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ArticleSeptember 29, 2014

POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics
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Journal of Chemical Theory and Computation

Cite this: J. Chem. Theory Comput. 2014, 10, 11, 5047–5056

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https://doi.org/10.1021/ct500381c

Published September 29, 2014

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Abstract

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Analysis of macromolecular/small-molecule binding pockets can provide important insights into molecular recognition and receptor dynamics. Since its release in 2011, the POVME (POcket Volume MEasurer) algorithm has been widely adopted as a simple-to-use tool for measuring and characterizing pocket volumes and shapes. We here present POVME 2.0, which is an order of magnitude faster, has improved accuracy, includes a graphical user interface, and can produce volumetric density maps for improved pocket analysis. To demonstrate the utility of the algorithm, we use it to analyze the binding pocket of RNA editing ligase 1 from the unicellular parasite Trypanosoma brucei, the etiological agent of African sleeping sickness. The POVME analysis characterizes the full dynamics of a potentially druggable transient binding pocket and so may guide future antitrypanosomal drug-discovery efforts. We are hopeful that this new version will be a useful tool for the computational- and medicinal-chemist community.

Subjects

Introduction

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Binding-pocket analysis is an active area of research that includes pocket detection and characterization, druggability prediction, and the study of binding-site flexibility. (1) The advent of the Protein Data Bank (PDB (2)) spurred the creation of a number of software packages aimed at facilitating the analysis of macromolecular pockets. (3-6) In recent years, additional programs have been developed with improved accuracy and increasingly advanced pocket-characterization algorithms, (7-16) as reviewed by Zheng et al. (17)

Pocket analysis is useful for studying receptor dynamics. (18-40) One can get a good sense of the full gamut of possible binding-pocket conformational states by obtaining multiple structures from X-ray crystallography, NMR spectroscopy, or molecular dynamics (MD) simulations and comparing pocket volumes and, in particular, shapes. These comparisons facilitate the identification of novel, pharmacologically relevant binding-pocket conformations, as well as transient binding pockets that are not evident when a limited number of static structures are considered.

Additionally, pocket analysis can also be applied to computer-aided drug discovery (CADD). Among the many complex factors that govern molecular recognition, (41, 42) pocket volume and shape are perhaps the most straightforward. Simply put, a ligand will not generally bind to a receptor if it cannot physically fit within the confines of the binding pocket, and receptor/ligand shape complementarity plays a key role in molecular recognition. (43) Consequently, pocket characterization has been used to inform CADD efforts aimed at predicting ligand binding, whether through virtual screening, QSAR, or volumetric similarity searching. (44-46) Given the astounding variety of pocket geometries possible, (47) this characterization is no trivial task.

To address this challenge, both ligand- and receptor-centric approaches have been developed. Ligand-based methods such as OpenEye’s Rapid Overlay of Chemical Structures (ROCS) algorithm (48) seek to identify novel small-molecule binders by querying a compound database for entries with three-dimensional shapes that are similar to that of a known template ligand, (49) as assessed by the degree of volume-overlap mismatch. These techniques perform comparably to more traditional virtual-screening methods (50) and have been used to successfully identify a number of experimentally validated ligands (see, for example, refs 51−53).

While ligand-based approaches will certainly continue to have high utility, (54) a more receptor-centric methodology is sometimes advantageous. Bound ligands often occupy only a portion of their respective pockets, (-43, 47) on average perhaps as little as a third of the total space available. (43) Analysis of ligand volume and shape alone cannot account for potential interactions with pocket regions that are not occupied by the template ligand itself. In contrast, receptor-based pocket analysis elucidates the volume and shape of the entire cavity, including regions not yet exploited by existing pharmacophores.

Receptor-centric techniques can also be used to select diverse pocket shapes for use in subsequent virtual-screening efforts. It is often helpful to dock a library of small molecules into multiple receptor conformations in order to account for receptor flexibility. Carefully selecting conformations with unique pocket geometries has been shown to enhance hit rates and subsequent ligand diversity. (55-58)

To simplify binding-pocket characterization, we recently developed an algorithm called POVME (POcket Volume MEasurer). (7) POVME floods a pocket-encompassing region with equidistant points, removes those points that are near receptor atoms, and calculates the volume from the remaining points. The points can themselves be saved, providing a specific description of the pocket shape as well. Inspired by the fairly widespread adoption of our program (at least 43 citations as of June 2014), we have now created a second, much improved version. POVME 2.0 is over an order of magnitude faster than POVME 1.0, includes a graphical user interface (Figure 1) that greatly improves usability, can calculate volumetric density maps to facilitate analysis, and has improved accuracy.

Figure 1. POVME 2.0 graphical user interface.

POVME 2.0 has been tested on all major operating systems with various versions of python, numpy, and scipy (Table 1). (59-63) A copy of the program, which is released under the terms of the GNU General Public License, can be obtained from http://nbcr.ucsd.edu/POVME. We are hopeful that POVME will be a useful tool for the computational- and medicinal-chemist community.

Table 1. Operating-System Compatibility^a

operating system	python version	numpy version	scipy version
Scientific Linux 6.2	2.6.6	1.6.2	0.11.0
OS X 10.9.1	2.7.5	1.6.2	0.11.0
Windows 7 Home Premium	2.7.6	1.8.0	0.13.3

POVME 2.0 has been successfully tested on all major operating systems with various versions of python, numpy, and scipy.

Materials and Methods

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The POVME Algorithm

Successful POVME use includes three required and two optional steps. Trajectory alignment, the construction of a pocket-encompassing region, and the subsequent identification of the pocket-occupying space are required. Optionally, the user can also instruct POVME to eliminate subregions that fall outside the receptor’s convex hull and/or are noncontiguous with the primary pocket. A detailed description of each of these steps follows.

1) Aligning the Trajectory

POVME accepts a multiframe PDB (Protein Data Bank) file as input. We expect that MD simulations will be the most common source of these files, but multiple crystal structures or NMR conformations can also be used. We have found that the computer program Visual Molecular Dynamics (VMD) (64) is useful for aligning trajectories and converting files to the PDB format, but other software packages can also be used for this purpose. Alignment is necessary because the POVME algorithm assumes the pocket being measured does not translate or rotate in space. Different alignment methodologies can subtly alter how this requirement is met, as discussed in the Results and Discussion. A tutorial showing how to align and convert trajectory files using VMD is included in the Supporting Information (Text S1). We note also that single-frame PDB files can likewise serve as POVME input if the user wishes only to characterize a single pocket.

2) Defining a Region That Encompasses All Trajectory Binding Pockets

The user must next define “inclusion” (Figure 2A) and “exclusion” (Figure 2B) regions, respectively. Both of these regions are constructed from a combination of user-specified spheres and rectangular prisms. The required inclusion region should entirely encompass all the binding-pocket conformations of the trajectory. POVME includes a helper script called “POVME Pocket ID,” described in greater detail below, to assist in identifying this region if necessary. The optional exclusion region defines portions of the inclusion region that should be ignored, perhaps because they are not truly associated with the pocket. To generate a grid of equidistant points that encompasses all the binding-pocket conformations of the trajectory (spaced 1.0 Å apart by default), POVME first floods the user-specified inclusion region with points and then removes any points also contained in the optional exclusion region (Figure 2C).

Figure 2. A graphical summary of the POVME 2.0 algorithm. A) The user defines an inclusion region. B) The user defines an exclusion region. C) The portion of the inclusion region that is not also in the exclusion region is flooded with equidistant points. D) Any of the points that are close to receptor atoms are deleted. E) Any points outside the convex hull are optionally deleted. F) The user can optionally define a contiguous-points region. G) All points that are not contiguous with that region are similarly deleted.

3) Removing Points That Are near Receptor Atoms

As the purpose of POVME is to measure the volume of the binding-pocket cavity, the program next removes any points that are close to receptor atoms, leaving only those points that are likely to be located within the binding pocket itself (Figure 2D). Specifically, the pairwise distances between all atoms and POVME points are calculated. Any point that is closer to a given atom than that atom’s van der Waals radius, plus a user-specified tolerance (1.09 Å, the radius of a hydrogen atom, by default), is removed. As POVME is written in python, users can easily modify the default radii or add new radius values for novel constituent atom types. If the user wishes to ignore all receptor hydrogen atoms when calculating pocket volumes, the hydrogen radius specified in the script can be set to 0.0 Å.

4) Removing Points Outside the Receptor’s Convex Hull

POVME 2.0 introduces an optional new feature for removing points that lie entirely outside the binding pocket. Specifically, the gift-wrapping algorithm is used in combination with the Akl-Toussaint heuristic (65) to define the convex hull of receptor atoms near the user-defined inclusion region. As the gift-wrapping algorithm runs in O(n²) time, where n is the number of atoms in the receptor structure, it is not necessarily the fastest algorithm for computing the convex hull. However, by coupling it with the Alk-Toussaint heuristic, the expected running time is lowered to O(n). Ultimately, any points that fall outside the convex hull are removed (Figure 2E). This feature is particularly useful when the user defines an inclusion region that protrudes into the surrounding solvent-occupying space.

5) Removing Points That Are Not Contiguous with the Primary Pocket

Like the original POVME program, version 2.0 retains the optional ability to remove isolated patches of points that are not contiguous with the primary binding pocket. This feature requires that the user define a third region, again using spheres and rectangular prisms, that always falls within the primary binding-pocket region regardless of the trajectory frame considered (Figure 2F). All pocket-occupying points within or contiguous to this region are retained, but isolated patches of points that are not directly connected are deleted (Figure 2G).

POVME Output

By default, POVME writes a number of files to the disk. The calculated pocket volumes, as well as user-defined parameters and progress messages, are saved to a simple text-based log file. POVME can also be instructed to save the volume measurements to a second file in a simple tabular format that can be easily pasted into popular spreadsheet programs. Pocket-occupying points are equidistant (1.0 Å by default), so each point is associated with an identical cubical volume (e.g., 1.0 Å³). The volume of a whole pocket is calculated by simply summing the individual volumes associated with each unique point.

POVME also optionally saves the pocket-occupying points of each frame to PDB file(s) on the disk. The user can instruct the program to save these points to separate files and/or to a single PDB trajectory. Some visualization programs (e.g., VMD) are only compatible with trajectories that have the same number of atoms in each frame. POVME can optionally write extra points to the origin (0.0, 0.0, 0.0) on a frame-by-frame basis to satisfy this requirement. As these POVME frames are formatted similarly to those produced by SiteMap, they are also compatible with the pocket-shape volumetric-overlap clustering tools produced by Schrödinger. (13, 14, 55, 56)

Finally, POVME also optionally saves a volumetric density map in the Data Explorer (DX) format, similar to the MDpocket algorithm. (12) A volumetric density value is associated with each of the pocket-occupying points by calculating the fraction of all trajectory pockets that include the given point. If the density map is displayed as an isosurface, the value of the isosurface expresses the fraction of time (e.g., over the course of the simulation) that the pocket included the displayed volume.

