC, United States) (1983), 221 (4612), 709-13CODEN: SCIEAS; ISSN:0036-8075.
  A method is presented for anal. calcg. a smooth, 3-dimensional contour about a mol. The mol. surface envelope may be drawn on either color raster computer displays or real-time vector computer graphics systems. Mol. areas and vols. may be computed anal. from this surface representation. Unlike most previous computer graphics representations of mols., which imitate wire models or space-filling plastic spheres, this surface shows only the atoms that are accessible to solvent. This anal. method extends the earlier dot surface numerical algorithm, which was applied in enzymol., rational drug design, immunol., and understanding DNA base sequence recognition.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXlsFCkt74%253D&md5=371e91867872dcc45ff5d9c5cc142c95
72. 72
  Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin, T. E. A Geometric Approach to Macromolecule-Ligand Interactions J. Mol. Biol. 1982, 161, 269– 288 DOI: 10.1016/0022-2836(82)90153-X
  72
  A geometric approach to macromolecule-ligand interactions
  Kuntz, Irwin D.; Blaney, Jeffrey M.; Oatley, Stuart J.; Langridge, Robert; Ferrin, Thomas E.
  Journal of Molecular Biology (1982), 161 (2), 269-88CODEN: JMOBAK; ISSN:0022-2836.
  A method is described to explore geometrically feasible alignments of ligands and receptors of known structure. Algorithms are presented that examine many binding geometries and evaluate them in terms of steric overlap. The procedure uses specific mol. conformations. A method is included for finding putative binding sites on a macromol. surface. Results are reported for heme-myoglobin interaction and the binding of thyroid hormone analogs to prealbumin. In each case, the program finds structures within 1 Å of the x-ray results and also finds distinctly different geometries that provide good steric fits. The approach seems well-suited for generating conformations for energy refinement programs and interactive computer graphics routines.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL38XmtFajsbw%253D&md5=8a4234b24356ea5340f33d906cd71d3e
73. 73
  Srinivasan, J.; Cheatham, T.; Cieplak, P.; Kollman, P.; Case, D. A. Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate-DNA Helices J. Am. Chem. Soc. 1998, 120, 9401– 9409 DOI: 10.1021/ja981844+
  73
  Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate-DNA Helixes
  Srinivasan, Jayashree; Cheatham, Thomas E., III; Cieplak, Piotr; Kollman, Peter A.; Case, David A.
  Journal of the American Chemical Society (1998), 120 (37), 9401-9409CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  We apply continuum solvent models to investigate the relative stability of A- and B-form helixes for three DNA sequences, d(CCAACGTTGG)2, d(ACCCGCGGGT)2, and d(CGCGAATTCGCG)2, a phosphoramidate-modified DNA duplex, p(CGCGAATTCGCG)2, in which the O3' atom in deoxyribose is replaced with NH, and an RNA duplex, r(CCAACGUUGG)2. Structures were taken as snapshots from multi-nanosecond mol. dynamics simulations computed in a consistent fashion using explicit solvent and with long-range electrostatics accounted for using the particle-mesh Ewald procedure. The electrostatic contribution to solvation energies were computed using both a finite-difference Poisson-Boltzmann (PB) model and a pairwise generalized Born model; nonelectrostatic contributions were estd. with a surface-area-dependent term. To these solvation free energies were added the mean solute internal energies (detd. from a mol. mechanics potential) and ests. of the solute entropy (from a harmonic anal.). Consistent with expt., the relative energies favor B-form helixes for DNA and A-form helixes for the NP-modified system and for RNA. Salt effects, modeled at the linear or nonlinear PB level, favor the A-form helixes by modest amts.; for d(ACCCGCGGGT)2, salt is nearly able to switch the conformational preference to "A". The results provide a phys. interpretation for the origins of the relative stabilities of A- and B-helixes and suggest that similar analyses might be useful in a variety of nucleic acid conformational problems.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXlsVyqtLo%253D&md5=f705ecbf7efdd5d42ab29f45a06c6e9d
74. 74
  Kollman, P.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, G.; Cieplak, P.; Srinivasan, J.; Case, D.; Cheatham, T. Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models Acc. Chem. Res. 2000, 33, 889– 897 DOI: 10.1021/ar000033j
  74
  Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models
  Kollman, Peter A.; Massova, Irina; Reyes, Carolina; Kuhn, Bernd; Huo, Shuanghong; Chong, Lillian; Lee, Matthew; Lee, Taisung; Duan, Yong; Wang, Wei; Donini, Oreola; Cieplak, Piotr; Srinivasan, Jaysharee; Case, David A.; Cheatham, Thomas E., III
  Accounts of Chemical Research (2000), 33 (12), 889-897CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)