The POVME Pocket ID Algorithm

The POVME distribution file also includes POVME Pocket ID, a simple script that identifies binding pockets and generates appropriate POVME inclusion regions. After loading the heavy atoms from a PDB file, the algorithm identifies pockets by 1) covering the entire protein in a low-resolution 3D grid of equidistant points (spaced 4.0 Å apart by default), 2) removing points that come within a user-specified distance of any protein atom (3.0 Å by default), and 3) removing points that fall outside the convex hull defined by the protein alpha carbons. The remaining points tend to congregate in binding pockets.

These points are then replaced with smaller higher-resolution 3D grids of equidistant points (spaced 1.0 Å apart by default). These higher-resolution points are subjected to the same protocol (i.e., points are removed if they are too close to protein atoms or fall outside the convex hull), thus providing a more detailed description of binding-pocket geometries. As stray, isolated points often remain, the algorithm iteratively removes points that have fewer than a user-specified number of neighbors (4 by default) until no such points remain.

Finally, stretches of contiguous high-resolution points are grouped together, thus partitioning the points according to their associated pockets. The points of each pocket are further divided into a user-defined number of clusters (5 by default) using the k-means clustering algorithm, and encompassing spheres are generated for each cluster.

The script outputs a separate PDB file for the points of each pocket. Within each of these files, each cluster of points is assigned a unique PDB chain id. The PDB header describes the inclusion-region sphere associated with each chain/cluster, formatted for use in a POVME input file. Once the user has determined via visualization which of the identified pockets is of greatest interest, he or she can select which calculated spheres are required to entirely encompass that pocket.

Test System: RNA Editing Ligase 1

The 1XDN crystal structure, (66) which includes enzyme residues 52–365 as well as an active-site ATP molecule and magnesium ion, was obtained from the Protein Data Bank. (2) Selenomethionine residues were replaced with methionine. All crystallographic water molecules were retained. The AMBER LEaP module was used to submerge the protein in a rectangular box of water molecules that extended 10 Å beyond the system atoms in all three Cartesian dimensions. Monovalent ions were added to neutralize the system and to bring it to a 0.1 M salt concentration. The protein and water atoms were parameterized using the Amber99SB force field (67) and the TIP4P-ew water model, (68) respectively. Additionally, the parameters for ATP, magnesium, and monovalent ions developed by Meagher et al., (69) Allner et al., (70) and Joung and Cheatham (71) were used, respectively.

The REL1 system was subjected to five 5000-step energy minimizations using the NAMD molecular-dynamics simulation package (72, 73) to gradually introduce full flexibility. We first allowed only hydrogen atoms to move, second released all water molecules, third released ions and ATP, fourth released the protein amino-acid side chains, and fifth removed all constraints. The system was then heated from 0 to 310 K in an NVT ensemble for 500 ps, with the protein backbone restrained. Equilibration was achieved in two segments, each consisting of a 250 ps simulation in the NPT ensemble. In the first segment, the protein backbone was restrained; in the second, no restraints were applied.

Five production simulations were performed, starting from the fully equilibrated structure. A total of 650 ns were simulated (one simulation of 250 ns and four of 100 ns). Different random seeds were used for each productive simulation to generate different starting velocities.

To study the flexibility of the REL1 active site, we extracted 6,500 frames from the simulations, evenly spaced 100 ps apart. All waters, counterions, ATP molecules, and magnesium ions were removed. VMD’s RMSD Trajectory Tool (64) was used to align the extracted frames. In order to determine how differing alignment methodologies would impact the POVME analysis, we used several different protocols. The extracted frames were concatenated and aligned by 1) the atoms of the bound ATP ligand; 2) the atoms of the active-site residues (e.g., any residue within 5 Å of the crystallographic ligand); 3) the alpha-carbon atoms (C_α) of the active-site residues; and 4) the C_α of the entire protein. Each of these four aligned trajectories was saved as a separate multiframe PDB file (Text S1).

Separate POVME analyses were performed for each aligned trajectory. In each case, we characterized the combined ATP/transient pockets using an inclusion region defined by 10 carefully positioned spheres, chosen by visualizing the system in VMD. This region was filled with equidistant points spaced 1.0 Å apart. No exclusion regions were required. Points that were not contiguous with those contained within a small sphere centered at the opening of the ATP-binding pocket were discarded. The new convex-hull feature was enabled.

To benchmark POVME 1.0 and POVME 2.0, we further considered the REL1 trajectory aligned by all C_α. Additional analyses of this trajectory were performed using POVME 1.0 and POVME 2.0 with the new convex-hull feature disabled.

Results and Discussion

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As pocket volume and shape play critical roles in determining small-molecule binding, they are often the focus of computer-docking campaigns, QSAR studies, and molecular-dynamics analyses. We previously created a novel algorithm for characterizing macromolecular pockets called POVME (POcket Volume MEasurer) that has been widely adopted. (7) We here present a much-improved version of the algorithm, POVME 2.0.

POVME 2.0 has four primary advantages over previous versions. First, it is an order of magnitude faster because it relies on the numpy and scipy python modules to perform matrix-based calculations at nearly the speed of compiled C programs. (59-63) Additionally, the user can instruct POVME 2.0 to take advantage of multiple processors to further improve the speed of the calculation.

Second, POVME 2.0 comes with an optional graphical user interface (GUI) to facilitate usability (Figure 1). The GUI requires that Tkinter, (74) a python binding to the Tk GUI toolkit, (75) be installed. Fortunately, Tkinter is included in the standard Windows and OS X python distributions, as well as many Linux distributions.

Third, POVME 2.0 includes a new convex-hull-clipping option that improves the accuracy of the volume calculation. Portions of the binding pocket that fall outside the convex hull of nearby receptor atoms are discarded; consequently, only portions of the pocket that are truly interior to the protein surface are considered.

Fourth, unlike the original version, POVME 2.0 can analyze entire trajectories in addition to single protein conformations. With POVME 1.0, users were required to save each trajectory frame as a separate PDB file in order to study changes in pocket volume and shape over the course of a MD trajectory. In contrast, POVME 2.0 can read multiframe trajectory files without requiring that each frame be saved separately. When analyzing MD trajectories, POVME outputs both frame-by-frame and whole-trajectory analyses. For frame-by-frame analysis, POVME saves the individual pocket shapes in the PDB format. For whole-trajectory analysis, POVME creates a volumetric density map showing the frequency with which different regions of the protein are included in the pocket over the course of the trajectory.

Test Case: Trypanosoma brucei RNA Editing Ligase 1 (REL1)

To demonstrate the utility of this new POVME implementation, we used it to analyze an MD simulation of RNA editing ligase 1 (REL1) from the parasite Trypanosoma brucei, the etiological agent of African sleeping sickness. REL1 is a critical component of the T. brucei editosome, which edits transcriptional RNA prior to translation. This extensive RNA-editing process is essential for trypanosomatid survival, and REL1 has been shown to be a viable drug target. (76, 77) Indeed, REL1 inhibitors have been identified that kill the whole-cell parasite. (78)

Previous studies of related crystal structures have hinted at the existence of a transient subpocket connected to the distal portion of the primary ATP-binding site that may provide unique opportunities for drug discovery. (79) Compounds that bind to the REL1 primary site may also target other ATP-binding proteins with structurally similar pockets; however, compounds that bind to the unique transient pocket may prove more target specific.

To better characterize the dynamics of the REL1 pockets, we examined 6,500 combined ATP-transient pockets extracted from 650 ns of MD simulations. We first aligned the trajectory to ensure that the binding pocket was consistently in the same location. As with other pocket-analysis programs, (12, 17, 80) simulation-trajectory alignment impacts the calculation of the average volumetric density maps. To demonstrate this sensitivity, we performed four separate POVME analyses, aligning the REL1 trajectory by 1) all ATP-ligand atoms; 2) all the atoms of the active-site residues; 3) the alpha-carbon (C_α) atoms of the active-site residues; and 4) the C_α atoms of the entire protein. Volumetric density maps were calculated for each of these aligned trajectories and were visualized superimposed on the receptor structure using VMD. When displayed as an isosurface, these density maps show the fraction of frames with measured pockets that included the displayed volume.

For the purposes of comparison, we judged the utility of each alignment protocol by how consistently the associated POVME analysis captured the ATP-binding-pocket region over the course of the entire trajectory. As our simulations included a bound ATP ligand, the ATP-binding subpocket should always be open (i.e., the region of the volumetric map corresponding to ATP in our simulations should have a high density, in excess of 95%).

When the trajectory was aligned by all active-site C_α atoms, the POVME-identified pocket consistently included the ATP-binding region (Figure 3B). We also found that aligning by all active-site atoms or even the atoms of the bound ligand itself led to similar POVME results (Figure S1). In contrast, the pocket analysis was less than optimal when the trajectory was aligned by the C_α atoms of the whole receptor (Figure 3C), likely because substantial protein motions distant from the active site led to poor binding-pocket alignment. Consequently, the transient pocket was identified as open only half as often when the trajectory was aligned by all C_α atoms vs active-site C_α atoms.

Figure 3. Volumetric density maps of the REL1 active site. Some regions of the protein have been removed to facilitate visualization. A) The crystallographic pose of the bound ATP molecule. Crystallographic water molecules indicate the location of a secondary binding pocket that is transiently accessible from the ATP-binding pocket. B) The region of the binding pocket identified as “open” at least 95% of the time when the trajectory was aligned by the active-site C_α atoms. C) The same region when the trajectory was aligned by the C_α atoms of the whole protein. D) The region of the binding pocket identified as “open” at least 25% of the time when the active-site-C_α alignment was again used.

While the best protocol to use is likely system dependent, based on these REL1 results we concur with others in recommending that trajectories be aligned by active-site C_α atoms. (80) This alignment 1) consistently identified the ATP-binding pocket; 2) does not rely on the presence of a bound ligand and so can be applied to apo systems as well; and 3) requires fewer atoms. When the binding pocket is partly composed of flexible loops, aligning by pocket C_α atoms that belong to stable secondary-structure elements may be more appropriate.

When the active-site-C_α alignment was used, POVME analysis revealed the full dynamics of the transient REL1 pocket, as indicated by the density maps in Figures 3B and 3D at isovalues of 95% and 25%, respectively. As expected given that we simulated the holo protein, the primary ATP-binding pocket was persistently open throughout the entire simulation (Figure 3D, 95% isovalue). The intermittent transient pocket was open at least 25% of the time (Figure 3D), suggesting a persistence sufficient to support our hypothesis of druggability.

Benchmarking/Insights into Use

To verify that the results of POVME 2.0 are comparable to those of the previous version, we similarly analyzed a REL1 trajectory using POVME 1.0. When the convex-hull algorithm was disabled, both POVME 1.0 and 2.0 gave nearly identical volume measurements (Figure 4 graph, in black). When the new convex-hull feature was enabled, POVME-calculated volumes were lower, as expected (Figure 4 graph, in gray). To verify that the volumes calculated both with and without the convex-hull feature were correlated, we performed a linear regression. A two-tailed t-test suggested that the correlation was statistically significant (Pearson correlation coefficient: 0.19; p-value: 0.0).

Figure 4. POVME 1.0 and 2.0 benchmarks. The graph shows benchmark REL1 pocket volumes as a function of simulation time. POVME 1.0 and 2.0 give nearly the same volume measurements (in black). When the POVME 2.0 convex-hull option is enabled, the volumes are smaller (in gray). The bottom panel, generated using the 1XDN crystal structure, illustrates the difference. When the convex-hull option is enabled, the region of the binding pocket is more accurately captured (solid gray) than when it is deactivated (black wireframe). Some portions of the protein have been removed to facilitate visualization.

Although the convex-hull feature does add computational expense, in some contexts it may lead to more accurate characterizations. If the user’s ultimate goal is to calculate pocket volumes in absolute terms, clearly discarding volumes that lie outside the convex hull leads to improved accuracy (Figure 4, bottom panel). In contrast, if the goal is to determine how frequently different pocket regions are open vs closed by calculating volumetric density maps, the convex-hull feature, which discards only volumes that fall outside the pocket, will have little impact.

Finally, if the goal is to compare changes in the total pocket volume over the course of a trajectory, the convex-hull feature should be used with caution. Arbitrary movements of surface side chains near pocket openings can subtly affect the specific convex hull calculated for each frame. If the POVME inclusion region includes many points that lie outside the convex hull, and if the system being studied has highly flexible pocket-adjacent surface residues, using the convex-hull feature can introduce substantial “noise” into the total-volume calculations. This noise can make it difficult to detect subtle changes in the total volume caused by actual pocket dynamics, especially if the pocket itself is relatively stable. To overcome this challenge, we recommend carefully defining the POVME inclusion region to encompass only the pocket itself. If the convex-hull feature is used at all, rely on it only as a secondary refinement to better account for those rare frames when the pocket opening “pulls back,” causing a few POVME points that are typically located within the pocket to barely fall outside the pocket boundaries.

Software Comparison

Pocket Identification

Many approaches have been developed to identify and measure protein pockets (see recent reviews (81, 82)). These approaches are commonly divided into geometry- and energy-based detection algorithms. (17, 81, 82) We here limit our comparison to free software packages that natively process ensembles of structures, whether derived from multiple crystal structures or MD simulations, without requiring custom scripting. Software packages with this native support, including POVME 2.0, EPOS^BP, (9) MDpocket, (12) PocketAnalyzer^PCA, (80) and trj_cavity, (83) can output ensemble-dependent features such as the frequency of transient subpocket opening and the pocket size over multiple frames.

For all these programs, pocket identification is the initial step. POVME 2.0 pocket-defining regions can be generated automatically using the new grid-based POVME Pocket ID helper script described above. PocketAnalyzer^PCA and trj_cavity use similar grid-based detection algorithms. PocketAnalyzer^PCA implements a variant of the LIGSITE algorithm, (84, 85) and trj_cavity employs a novel neighbor-search method. In contrast, MDpocket uses fpocket to detect and calculate pocket properties. Fpocket calls the Qhull algorithm (86) to perform a Voronoi tessellation of the receptor; the coordinates of each Voronoi vertex, together with the associated atomic and vertex neighbors, constitute an “α-sphere” with a distinct radius. The collection of all α-spheres is then used to locate protein pockets. Finally, EPOS^BP relies on the PASS algorithm, which first covers the entire protein surface with spheres and then removes spheres with low burial counts. As a second step, the algorithm covers the remaining spheres from the first step with an additional layer of spheres. Pockets are enumerated once repeating this process iteratively has successfully filled all cavities. EPOS^BP also maps the identified pockets to a set of pocket-lining atoms (PLAs), avoiding the need to align the trajectory.

Volume Measurements

Pocket volumetric analysis follows the initial identification step. In order to facilitate comparison, we repeated the trajectory-based analysis of the REL1 ATP-binding pocket using several programs (active-site-C_α alignment). For each program, a grid resolution of 1.0 Å was used where appropriate. The remaining parameters were set to their default values.

Given the set of POVME inclusion and exclusion spheres chosen, POVME 2.0 without the convex-hull algorithm generally calculated pocket volumes that were larger than those determined by other codes. In contrast, when the convex-hull algorithm was used to ignore regions of space outside the confines of the pocket, the POVME-calculated volumes decreased substantially (see Table 2 and Figure S2).

Table 2. Program Comparisons^a

program	average volume ± SD	run time (1 thread)	run time (24 threads)
POVME 1.0		10500
POVME 2.0	2071.3 ± 129.1	16	2
POVME 2.0/convex hull	1021.8 ± 154.8	92	8
trj_cavity	814.4 ± 254.6	16
MDpocket	523.2 ± 60.5	11
PocketAnalyzer^PCA	811.5 ± 100.2	96*
EPOS^BP without clustering		43
EPOS^BP with clustering		259

The average pocket volume (in Å³), plus or minus the standard deviation, measured over the course of a REL1 trajectory using several pocket-analysis programs. Note that the POVME 1.0 results were, for all intents and purposes, identical to the POVME 2.0 results with the convex-hull feature disabled. Additionally, EPOS^BP volume measurements are not included because that program does not output volume-per-frame data. The total run times for each program are given in minutes. A PCA calculation accounted for approximately 6 min of the PocketAnalyzer^PCA run time (marked with an asterisk). As POVME 2.0 is designed to use multiple processors, the run times for parallel POVME 2.0 calculations are also shown.

If accuracy is judged by consistency with other programs, using POVME 2.0 together with the convex-hull algorithm leads to substantial improvements when the user wishes to measure pocket volume in absolute terms. Nevertheless, the POVME-calculated volumes are still somewhat larger than those calculated by other algorithms, even with the convex-hull algorithm enabled. This discrepancy does not necessarily mean that POVME volumes are overestimated, as determining the accuracy of these varied methods is not straightforward. Binding pockets are not uniquely defined; they often have an opening toward the solvent, and no commonly agreed upon criteria exists for delineating the exact boundary between the pocket and solvent-filled spaces.

The tunable parameters of each algorithm may also contribute to the differences in calculated volumes. Although it is useful to allow the user to adjust key parameters as required to best analyze a specific pocket of interest, doing so often presents a problem when the parameters are not intuitive. With the end user in mind, POVME 2.0 requires only a small number of parameters with straightforward physical interpretations. We believe this is a distinct advantage of our code. While useful, parameters such as degree of buriedness and minimal cluster size (PocketAnalyzer^PCA); PASS input (EPOS^BP); and allowed range of α-sphere radii and number of α-spheres per pocket (MDpocket) are less intuitive.

PocketAnalyzer^PCA and trj_cavity volumes are somewhat smaller than the POVME volumes obtained when the convex-hull feature is enabled; in contrast, MDpocket volumes are substantially smaller and have only minor fluctuations (Table 2 and Figure S2), possibly because MDpocket uses α-spheres rather than equidistant points. To determine if the volumes calculated using these four methods were even correlated, we first reduced the noise in the data using a simple moving average with a 40-point sample window. We then performed linear regressions to explore the relationships between these averaged POVME volumes and the averaged volumes obtained using PocketAnalyzer^PCA, trj_cavity, and MDpocket, respectively. A two-tailed t-test was applied to the Pearson’s correlation coefficients to judge significance. The p-values were 0.46, 0.00, and 0.00 for POVME vs PocketAnalyzer^PCA, trj_cavity, and MDpocket, respectively, suggesting that the correlations between POVME volumes and both trj_cavity and MDpocket volumes are statistically significant.

Future efforts will focus on expanding POVME analysis beyond simple volume and shape calculations. We intend to implement other pocket metrics (e.g., pocket surface-area calculations) and analysis tools (e.g., volume-based pharmacophore modeling).

Execution Time

The trajectory-based pocket calculations were also used to compare the execution times of each program (Table 2). All calculations were run on the same machine (2 Intel Xeon X5690 3.47Ghz processors, 6 CPU cores per processor, 2 threads per core = 24 threads total). On a single thread, POVME 2.0 is several orders of magnitude faster than its predecessor. When the convex-hull feature is disabled, POVME 2.0 is also faster than EPOS^BP and PocketAnalyzer^PCA, comparable to trj_cavity, and only slightly slower than MDpocket. We are exploring alternative methods for optimizing the optional convex-hull feature. Fortunately, the current POVME implementation is highly parallelized; when 24 threads are used rather than one, POVME 2.0 is substantially faster than all other packages tested, even with the convex-hull feature enabled.

Program Input and Output

POVME 2.0, trj_cavity, MDpocket, PocketAnalyzer^PCA, and EPOS^BP all output simple PDB file(s) that are easily visualized with other software packages. POVME 2.0 and MDpocket also output volumetric density maps in the DX format to make exploration of potentially druggable transient subpockets with varying opening frequencies simpler to interpret. Finally, EPOS^BP and PocketAnalyzer^PCA both have built-in features to cluster pocket shapes. Similarly, POVME output is easily clustered using utilities like the volume-overlap tool in Schrödinger’s Maestro suite. (55, 56)

POVME 2.0 accepts a multiframe PDB file as input. As the PDB format is general purpose and universally accepted, POVME is independent of any specific simulation package or force field. Nevertheless, PDB trajectories can become quite large, so being able to read binary formats specific to selected simulation software packages would also be useful. Indeed, trj_cavity can be compiled to read binary trajectories saved in the Gromacs format. Future implementations of POVME may include support for additional formats, though we do wish to avoid limiting our software to a specific simulation engine. We note that POVME trajectories do not include water molecules, substantially cutting down on the file size even when the PDB format is used. Additionally, freely available software can easily convert binary formats to PDB. A helpful tutorial is included in the Supporting Information that describes how to perform this conversion using VMD (64) (Text S1).

Conclusion

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POVME 2.0 is a much improved version of our popular algorithm for characterizing the volumes and shapes of macromolecular (e.g., protein) binding pockets. Version 2.0 implements a number of enhancements, including speed improvements due to numpy/scipy integration and the optional use of multiple processors; better accuracy due to an optional convex-hull implementation; additional volumetric-analysis tools (i.e., volumetric density maps); and a graphical user interface that improves usability.

Although pocket-shape and volumetric analyses are not novel, factors such as the high computational cost of most algorithms have discouraged widespread adoption. POVME 2.0 significantly reduces the amount of time required, allowing users to more rapidly analyze large ensembles of pocket shapes derived from multiple experimental structures or simulation methods, such as MD. The added volumetric-density-map analysis feature provides a pocket-centric view of receptor flexibility with potentially useful drug-discovery applications. Indeed, others have shown that docking into structurally distinct binding pockets can lead to enhanced hit rates and chemical diversity. (55-58)

To demonstrate how POVME 2.0 can provide pharmacologically relevant information about pocket flexibility, we used it to analyze the dynamics of an essential, ATP-binding component of the T. brucei editosome, REL1. (76, 77) Given that ATP-binding pockets are ubiquitous, small-molecule inhibitors that bind exclusively to the primary REL1 pocket may also bind to the ATP pockets of critical human enzymes, leading to undesirable side effects. Consequently, we considered a unique secondary binding pocket that is transiently accessible from the primary REL1 ATP-binding pocket. POVME suggests this transient pocket assumes an open conformation roughly 25% of the time. Identifying less promiscuous REL1 inhibitors that exploit this unique pocket is an important component of our ongoing efforts to target this crucial enzyme.