  A review, with 63 refs. A historical perspective on the application of mol. dynamics (MD) to biol. macromols. is presented. Recent developments combining state-of-the-art force fields with continuum solvation calcns. have allowed us to reach the fourth era of MD applications in which one can often derive both accurate structure and accurate relative free energies from mol. dynamics trajectories. We illustrate such applications on nucleic acid duplexes, RNA hairpins, protein folding trajectories, and protein-ligand, protein-protein, and protein-nucleic acid interactions.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXmvFGiu7g%253D&md5=8436ee610ae145894428db1a1deff73c
75. 75
  Hawkins, G. D.; Cramer, C. J.; Truhlar, D. G. Pairwise Solute Descreening of Solute Charges from a Dielectric Medium Chem. Phys. Lett. 1995, 246, 122– 129 DOI: 10.1016/0009-2614(95)01082-K
  75
  Pairwise solute descreening of solute charges from a dielectric medium
  Hawkins, Gregory D.; Cramer, Christopher J.; Truhlar, Donald G.
  Chemical Physics Letters (1995), 246 (1,2), 122-9CODEN: CHPLBC; ISSN:0009-2614. (Elsevier)
  An algorithm is presented for incorporating a pairwise descreening approxn. into the calcn. of the electrostatic component of the polarization free energy of solvation within the generalized Born approxn. The method was tested on a set of 139 mols. contg. H, C, O, and N. The complexity of the descreening calcn. is greatly simplified by the pairwise approxn.; nevertheless, using the pairwise descreening method to parameterize a new version of a previous generalized Born solvation model, it was found that the rms error relative to expt. increased by only 0.2 kcal/mol.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXpsV2mt74%253D&md5=82ab77c3f7d9822bbd5e165701fcb6c4
76. 76
  Hawkins, G. D.; Cramer, C. J.; Truhlar, D. G. Parametrized Models of Aqueous Free Energies of Solvation Based on Pairwise Descreening of Solute Atomic Charges from a Dielectric Medium J. Phys. Chem. 1996, 100, 19824– 19839 DOI: 10.1021/jp961710n
  76
  Parametrized Models of Aqueous Free Energies of Solvation Based on Pairwise Descreening of Solute Atomic Charges from a Dielectric Medium
  Hawkins, Gregory D.; Cramer, Christopher J.; Truhlar, Donald G.
  Journal of Physical Chemistry (1996), 100 (51), 19824-19839CODEN: JPCHAX; ISSN:0022-3654. (American Chemical Society)
  The pairwise descreening approxn. provides a rapid computational algorithm for the evaluation of solute shape effects on electrostatic contributions to solvation energies. In this article the authors show that solvation models based on this algorithm are useful for predicting free energies of solvation across a wide range of solute functionalities, and six new general parametrizations of aq. free energies of solvation based on this approach are presented. The first new model is based on SM2-type at. surface tensions, the AM1 model for the solute, and Mulliken charges. The next two new models are based on SM5-type surface tensions, either the AM1 or the PM3 model for the solute, and Mulliken charges. The final three models are based on SM5-type at. surface tensions and are parametrized using the AM1 or the PM3 model for the solute and CM1 charges. The parametrizations are based on exptl. data for a set of 219 neutral solute mols. contg. a wide range of org. functional groups and the atom types H, C, N, O, F, P, S, Cl, Br, and I and on data for 42 ions contg. the same elements. The av. errors relative to expt. are slightly better than previous methods, but-more significantly-the computational cost is reduced for large mols., and the methods are well suited to using analytic derivs.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XntFSgsb8%253D&md5=b85ff8ba5589119be8f11360a6ea7d9e
77. 77
  Rizzo, R.; Aynechi, T.; Case, D. A.; Kuntz, I. Estimation of Absolute Free Energies of Hydration using Continuum Methods: Accuracy of Partial Charge Models and Optimization of Nonpolar Contributions J. Chem. Theory Comput. 2006, 2, 128– 139 DOI: 10.1021/ct050097l
  77
  Estimation of Absolute Free Energies of Hydration Using Continuum Methods: Accuracy of Partial Charge Models and Optimization of Nonpolar Contributions
  Rizzo, Robert C.; Aynechi, Tiba; Case, David A.; Kuntz, Irwin D.