Supporting Information

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The Supporting Information contains two files. The first contains Figures S1 and S2. Figure S1 shows the POVME volumetric density maps generated when the REL1 trajectory was aligned by all active-site atoms and the atoms of the bound ligand. Figure S2 shows the pocket volumes calculated over the course of a REL1 molecular dynamics simulation, using several different software packages. The second file, Text S1, contains a tutorial that shows how VMD can be used to align a trajectory. The same tutorial also shows how to save a trajectory in the multiframe PDB format for subsequent POVME analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

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

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Corresponding Author
- Rommie E. Amaro - Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, United States; National Biomedical Computation Resource, Center for Research in Biological Systems, University of California San Diego, La Jolla, California 92093, United States; Email: [email protected]
Authors
- Jacob D. Durrant - Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, United States; National Biomedical Computation Resource, Center for Research in Biological Systems, University of California San Diego, La Jolla, California 92093, United States
- Lane Votapka - Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, United States
- Jesper Sørensen - Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, United States
Notes
The authors declare no competing financial interest.

Acknowledgment

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We would like to thank Dr. Victoria A. Feher and Mr. Jeffrey R. Wagner for helpful discussions, as well as Mr. Cam Farnell for developing Rapyd-Tk, a python-based program used to develop the POVME 2.0 graphical user interface. This work was funded by the National Institutes of Health through the NIH Director’s New Innovator Award Program DP2-OD007237 and the NSF XSEDE Supercomputer resources grant RAC CHE060073N to R.E.A. L.V. is funded by the National Science Foundation’s Graduate Research Fellowship Program. J.S. thanks the Alfred Benzon Foundation for generous postdoctoral funding. In addition, this work was also supported by the National Biomedical Computation Resource (NBCR), P41 GM103426.

References

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Tunnels and channels facilitate the transport of small mols., ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chem. properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromol. structures. Herein we present a new version of CAVER enabling automatic anal. of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calcn. and clustering of pathways. A trajectory from a mol. dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the anal. of mol. dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estd. the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that anal. of mol. dynamics simulation is essential for the estn. of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochem. phenomena in the area of mol. transport, mol. recognition and enzymic catalysis. The software is freely available as a multiplatform command-line application online.
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Identification and characterization of binding sites is key in the process of structure-based drug design. In some cases there may not be any information about the binding site for a target of interest. In other cases, a putative binding site has been identified by computational or exptl. means, but the druggability of the target is not known. Even when a site for a given target is known, it may be desirable to find addnl. sites whose targeting could produce a desired biol. response. A new program, called SiteMap, is presented for identifying and analyzing binding sites and for predicting target druggability. In a large-scale validation, SiteMap correctly identifies the known binding site as the top-ranked site in 86% of the cases, with best results (>98%) coming for sites that bind ligands with subnanomolar affinity. In addn., a modified version of the score employed for binding-site identification allows SiteMap to accurately classify the druggability of proteins as measured by their ability to bind passively absorbed small mols. tightly. In characterizing binding sites, SiteMap provides quant. and graphical information that can help guide efforts to critically assess virtual hits in a lead-discovery application or to modify ligand structure to enhance potency or improve phys. properties in a lead-optimization context.
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Nature communications (2011), 2 (), 388 ISSN:.
The recently discovered 150-cavity in the active site of group-1 influenza A neuraminidase (NA) proteins provides a target for rational structure-based drug development to counter the increasing frequency of antiviral resistance in influenza. Surprisingly, the 2009 H1N1 pandemic virus (09N1) neuraminidase was crystalized without the 150-cavity characteristic of group-1 NAs. Here we demonstrate, through a total sum of 1.6 μs of biophysical simulations, that 09N1 NA exists in solution preferentially with an open 150-cavity. Comparison with simulations using avian N1, human N2 and 09N1 with a I149V mutation and an extensive bioinformatics analysis suggests that the conservation of a key salt bridge is crucial in the stabilization of the 150-cavity across both subtypes. This result provides an atomic-level structural understanding of the recent finding that antiviral compounds designed to take advantage of contacts in the 150-cavity can inactivate both 2009 H1N1 pandemic and avian H5N1 viruses.
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Baron, R.; Vellore, N. A. LSD1/CoREST is an allosteric nanoscale clamp regulated by H3-histone-tail molecular recognition Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 12509– 12514
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Fuchs, J. E.; Huber, R. G.; Von Grafenstein, S.; Wallnoefer, H. G.; Spitzer, G. M.; Fuchs, D.; Liedl, K. R. Dynamic Regulation of Phenylalanine Hydroxylase by Simulated Redox Manipulation PLoS One 2012, 7, e53005
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Sinko, W.; de Oliveira, C.; Williams, S.; Van Wynsberghe, A.; Durrant, J. D.; Cao, R.; Oldfield, E.; McCammon, J. A. Applying Molecular Dynamics Simulations to Identify Rarely Sampled Ligand-bound Conformational States of Undecaprenyl Pyrophosphate Synthase, an Antibacterial Target Chem. Biol. Drug Des. 2011, 77, 412– 420
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Applying molecular dynamics simulations to identify rarely sampled ligand-bound conformational states of undecaprenyl pyrophosphate synthase, an antibacterial target
Sinko, William; de Oliveira, Cesar; Williams, Sarah; Van Wynsberghe, Adam; Durrant, Jacob D.; Cao, Rong; Oldfield, Eric; McCammon, J. Andrew
Chemical Biology & Drug Design (2011), 77 (6), 412-420CODEN: CBDDAL; ISSN:1747-0277. (Wiley-Blackwell)
Undecaprenyl pyrophosphate synthase is a cis-prenyltransferase enzyme, which is required for cell wall biosynthesis in bacteria. Undecaprenyl pyrophosphate synthase is an attractive target for antimicrobial therapy. We performed long mol. dynamics simulations and docking studies on undecaprenyl pyrophosphate synthase to investigate its dynamic behavior and the influence of protein flexibility on the design of undecaprenyl pyrophosphate synthase inhibitors. We also describe the first x-ray crystallog. structure of Escherichia coli apo-undecaprenyl pyrophosphate synthase. The mol. dynamics simulations indicate that undecaprenyl pyrophosphate synthase is a highly flexible protein, with mobile binding pockets in the active site. By carrying out docking studies with exptl. validated undecaprenyl pyrophosphate synthase inhibitors using high- and low-populated conformational states extd. from the mol. dynamics simulations, we show that structurally dissimilar compds. can bind preferentially to different and rarely sampled conformational states. By performing structural analyses on the newly obtained apo-undecaprenyl pyrophosphate synthase and other crystal structures previously published, we show that the changes obsd. during the mol. dynamics simulation are very similar to those seen in the crystal structures obtained in the presence or absence of ligands. We believe that this is the first time that a rare "expanded pocket" state, key to drug design and verified by crystallog., has been extd. from a mol. dynamics simulation.
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Lindert, S.; McCammon, J. A. Dynamics of Plasmodium falciparum enoyl-ACP reductase and implications on drug discovery Protein Sci. 2012, 21, 1734– 1745
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Dynamics of Plasmodium falciparum enoyl-ACP reductase and implications on drug discovery
Lindert, Steffen; McCammon, J. Andrew
Protein Science (2012), 21 (11), 1734-1745CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
Enoyl-acyl carrier protein reductase (ENR) is a crucial enzyme in the type II fatty acid synthesis pathway of many pathogens such as Plasmodium falciparum, the etiol. agent of the most severe form of malaria. Because of its essential function of fatty acid double bond redn. and the absence of a human homolog, PfENR is an interesting drug target. Although extensive knowledge of the protein structure has been gathered over the last decade, comparatively little remains known about the dynamics of this crucial enzyme. Here, we perform extensive mol. dynamics simulations of tetrameric PfENR in different states of cofactor and ligand binding, and with a variety of different ligands bound. A pocket-vol. anal. is also performed, and virtual screening is used to identify potential druggable hotspots. The implications of the results for future drug-discovery projects are discussed.
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Boechi, L.; de Oliveira, C. A.; Da Fonseca, I.; Kizjakina, K.; Sobrado, P.; Tanner, J. J.; McCammon, J. A. Substrate-dependent dynamics of UDP-galactopyranose mutase: Implications for drug design Protein Sci. 2013, 22, 1490– 1501
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Substrate-dependent dynamics of UDP-galactopyranose mutase: Implications for drug design
Boechi, Leonardo; de Oliveira, Cesar Augusto F.; Da Fonseca, Isabel; Kizjakina, Karina; Sobrado, Pablo; Tanner, John J.; McCammon, J. Andrew
Protein Science (2013), 22 (11), 1490-1501CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
Trypanosoma cruzi is the causative agent of Chagas disease, a neglected tropical disease that represents one of the major health challenges of the Latin American countries. Successful efforts were made during the last few decades to control the transmission of this disease, but there is still no treatment for the 10 million adults in the chronic phase of the disease. In T. cruzi, as well as in other pathogens, the flavoenzyme UDP-galactopyranose mutase (UGM) catalyzes the conversion of UDP-galactopyranose to UDP-galactofuranose, a precursor of the cell surface β-galactofuranose that is involved in the virulence of the pathogen. The fact that UGM is not present in humans makes inhibition of this enzyme a good approach in the design of new Chagas therapeutics. By performing a series of computer simulations of T. cruzi UGM in the presence or absence of an active site ligand, we address the mol. details of the mechanism that controls the uptake and retention of the substrate. The simulations suggest a modular mechanism in which each moiety of the substrate controls the flexibility of a different protein loop. Furthermore, the calcns. indicate that interactions with the substrate diphosphate moiety are esp. important for stabilizing the closed active site. This hypothesis is supported with kinetics measurements of site-directed mutants of T. cruzi UGM. Our results extend our knowledge of UGM dynamics and offer new alternatives for the prospective design of drugs.
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Wu, Y.; Qin, G. R.; Gao, F.; Liu, Y.; Vavricka, C. J.; Qi, J. X.; Jiang, H. L.; Yu, K. Q.; Gao, G. F. Induced opening of influenza virus neuraminidase N2 150-loop suggests an important role in inhibitor binding Sci. Rep. 2013, 3, 1551
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Induced opening of influenza virus neuraminidase N2 150-loop suggests an important role in inhibitor binding
Wu, Yan; Qin, Guangrong; Gao, Feng; Liu, Yue; Vavricka, Christopher J.; Qi, Jianxun; Jiang, Hualiang; Yu, Kunqian; Gao, George F.
Scientific Reports (2013), 3 (), 1551, 8 pp.CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)
The recently discovered 150-cavity (formed by loop residues 147-152, N2 numbering) adjacent to the enzymic active site of Group 1 influenza A neuraminidase (NA) has introduced a novel target for the design of next-generation NA inhibitors. However, only Group 1 NAs, with the exception of the 2009 pandemic H1N1 NA, possess a 150-cavity, and no 150-cavity has been obsd. in Group 2 NAs. The role of the 150-cavity played in enzymic activity and inhibitor binding is not well understood. Here, oseltamivir carboxylate can induce opening of the rigid closed N2 150-loop and provide a novel mechanism for 150-loop movement using mol. dynamics simulations. The authors' results provide the structural and biophys. basis of the open form of 150-loop and illustrates that the inherent flexibility and the ligand induced flexibility of the 150-loop should be taken into consideration for future drug design.
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Han, N. Y.; Mu, Y. G. Plasticity of 150-Loop in Influenza Neuraminidase Explored by Hamiltonian Replica Exchange Molecular Dynamics Simulations PLoS One 2013, 8, e60995
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Schultes, S.; Nijmeijer, S.; Engelhardt, H.; Kooistra, A. J.; Vischer, H. F.; de Esch, I. J. P.; Haaksma, E. E. J.; Leurs, R.; de Graaf, C. Mapping histamine H-4 receptor-ligand binding modes MedChemComm 2013, 4, 193– 204
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Li, P.; Chen, Z.; Xu, H.; Sun, H.; Li, H.; Liu, H.; Yang, H.; Gao, Z.; Jiang, H.; Li, M. The gating charge pathway of an epilepsy-associated potassium channel accommodates chemical ligands Cell Res. 2013, 23, 1106– 1118
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The gating charge pathway of an epilepsy-associated potassium channel accommodates chemical ligands
Li, Ping; Chen, Zhuxi; Xu, Haiyan; Sun, Haifeng; Li, Hao; Liu, Hong; Yang, Huaiyu; Gao, Zhaobing; Jiang, Hualiang; Li, Min
Cell Research (2013), 23 (9), 1106-1118CODEN: CREEB6; ISSN:1001-0602. (NPG Nature Asia-Pacific)
Voltage-gated potassium (Kv) channels derive their voltage sensitivity from movement of gating charges in voltage-sensor domains (VSDs). The gating charges translocate through a phys. pathway in the VSD to open or close the channel. Previous studies showed that the gating charge pathways of Shaker and Kv1.2-2.1 chimeric channels are occluded, forming the structural basis for the focused elec. field and gating charge transfer center. Here, we show that the gating charge pathway of the voltage-gated KCNQ2 potassium channel, activity redn. of which causes epilepsy, can accommodate various small mol. ligands. Combining mutagenesis, mol. simulation and electrophysiol. recording, a binding model for the probe activator, ztz240, in the gating charge pathway was defined. This information was used to establish a docking-based virtual screening assay targeting the defined ligand-binding pocket. Nine activators with five new chemotypes were identified, and in vivo expts. showed that three ligands binding to the gating charge pathway exhibit significant anti-epilepsy activity. Identification of various novel activators by virtual screening targeting the pocket supports the presence of a ligand-binding site in the gating charge pathway. The capability of the gating charge pathway to accommodate small mol. ligands offers new insights into the gating charge pathway of the therapeutically relevant KCNQ2 channel.
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Kekenes-Huskey, P. M.; Metzger, V. T.; Grant, B. J.; McCammon, J. A. Calcium binding and allosteric signaling mechanisms for the sarcoplasmic reticulum Ca2+ATPase Protein Sci. 2012, 21, 1429– 1443
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Calcium binding and allosteric signaling mechanisms for the sarcoplasmic reticulum Ca2+ ATPase
Kekenes-Huskey, Peter M.; Metzger, Vincent T.; Grant, Barry J.; McCammon, J. Andrew
Protein Science (2012), 21 (10), 1429-1443CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
The sarcoplasmic reticulum Ca2+ ATPase (SERCA) is a membrane-bound pump that utilizes ATP to drive calcium ions from the myocyte cytosol against the higher calcium concn. in the sarcoplasmic reticulum. Conformational transitions assocd. with Ca2+-binding are important to its catalytic function. We have identified collective motions that partition SERCA crystallog. structures into multiple catalytically-distinct states using principal component anal. Using Brownian dynamics simulations, we demonstrate the important contribution of surface-exposed, polar residues in the diffusional encounter of Ca2+. Mol. dynamics simulations indicate the role of Glu309 gating in binding Ca2+, as well as subsequent changes in the dynamics of SERCA's cytosolic domains. Together these data provide structural and dynamical insights into a multistep process involving Ca2+ binding and catalytic transitions.
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Bung, N.; Pradhan, M.; Srinivasan, H.; Bulusu, G. Structural Insights into E. coli Porphobilinogen Deaminase during Synthesis and Exit of 1-Hydroxymethylbilane PLoS Comput. Biol. 2014, 10, e1003484
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Torres, R.; Swift, R. V.; Chim, N.; Wheatley, N.; Lan, B. S.; Atwood, B. R.; Pujol, C.; Sankaran, B.; Bliska, J. B.; Amaro, R. E.; Goulding, C. W. Biochemical, Structural and Molecular Dynamics Analyses of the Potential Virulence Factor RipA from Yersinia pestis PLoS One 2011, 6, e25084