  Journal of Chemical Theory and Computation (2006), 2 (1), 128-139CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  Abs. free energies of hydration (ΔGhyd) for more than 500 neutral and charged compds. have been computed, using Poisson-Boltzmann (PB) and Generalized Born (GB) continuum methods plus a solvent-accessible surface area (SA) term, to evaluate the accuracy of eight simple point-charge models used in mol. modeling. The goal is to develop improved procedures and protocols for protein-ligand binding calcns. and virtual screening (docking). The best overall PBSA and GBSA results, in comparison with exptl. ΔGhyd values for small mols., were obtained using MSK, RESP, or ChelpG charges obtained from ab initio calcns. using 6-31G* wave functions. Correlations using semiempirical (AM1BCC, AM1CM2, and PM3CM2) or empirical (Gasteiger-Marsili and MMFF94) methods yielded mixed results, particularly for charged compds. For neutral compds., the AM1BCC method yielded the best agreement with exptl. results. In all cases, the PBSA and GBSA results are highly correlated (overall r2 = 0.94), which highlights the fact that various partial charge models influence the final results much more than which continuum method is used to compute hydration free energies. Overall improved agreement with exptl. results was demonstrated using atom-based consts. in place of a single surface area term. Sets of optimized SA consts., suitable for use with a given charge model, were derived by fitting to the difference in exptl. free energies and polar continuum results. The use of optimized atom-based SA consts. for the computation of ΔGhyd can fine-tune already reasonable agreement with exptl. results, ameliorate gross deficiencies in any particular charge model, account for non-optimal radii, or correct for systematic errors.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXht1SrsL3N&md5=0847c6f2749bad3b9196a4d3a2baa8cf
78. 78
  Lessard, I. A.; Healy, V. L.; Park, I.-S.; Walsh, C. T. Determinants for Differential Effects on d-Ala-d-lactate vs d-Ala-d-Ala Formation by the VanA Ligase from Vancomycin-resistant Enterococci Biochemistry 1999, 38, 14006– 14022 DOI: 10.1021/bi991384c
  There is no corresponding record for this reference.
79. 79
  Song, J.; Singh, M. From Hub Proteins to Hub Modules: the Relationship between Essentiality and Centrality in the Yeast Interactome at Different Scales of Organization PLoS Comput. Biol. 2013, 9, e1002910 DOI: 10.1371/journal.pcbi.1002910
  There is no corresponding record for this reference.
80. 80
  Hert, J.; Keiser, M. J.; Irwin, J. J.; Oprea, T. I.; Shoichet, B. K. Quantifying the Relationships among Drug Classes J. Chem. Inf. Model. 2008, 48, 755– 765 DOI: 10.1021/ci8000259
  80
  Quantifying the Relationships among Drug Classes
  Hert, Jerome; Keiser, Michael J.; Irwin, John J.; Oprea, Tudor I.; Shoichet, Brian K.
  Journal of Chemical Information and Modeling (2008), 48 (4), 755-765CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacol. We calcd. the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calcd. with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calc. the ligand-set similarities and to the chem. representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXjt1WqsrY%253D&md5=628dde363e13134238c9de5dc27acd7e
Supporting Information
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.6b00211.
- Variation of the mean force estimator G_j(N), used to determine the characteristic time of relaxation of the Cartesian variable, and hence to give an estimate of γ̅ in TAMD to ensure the time-scales separation γ̅/γ (Figure S1); conformations of VanA extracted along the trajectory TAMD_ΩN and displaying an opening of the loop ω (Figure S2); definition of the collectives variables used during the TAMD trajectories (Table S1); list of TAMD trajectories along with their corresponding sets of collective variables (Table S2); definition of the different communities calculated using the Girvan–Newman algorithm over the MD and TAMD trajectories (Table S3); and time percentage formation of the α helices and β strands along MD and TAMD trajectories (Table S4) (PDF)
- ci6b00211_si_001.pdf (0.99 MB)
Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanAClick to copy article linkArticle link copied!

Journal of Chemical Information and Modeling

Abstract

1 Introduction

Figure 1

2 Materials and Methods

2.1 Molecular Dynamics Simulation

2.2 TAMD Simulations

2.3 Determination of Contact Communities

2.4 Conformational Analysis of the Simulations Using SOM

2.5 Graph Processing of the Self-Organizing Maps

2.6 Analysis of Hydrogen Bonds within VanA

2.7 Ligand Docking Procedure and GBSA Scoring

3 Results

3.1 Choice of Collective Variables from the Structural and Community Domains of VanA

Figure 2

Figure 3

3.2 Conformational Clustering of the Conformational Landscape

Figure 4

Figure 5

3.3 Kinetic Clustering of the VanA Conformational Space

Figure 6

Figure 7

3.4 A Path Describing the ω-Loop Opening

Figure 8

Figure 9

4 Discussion

5 Conclusion

Supporting Information

Terms & Conditions

Author Information

Acknowledgment

References

Cited By

Journal of Chemical Information and Modeling

Article Views

Altmetric

Citations

Recommended Articles

Abstract

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

References

Supporting Information

Supporting Information

Terms & Conditions

Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA
Click to copy article linkArticle link copied!