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Grant, B. J.; Lukman, S.; Hocker, H. J.; Sayyah, J.; Brown, J. H.; McCammon, J. A.; Gorfe, A. A. Novel Allosteric Sites on Ras for Lead Generation PLoS One 2011, 6, e25711
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Mowrey, D. D.; Liu, Q.; Bondarenko, V.; Chen, Q.; Seyoum, E.; Xu, Y.; Wu, J.; Tang, P. Insights into Distinct Modulation of alpha 7 and alpha 7 beta 2 Nicotinic Acetylcholine Receptors by the Volatile Anesthetic Isoflurane J. Biol. Chem. 2013, 288, 35793– 35800
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Yi-Xin, A.; Jun-Rui, L.; Chun-Wei, X.; Jiang-Bei, M.; Xu-Yun, Y.; He, Z. Simulated Mechanism of Triclosan in Modulating the Active Site and Loop of FabI by Computer Acta Phys.-Chim. Sin. 2014, 30, 559– 568
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Blachly, P. G.; de Oliveira, C. A. F.; Williams, S. L.; McCammon, J. A. Utilizing a Dynamical Description of IspH to Aid in the Development of Novel Antimicrobial Drugs PLoS Comput. Biol. 2013, 9, e1003395
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Utilizing a dynamical description of IspH to aid in the development of novel antimicrobial drugs
Blachly, Patrick G.; de Oliveira, Cesar A. F.; Williams, Sarah L.; Andrew McCammon, J.
PLoS Computational Biology (2013), 9 (12), e1003395/1-e1003395/13, 13 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
The nonmevalonate pathway is responsible for isoprenoid prodn. in microbes, including H. pylori, M. tuberculosis and P. falciparum, but is nonexistent in humans, thus providing a desirable route for antibacterial and antimalarial drug discovery. We coordinate a structural study of IspH, a [4Fe-4S] protein responsible for converting HMBPP to IPP and DMAPP in the ultimate step in the nonmevalonate pathway. By performing accelerated mol. dynamics simulations on both substrate-free and HMBPP-bound [Fe4S4]2+ IspH, we elucidate how substrate binding alters the dynamics of the protein. Using principal component anal., we note that while substrate-free IspH samples various open and closed conformations, the closed conformation obsd. exptl. for HMBPP-bound IspH is inaccessible in the absence of HMBPP. In contrast, simulations with HMBPP bound are restricted from accessing the open states sampled by the substrate-free simulations. Further investigation of the substrate-free simulations reveals large fluctuations in the HMBPP binding pocket, as well as allosteric pocket openings - both of which are achieved through the hinge motions of the individual domains in IspH. Coupling these findings with solvent mapping and various structural analyses reveals alternative druggable sites that may be exploited in future drug design efforts.
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Demir, O.; Amaro, R. E. Elements of Nucleotide Specificity in the Trypanosoma brucei Mitochondrial RNA Editing Enzyme RET2 J. Chem. Inf. Model. 2012, 52, 1308– 1318
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Elements of Nucleotide Specificity in the Trypanosoma brucei Mitochondrial RNA Editing Enzyme RET2
Demir, Ozlem; Amaro, Rommie E.
Journal of Chemical Information and Modeling (2012), 52 (5), 1308-1318CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
The causative agent of African sleeping sickness, Trypanosoma brucei, undergoes an unusual mitochondrial RNA editing process that is essential for its survival. RNA editing terminal uridylyl transferase 2 of T. brucei (TbRET2) is an indispensable component of the editosome machinery that performs this editing. TbRET2 is required to maintain the vitality of both the insect and bloodstream forms of the parasite, and with its high-resoln. crystal structure, it poses as a promising pharmaceutical target. Neither the exclusive requirement of UTP for catalysis, nor the RNA primer preference of TbRET2 is well-understood. Using all-atom explicitly solvated mol. dynamics (MD) simulations, we investigated the effect of UTP binding on TbRET2 structure and dynamics, as well as the determinants governing TbRET2's exclusive UTP preference. Through our investigations of various nucleoside triphosphate substrates (NTPs), we show that UTP preorganizes the binding site through an extensive water-mediated H-bonding network, bringing Glu424 and Arg144 side chains to an optimum position for RNA primer binding. In contrast, cytosine 5'-triphosphate (CTP) and ATP cannot achieve this preorganization and thus preclude productive RNA primer binding. Addnl., we have located ligand-binding hot spots of TbRET2 based on the MD conformational ensembles and computational fragment mapping. TbRET2 reveals different binding pockets in the apo and UTP-bound MD simulations, which could be targeted for inhibitor design.
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Mowrey, D.; Cheng, M. H.; Liu, L. T.; Willenbring, D.; Lu, X. H.; Wymore, T.; Xu, Y.; Tang, P. Asymmetric Ligand Binding Facilitates Conformational Transitions in Pentameric Ligand-Gated Ion Channels J. Am. Chem. Soc. 2013, 135, 2172– 2180
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Bustamante, J. P.; Abbruzzetti, S.; Marcelli, A.; Gauto, D.; Boechi, L.; Bonamore, A.; Boffi, A.; Bruno, S.; Feis, A.; Foggi, P.; Estrin, D. A.; Viappiani, C. Ligand Uptake Modulation by Internal Water Molecules and Hydrophobic Cavities in Hemoglobins J. Phys. Chem. B 2014, 118, 1234– 1245
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Ligand Uptake Modulation by Internal Water Molecules and Hydrophobic Cavities in Hemoglobins
Bustamante, Juan P.; Abbruzzetti, Stefania; Marcelli, Agnese; Gauto, Diego; Boechi, Leonardo; Bonamore, Alessandra; Boffi, Alberto; Bruno, Stefano; Feis, Alessandro; Foggi, Paolo; Estrin, Dario A.; Viappiani, Cristiano
Journal of Physical Chemistry B (2014), 118 (5), 1234-1245CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
Internal water mols. play an active role in ligand uptake regulation, since displacement of retained water mols. from protein surfaces or cavities by incoming ligands can promote favorable or disfavorable effects over the global binding process. Detection of these water mols. by x-ray crystallog. is difficult given their positional disorder and low occupancy. In this work, we employ a combination of mol. dynamics simulations and ligand rebinding over a broad time range to shed light into the role of water mols. in ligand migration and binding. Computational studies on the unliganded structure of the thermostable truncated Hb from Thermobifida fusca (Tf-trHbO) show that a water mol. is in the vicinity of the iron heme, stabilized by WG8 with the assistance of YCD1, exerting a steric hindrance for binding of an exogenous ligand. Mutation of WG8 to F results in a significantly lower stabilization of this water mol. and in subtle dynamical structural changes that favor ligand binding, as obsd. exptl. Water is absent from the fully hydrophobic distal cavity of the triple mutant YB10F-YCD1F-WG8F (3F), due to the lack of residues capable of stabilizing it nearby the heme. In agreement with these effects on the barriers for ligand rebinding, over 97% of the photodissociated ligands are rebound within a few nanoseconds in the 3F mutant case. Our results demonstrate the specific involvement of water mols. in shaping the energetic barriers for ligand migration and binding.
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Selvam, B.; Porter, S. L.; Tikhonova, I. G. Addressing Selective Polypharmacology of Antipsychotic Drugs Targeting the Bioaminergic Receptors through Receptor Dynamic Conformational Ensembles J. Chem. Inf. Model. 2013, 53, 1761– 1774
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Addressing Selective Polypharmacology of Antipsychotic Drugs Targeting the Bioaminergic Receptors through Receptor Dynamic Conformational Ensembles
Selvam, Balaji; Porter, Simon L.; Tikhonova, Irina G.
Journal of Chemical Information and Modeling (2013), 53 (7), 1761-1774CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
Selective polypharmacol., where a drug acts on multiple rather than a single mol. target involved in a disease, emerges to develop a structure-based system biol. approach to design drugs selectively targeting a disease-active protein network. We focus on the bioaminergic receptors that belong to the group of G-protein-coupled receptors (GPCRs) and represent targets for therapeutic agents against schizophrenia and depression. Among them, it has been shown that the serotonin (5-HT2A and 5-HT6) and dopamine (D2 and D3) receptors induce a cognition-enhancing effect (group 1), while the histamine (H1) and serotonin (5-HT2C) receptors lead to metabolic side effects and the 5-HT2B serotonin receptor causes pulmonary hypertension (group 2). Thus, the problem arises to develop an approach that allows identifying drugs targeting only the disease-active receptors, i.e. group 1. The recent release of several crystal structures of the bioaminergic receptors, involving the D3 and H1 receptors, provides the possibility to model the structures of all receptors and initiate a study of the structural and dynamic context of selective polypharmacol. In this work, we use mol. dynamics simulations to generate a conformational space of the receptors and subsequently characterize its binding properties applying mol. probe mapping. All-against-all comparison of the generated probe maps of the selected diverse conformations of all receptors with the Tanimoto similarity coeff. (Tc) enable the sepn. of the receptors of group 1 from group 2. The pharmacophore built based on the Tc-selected receptor conformations, using the multiple probe maps discovers structural features that can be used to design mols. selective toward the receptors of group 1. The importance of several predicted residues to ligand selectivity is supported by the available mutagenesis and ligand structure-activity relationship studies. In addn., the Tc-selected conformations of the receptors for group 1 show good performance in isolation of known ligands from a random decoy. Our computational structure-based protocol to tackle selective polypharmacol. of antipsychotic drugs could be applied for other diseases involving multiple drug targets, such as oncol. and infectious disorders.
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Weinreb, V.; Li, L.; Chandrasekaran, S. N.; Koehl, P.; Delarue, M.; Carter, C. W., Jr. Enhanced Amino Acid Selection in Fully Evolved Tryptophanyl-tRNA Synthetase, Relative to Its Urzyme, Requires Domain Motion Sensed by the D1 Switch, a Remote Dynamic Packing Motif J. Biol. Chem. 2014, 289, 4367– 4376
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Li, J. N.; Jonsson, A. L.; Beuming, T.; Shelley, J. C.; Voth, G. A. Ligand-Dependent Activation and Deactivation of the Human Adenosine A(2A) Receptor J. Am. Chem. Soc. 2013, 135, 8749– 8759
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Ligand-Dependent Activation and Deactivation of the Human Adenosine A2A Receptor
Li, Jianing; Jonsson, Amanda L.; Beuming, Thijs; Shelley, John C.; Voth, Gregory A.
Journal of the American Chemical Society (2013), 135 (23), 8749-8759CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
G-protein-coupled receptors (GPCRs) are membrane proteins with crit. functions in cellular signal transduction, representing a primary class of drug targets. Acting by direct binding, many drugs modulate GPCR activity and influence the signaling pathways assocd. with numerous diseases. However, complete details of ligand-dependent GPCR activation/deactivation are difficult to obtain from expts. Therefore, it remains unclear how ligands modulate a GPCR's activity. To elucidate the ligand-dependent activation/deactivation mechanism of the human adenosine A2A receptor (AA2AR), a member of the class A GPCRs, we performed large-scale unbiased mol. dynamics and metadynamics simulations of the receptor embedded in a membrane. At the at. level, we have obsd. distinct structural states that resemble the active and inactive states. In particular, we noted key structural elements changing in a highly concerted fashion during the conformational transitions, including six conformational states of a tryptophan (Trp2466.48). Our findings agree with a previously proposed view that, during activation, this tryptophan residue undergoes a rotameric transition that may be coupled to a series of coherent conformational changes, resulting in the opening of the G-protein binding site. Further, metadynamics simulations provide quant. evidence for this mechanism, suggesting how ligand binding shifts the equil. between the active and inactive states. Our anal. also proposes that a few specific residues are assocd. with agonism/antagonism, affinity, and selectivity, and suggests that the ligand-binding pocket can be thought of as having three distinct regions, providing dynamic features for structure-based design. Addnl. simulations with AA2AR bound to a novel ligand are consistent with our proposed mechanism. Generally, our study provides insights into the ligand-dependent AA2AR activation/deactivation in addn. to what has been found in crystal structures. These results should aid in the discovery of more effective and selective GPCR ligands.
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Baron, R.; McCammon, J. A. Molecular Recognition and Ligand Association Annu. Rev. Phys. Chem. 2013, 64, 151– 175
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Molecular recognition and ligand association
Baron, Riccardo; McCammon, J. Andrew
Annual Review of Physical Chemistry (2013), 64 (), 151-175CODEN: ARPLAP; ISSN:0066-426X. (Annual Reviews Inc.)
We review recent developments in our understanding of mol. recognition and ligand assocn., focusing on two major viewpoints: (a) studies that highlight new phys. insight into the mol. recognition process and the driving forces detg. thermodn. signatures of binding and (b) recent methodol. advances in applications to protein-ligand binding. In particular, we highlight the challenges posed by compensating enthalpic and entropic terms, competing solute and solvent contributions, and the relevance of complex configurational ensembles comprising multiple protein, ligand, and solvent intermediate states. As more complete physics is taken into account, computational approaches increase their ability to complement exptl. measurements, by providing a microscopic, dynamic view of ensemble-averaged exptl. observables. Physics-based approaches are increasingly expanding their power in pharmacol. applications.
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Ariga, K.; Ito, H.; Hill, J. P.; Tsukube, H. Molecular recognition: from solution science to nano/materials technology Chem. Soc. Rev. 2012, 41, 5800– 5835
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Molecular recognition: from solution science to nano/materials technology
Ariga, Katsuhiko; Ito, Hiroshi; Hill, Jonathan P.; Tsukube, Hiroshi
Chemical Society Reviews (2012), 41 (17), 5800-5835CODEN: CSRVBR; ISSN:0306-0012. (Royal Society of Chemistry)
A review. In the 25 years since its Nobel Prize in chem., supramol. chem. based on mol. recognition has been paid much attention in scientific and technol. fields. Nanotechnol. and the related areas seek breakthrough methods of nanofabrication based on rational organization through assembly of constituent mols. Advanced biochem., medical applications, and environmental and energy technologies also depend on the importance of specific interactions between mols. In those current fields, mol. recognition is now being re-evaluated. In this review, we re-examine current trends in mol. recognition from the viewpoint of the surrounding media, that is (i) the soln. phase for development of basic science and mol. design advances; (ii) at nano/materials interfaces for emerging technologies and applications. The first section of this review includes mol. recognition frontiers, receptor design based on combinatorial approaches, org. capsule receptors, metallo-capsule receptors, helical receptors, dendrimer receptors, and the future design of receptor architectures. The following section summarizes topics related to mol. recognition at interfaces including fundamentals of mol. recognition, sensing and detection, structure formation, mol. machines, mol. recognition involving polymers and related materials, and mol. recognition processes in nanostructured materials.
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Kahraman, A.; Morris, R. J.; Laskowski, R. A.; Thornton, J. M. Shape variation in protein binding pockets and their ligands J. Mol. Biol. 2007, 368, 283– 301
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Shape Variation in Protein Binding Pockets and their Ligands
Kahraman, Abdullah; Morris, Richard J.; Laskowski, Roman A.; Thornton, Janet M.
Journal of Molecular Biology (2007), 368 (1), 283-301CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
A common assumption about the shape of protein binding pockets is that they are related to the shape of the small ligand mols. that can bind there. But to what extent is that assumption true Here we use a recently developed shape matching method to compare the shapes of protein binding pockets to the shapes of their ligands. We find that pockets binding the same ligand show greater variation in their shapes than can be accounted for by the conformational variability of the ligand. This suggests that geometrical complementarity in general is not sufficient to drive mol. recognition. Nevertheless, we show when considering only shape and size that a significant proportion of the recognition power of a binding pocket for its ligand resides in its shape. Addnl., we observe a "buffer zone" or a region of free space between the ligand and protein, which results in binding pockets being on av. three times larger than the ligand that they bind.
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Seddon, G.; Lounnas, V.; McGuire, R.; van den Bergh, T.; Bywater, R. P.; Oliveira, L.; Vriend, G. Drug design for ever, from hype to hope J. Comput.-Aided Mol. Des. 2012, 26, 137– 150
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Meng, X. Y.; Zhang, H. X.; Mezei, M.; Cui, M. Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery Curr. Comput.-Aided Drug Des. 2011, 7, 146– 157
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Molecular docking: A powerful approach for structure-based drug discovery
Meng, Xuan-Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng
Current Computer-Aided Drug Design (2011), 7 (2), 146-157CODEN: CCDDAS; ISSN:1573-4099. (Bentham Science Publishers Ltd.)
A review. Mol. docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available mol. docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor mol. docking approaches, esp. those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential soln. to flexible receptor docking problems. Three application examples of mol. docking approaches for drug discovery are provided.
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Golbraikh, A.; Wang, X. S.; Zhu, H.; Tropsha, A. Predictive QSAR Modeling: Methods and Applications in Drug Discovery and Chemical Risk Assessment. In Handbook of Computational Chemistry; Leszczynski, J., Ed.; Springer: Dordrecht, Netherlands, 2012; pp 1309– 1342.
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Liang, J.; Edelsbrunner, H.; Woodward, C. Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design Protein Sci. 1998, 7, 1884– 1897
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Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design
Liang, Jie; Edelsbrunner, Herbert; Woodward, Clare
Protein Science (1998), 7 (9), 1884-1897CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the vol. and area of pockets and cavities; and the area and circumference of mouth openings. CAST anal. of over 100 proteins has been carried out; proteins examd. include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a no. of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 102-103 Å3. Statistical anal. reveals that the no. of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a no. of instructive exceptions. Ligand vol. and binding site vol. are somewhat correlated when binding site vol. is ≤700 Å3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as addnl. binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Biol 1:55-58). Anal. of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Anal. of the FK506 binding protein, and of compds. developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST anal. of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 Å3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.
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Rush, T. S.; Grant, J. A.; Mosyak, L.; Nicholls, A. A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction J. Med. Chem. 2005, 48, 1489– 1495
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A Shape-Based 3-D Scaffold Hopping Method and Its Application to a Bacterial Protein-Protein Interaction
Rush, Thomas S., III; Grant, J. Andrew; Mosyak, Lidia; Nicholls, Anthony
Journal of Medicinal Chemistry (2005), 48 (5), 1489-1495CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
In this paper, the authors describe the first prospective application of the shape-comparison program ROCS (Rapid Overlay of Chem. Structures) to find new scaffolds for small mol. inhibitors of the ZipA-FtsZ protein-protein interaction, a proposed antibacterial target. The shape comparisons are made relative to the crystallog. detd., bioactive conformation of a high-throughput screening (HTS) hit. The use of ROCS led to the identification of a set of novel, weakly binding inhibitors with scaffolds presenting synthetic opportunities to further optimize biol. affinity and lacking development issues assocd. with the HTS lead. These ROCS-identified scaffolds would have been missed using other structural similarity approaches such as ISIS 2D fingerprints. X-ray crystallog. anal. of one of the new inhibitors bound to ZipA reveals that the shape comparison approach very accurately predicted the binding mode. These exptl. results validate this use of ROCS for chemotype switching or "lead hopping" and suggest that it is of general interest for lead identification in drug discovery endeavors.
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Wirth, M.; Volkamer, A.; Zoete, V.; Rippmann, F.; Michielin, O.; Rarey, M.; Sauer, W. H. B. Protein pocket and ligand shape comparison and its application in virtual screening J. Comput.-Aided Mol. Des. 2013, 27, 511– 524
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Hawkins, P. C. D.; Skillman, A. G.; Nicholls, A. Comparison of shape-matching and docking as virtual screening tools J. Med. Chem. 2007, 50, 74– 82
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Comparison of Shape-Matching and Docking as Virtual Screening Tools
Hawkins, Paul C. D.; Skillman, A. Geoffrey; Nicholls, Anthony
Journal of Medicinal Chemistry (2007), 50 (1), 74-82CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
Ligand docking is a widely used approach in virtual screening. In recent years a large no. of publications have appeared in which docking tools are compared and evaluated for their effectiveness in virtual screening against a wide variety of protein targets. These studies have shown that the effectiveness of docking in virtual screening is highly variable due to a large no. of possible confounding factors. Another class of method that has shown promise in virtual screening is the shape-based, ligand-centric approach. Several direct comparisons of docking with the shape-based tool ROCS have been conducted using data sets from some of these recent docking publications. The results show that a shape-based, ligand-centric approach is more consistent than, and often superior to, the protein-centric approach taken by docking.
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Distinto, S.; Esposito, F.; Kirchmair, J.; Cardia, M. C.; Gaspari, M.; Maccioni, E.; Alcaro, S.; Markt, P.; Wolber, G.; Zinzula, L.; Tramontano, E. Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach Eur. J. Med. Chem. 2012, 50, 216– 229
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LaLonde, J. M.; Elban, M. A.; Courter, J. R.; Sugawara, A.; Soeta, T.; Madani, N.; Princiotto, A. M.; Do Kwon, Y.; Kwong, P. D.; Schon, A.; Freire, E.; Sodroski, J.; Smith, A. B. Design, synthesis and biological evaluation of small molecule inhibitors of CD4-gp120 binding based on virtual screening Bioorg. Med. Chem. 2011, 19, 91– 101
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Tuccinardi, T.; Ortore, G.; Santos, M. A.; Marques, S. M.; Nuti, E.; Rossello, A.; Martinelli, A. Multitemplate Alignment Method for the Development of a Reliable 3D-QSAR Model for the Analysis of MMP3 Inhibitors J. Chem. Inf. Model. 2009, 49, 1715– 1724
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Multitemplate Alignment Method for the Development of a Reliable 3D-QSAR Model for the Analysis of MMP3 Inhibitors
Tuccinardi, Tiziano; Ortore, Gabriella; Santos, M. Amelia; Marques, Sergio M.; Nuti, Elisa; Rossello, Armando; Martinelli, Adriano
Journal of Chemical Information and Modeling (2009), 49 (7), 1715-1724CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
A ligand-based 3D-QSAR study for the identification of MMP3 inhibitors was developed by applying an innovative alignment method capable of taking into account information obtained from available X-ray MMP3 structures. Comparison of the obtained model with data recently published using a docking-based alignment method indicated that the ligand-based 3D-QSAR model provided better predictive ability. A second external test set of 106 MMP3 inhibitors further confirmed the predictive ability of the 3D-QSAR model. Finally, certain iminodiacetyl-based hydroxamate-benzenesulfonamide conjugates, which were predicted to be active by the 3D-QSAR model, were tested in vitro for MMP3 inhibition; some provided low nanomolar activity. As such, the results suggest that the multi-template alignment method is capable of improving the quality of 3D-QSAR models and therefore could be applied to the study of other systems. Furthermore, since MMP3 is an important target toward the treatment of arthritis, this model could be applied to the design of new active MMP3 inhibitors.
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Nicholls, A.; McGaughey, G. B.; Sheridan, R. P.; Good, A. C.; Warren, G.; Mathieu, M.; Muchmore, S. W.; Brown, S. P.; Grant, J. A.; Haigh, J. A.; Nevins, N.; Jain, A. N.; Kelley, B. Molecular Shape and Medicinal Chemistry: A Perspective J. Med. Chem. 2010, 53, 3862– 3886
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Molecular Shape and Medicinal Chemistry: A Perspective
Nicholls, Anthony; McGaughey, Georgia B.; Sheridan, Robert P.; Good, Andrew C.; Warren, Gregory; Mathieu, Magali; Muchmore, Steven W.; Brown, Scott P.; Grant, J. Andrew; Haigh, James A.; Nevins, Neysa; Jain, Ajay N.; Kelley, Brian
Journal of Medicinal Chemistry (2010), 53 (10), 3862-3886CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
A review article with 111 refs. summarized perspectives of mol. shape and medicinal chem. in drug screening.
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Osguthorpe, D. J.; Sherman, W.; Hagler, A. T. Exploring Protein Flexibility: Incorporating Structural Ensembles From Crystal Structures and Simulation into Virtual Screening Protocols J. Phys. Chem. B 2012, 116, 6952– 6959
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Osguthorpe, D. J.; Sherman, W.; Hagler, A. T. Generation of Receptor Structural Ensembles for Virtual Screening Using Binding Site Shape Analysis and Clustering Chem. Biol. Drug Des. 2012, 80, 182– 193
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Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering
Osguthorpe, David J.; Sherman, Woody; Hagler, Arnold T.
Chemical Biology & Drug Design (2012), 80 (2), 182-193CODEN: CBDDAL; ISSN:1747-0277. (Wiley-Blackwell)
Accounting for protein flexibility is an essential yet challenging component of structure-based virtual screening. Whereas an ideal approach would account for full protein and ligand flexibility during the virtual screening process, this is currently intractable using available computational resources. An alternative is ensemble docking, where calcns. are performed on a set of individual rigid receptor conformations and the results combined. The primary challenge assocd. with this approach is the choice of receptor structures to use for the docking calcns. In this work, we show that selection of a small set of structures based on clustering on binding site vol. overlaps provides an efficient and effective way to account for protein flexibility in virtual screening. We first apply the method to crystal structures of cyclin-dependent kinase 2 and HIV protease and show that virtual screening for ensembles of four cluster representative structures yields consistently high enrichments and diverse actives. We then apply the method to a structural ensemble of the androgen receptor generated with mol. dynamics and obtain results that are in agreement with those from the crystal structures of cyclin-dependent kinase 2 and HIV protease. This work provides a step forward in the incorporation of protein flexibility into structure-based virtual screening.
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Ben Nasr, N.; Guillemain, H.; Lagarde, N.; Zagury, J. F.; Montes, M. Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query J. Chem. Inf. Model. 2013, 53, 293– 311
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Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query
Ben Nasr, Nesrine; Guillemain, Helene; Lagarde, Nathalie; Zagury, Jean-Francois; Montes, Matthieu
Journal of Chemical Information and Modeling (2013), 53 (2), 293-311CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a ref., whose choice requires retrospective enrichment studies on benchmarking databases which consume addnl. resources. In the present study, the authors have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their vol. and opening, the authors show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site vol. is lower than 350 A3, the structure with the smaller vol. should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion addnl. to its vol. as the most closed structures should not be preferred in the large systems. These "binding site properties-based" guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available.
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Nichols, S. E.; Swift, R. V.; Amaro, R. E. Rational Prediction with Molecular Dynamics for Hit Identification Curr. Top. Med. Chem. 2012, 12, 2002– 2012
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Rational prediction with molecular dynamics for hit identification
Nichols, Sara E.; Swift, Robert V.; Amaro, Rommie E.
Current Topics in Medicinal Chemistry (Sharjah, United Arab Emirates) (2012), 12 (18), 2002-2012CODEN: CTMCCL; ISSN:1568-0266. (Bentham Science Publishers Ltd.)
A review. Although the motions of proteins are fundamental for their function, for pragmatic reasons, the consideration of protein elasticity has traditionally been neglected in drug discovery and design. This review details protein motion, its relevance to biomol. interactions and how it can be sampled using mol. dynamics simulations. Within this context, two major areas of research in structure-based prediction that can benefit from considering protein flexibility, binding site detection and mol. docking, are discussed. Basic classification metrics and statistical anal. techniques, which can facilitate performance anal., are also reviewed. With hardware and software advances, mol. dynamics in combination with traditional structure-based prediction methods can potentially reduce the time and costs involved in the hit identification pipeline.
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Ascher, D.; Dubois, P. F.; Hinsen, K.; James, J. H.; Oliphant, T. Numerical Python; UCRL-MA-128569 ed.; Lawrence Livermore National Laboratory: Livermore, CA, 1999.
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Dubois, P. F. Extending Python with Fortran Comput. Sci. Eng. 1999, 1, 66– 73
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Jones, E.; Oliphant, T.; Peterson, P. Others SciPy: Open Source Scientific Tools for Python, 0.11.0; 2001.
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Oliphant, T. E. Guide to NumPy; Brigham Young University: Provo, UT, 2006.
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Peterson, P. F2PY: a tool for connecting Fortran and Python programs Int. J. Comput. Sci. Eng. 2009, 4, 296– 305
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Humphrey, W.; Dalke, A.; Schulten, K. VMD: visual molecular dynamics J. Mol. Graphics 1996, 14, 33– 38
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VDM: visual molecular dynamics
Humphrey, William; Dalke, Andrew; Schulten, Klaus
Journal of Molecular Graphics (1996), 14 (1), 33-8, plates, 27-28CODEN: JMGRDV; ISSN:0263-7855. (Elsevier)
VMD is a mol. graphics program designed for the display and anal. of mol. assemblies, in particular, biopolymers such as proteins and nucleic acids. VMD can simultaneously display any no. of structures using a wide variety of rendering styles and coloring methods. Mols. are displayed as one or more "representations," in which each representation embodies a particular rendering method and coloring scheme for a selected subset of atoms. The atoms displayed in each representation are chosen using an extensive atom selection syntax, which includes Boolean operators and regular expressions. VMD provides a complete graphical user interface for program control, as well as a text interface using the Tcl embeddable parser to allow for complex scripts with variable substitution, control loops, and function calls. Full session logging is supported, which produces a VMD command script for later playback. High-resoln. raster images of displayed mols. may be produced by generating input scripts for use by a no. of photorealistic image-rendering applications. VMD has also been expressly designed with the ability to animate mol. dynamics (MD) simulation trajectories, imported either from files or from a direct connection to a running MD simulation. VMD is the visualization component of MDScope, a set of tools for interactive problem solving in structural biol., which also includes the parallel MD program NAMD, and the MDCOMM software used to connect the visualization and simulation programs, VMD is written in C++, using an object-oriented design; the program, including source code and extensive documentation, is freely available via anonymous ftp and through the World Wide Web.
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Akl, S. G.; Toussaint, G. T. In Efficient convex hull algorithms for pattern recognition applications, Proc. 4th. Int. Joint Conf. on Pattern Recognition (Kyoto, Japan), 1978; pp 483– 487.
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Deng, J.; Schnaufer, A.; Salavati, R.; Stuart, K. D.; Hol, W. G. High resolution crystal structure of a key editosome enzyme from Trypanosoma brucei: RNA editing ligase 1 J. Mol. Biol. 2004, 343, 601– 613
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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 2006, 65, 712– 725
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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.
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Horn, H. W.; Swope, W. C.; Pitera, J. W.; Madura, J. D.; Dick, T. J.; Hura, G. L.; Head-Gordon, T. Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew J. Chem. Phys. 2004, 120, 9665– 9678
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Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew
Horn, Hans W.; Swope, William C.; Pitera, Jed W.; Madura, Jeffry D.; Dick, Thomas J.; Hura, Greg L.; Head-Gordon, Teresa
Journal of Chemical Physics (2004), 120 (20), 9665-9678CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
A re-parameterization of the std. TIP4P water model for use with Ewald techniques is introduced, providing an overall global improvement in water properties relative to several popular nonpolarizable and polarizable water potentials. Using high precision simulations, and careful application of std. anal. corrections, we show that the new TIP4P-Ew potential has a d. max. at ∼1°, and reproduces exptl. bulk-densities and the enthalpy of vaporization, ΔHvap, from -37.5 to 127° at 1 atm with an abs. av. error of less than 1%. Structural properties are in very good agreement with X-ray scattering intensities at temps. between 0 and 77° and dynamical properties such as self-diffusion coeff. are in excellent agreement with expt. The parameterization approach used can be easily generalized to rehabilitate any water force field using available exptl. data over a range of thermodn. points.
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Meagher, K. L.; Redman, L. T.; Carlson, H. A. Development of polyphosphate parameters for use with the AMBER force field J. Comput. Chem. 2003, 24, 1016– 1025
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Development of polyphosphate parameters for use with the AMBER force field
Meagher, Kristin L.; Redman, Luke T.; Carlson, Heather A.
Journal of Computational Chemistry (2003), 24 (9), 1016-1025CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
Accurate force fields are essential for reproducing the conformational and dynamic behavior of condensed-phase systems. The popular AMBER force field has parameters for monophosphates, but they do not extend well to polyphorylated mols. such as ADP and ATP. This work presents parameters for the partial charges, atom types, bond angles, and torsions in simple polyphosphorylated compds. The parameters are based on MO calcns. of methyldiphosphate and methyltriphosphate at the RHF/6-31+G* level. The new parameters were fit to the entire potential energy surface (not just min.) with an RMSD of 0.62 kcal/mol. This is exceptional agreement and a significant improvement over the current parameters that produce a potential surface with an RMSD of 7.8 kcal/mol to that of the ab initio calcns. Testing has shown that the parameters are transferable and capable of reproducing the gas-phase conformations of inorg. diphosphate and triphosphate. Also, the parameters are an improvement over existing parameters in the condensed phase as shown by minimizations of ATP bound in several proteins. These parameters are intended for use with the existing AMBER 94/99 force field, and they will permit users to apply AMBER to a wider variety of important enzymic systems.
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Allner, O.; Nilsson, L.; Villa, A. Magnesium Ion-Water Coordination and Exchange in Biomolecular Simulations J. Chem. Theory Comput. 2012, 8, 1493– 1502
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Magnesium Ion-Water Coordination and Exchange in Biomolecular Simulations
Allner, Olof; Nilsson, Lennart; Villa, Alessandra
Journal of Chemical Theory and Computation (2012), 8 (4), 1493-1502CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
Magnesium ions have an important role in the structure and folding mechanism of RNA systems. To properly simulate these biophys. processes, the applied mol. models should reproduce, among other things, the kinetic properties of the ions in water soln. Here, the authors have studied the kinetics of the binding of magnesium ions with water mols. and nucleic acid systems using mol. dynamics simulation. The authors have validated the parameters used in biomol. force fields, such as AMBER and CHARMM, for Mg2+ ions and also for the biol. relevant ions Na+, K+, and Ca2+ together with three different water models (TIP3P, SPC/E, and TIP5P). Mg2+ ions have a slower exchange rate than Na+, K+, and Ca2+ in agreement with the exptl. trend, but the simulated value underestimates the exptl. obsd. Mg2+-water exchange rate by several orders of magnitude, irresp. of the force field and water model. A new set of parameters for Mg2+ was developed to reproduce the exptl. kinetic data. This set also leads to better reprodn. of structural data than existing models. The authors have applied the new parameter set to Mg2+ binding with a monophosphate model system and with the purine riboswitch, add A-riboswitch. In line with the Mg2+-water results, the newly developed parameters show a better description of the structure and kinetics of the Mg2+-phosphate binding than all other models. The characterization of the ion binding to the riboswitch system shows that the new parameter set does not affect the global structure of the RNA system or the no. of ions involved in direct or indirect binding. A slight decrease in the no. of water-bridged contacts between A-riboswitch and the Mg2+ ion is obsd. The results support the ability of the newly developed parameters to improve the kinetic description of the Mg2+ and phosphate ions and their applicability in nucleic acid simulation.
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Joung, I. S.; Cheatham, T. E. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations J. Phys. Chem. B 2008, 112, 9020– 9041
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Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations
Joung, In Suk; Cheatham, Thomas E.
Journal of Physical Chemistry B (2008), 112 (30), 9020-9041CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)
Alkali (Li+, Na+, K+, Rb+, and Cs+) and halide (F-, Cl-, Br-, and I-) ions play an important role in many biol. phenomena, roles that range from stabilization of biomol. structure, to influence on biomol. dynamics, to key physiol. influence on homeostasis and signaling. To properly model ionic interaction and stability in atomistic simulations of biomol. structure, dynamics, folding, catalysis, and function, an accurate model or representation of the monovalent ions is critically necessary. A good model needs to simultaneously reproduce many properties of ions, including their structure, dynamics, solvation, and moreover both the interactions of these ions with each other in the crystal and in soln. and the interactions of ions with other mols. At present, the best force fields for biomols. employ a simple additive, nonpolarizable, and pairwise potential for at. interaction. In this work, the authors describe their efforts to build better models of the monovalent ions within the pairwise Coulombic and 6-12 Lennard-Jones framework, where the models are tuned to balance crystal and soln. properties in Ewald simulations with specific choices of well-known water models. Although it has been clearly demonstrated that truly accurate treatments of ions will require inclusion of nonadditivity and polarizability (particularly with the anions) and ultimately even a quantum mech. treatment, the authors' goal was to simply push the limits of the additive treatments to see if a balanced model could be created. The applied methodol. is general and can be extended to other ions and to polarizable force-field models. The authors' starting point centered on observations from long simulations of biomols. in salt soln. with the AMBER force fields where salt crystals formed well below their soly. limit. The likely cause of the artifact in the AMBER parameters relates to the naive mixing of the Smith and Dang chloride parameters with AMBER-adapted Aqvist cation parameters. To provide a more appropriate balance, the authors reoptimized the parameters of the Lennard-Jones potential for the ions and specific choices of water models. To validate and optimize the parameters, the authors calcd. hydration free energies of the solvated ions and also lattice energies (LE) and lattice consts. (LC) of alkali halide salt crystals. This is the first effort that systematically scans across the Lennard-Jones space (well depth and radius) while balancing ion properties like LE and LC across all pair combinations of the alkali ions and halide ions. The optimization across the entire monovalent series avoids systematic deviations. The ion parameters developed, optimized, and characterized were targeted for use with some of the most commonly used rigid and nonpolarizable water models, specifically TIP3P, TIP4PEW, and SPC/E. In addn. to well reproducing the soln. and crystal properties, the new ion parameters well reproduce binding energies of the ions to water and the radii of the first hydration shells.
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Kale, L.; Skeel, R.; Bhandarkar, M.; Brunner, R.; Gursoy, A.; Krawetz, N.; Phillips, J.; Shinozaki, A.; Varadarajan, K.; Schulten, K. NAMD2: greater scalability for parallel molecular dynamics J. Comput. Phys. 1999, 151, 283– 312
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NAMD2: Greater Scalability for Parallel Molecular Dynamics
Kale, Laxmikant; Skeel, Robert; Bhandarkar, Milind; Brunner, Robert; Gursoy, Attila; Krawetz, Neal; Phillips, James; Shinozaki, Aritomo; Varadarajan, Krishnan; Schulten, Klaus
Journal of Computational Physics (1999), 151 (1), 283-312CODEN: JCTPAH; ISSN:0021-9991. (Academic Press)
Mol. dynamics programs simulate the behavior of biomol. systems, leading to understanding of their functions. However, the computational complexity of such simulations is enormous. Parallel machines provide the potential to meet this computational challenge. To harness this potential, it is necessary to develop a scalable program. It is also necessary that the program be easily modified by application-domain programmers. The NAMD2 program presented in this paper seeks to provide these desirable features. It uses spatial decompn. combined with force decompn. to enhance scalability. It uses intelligent periodic load balancing, so as to maximally utilize the available compute power. It is modularly organized, and implemented using Charm++, a parallel C++ dialect, so as to enhance its modifiability. It uses a combination of numerical techniques and algorithms to ensure that energy drifts are minimized, ensuring accuracy in long running calcns. NAMD2 uses a portable run-time framework called Converse that also supports interoperability among multiple parallel paradigms. As a result, different components of applications can be written in the most appropriate parallel paradigms. NAMD2 runs on most parallel machines including workstation clusters and has yielded speedups in excess of 180 on 220 processors. This paper also describes the performance obtained on some benchmark applications. (c) 1999 Academic Press.
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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
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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.
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Shipman, J. W. Tkinter reference: a GUI for Python; New Mexico Tech Computer Center: Socorro, NM, 2010.
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Welch, B. B.; Jones, K. Practical programming in Tcl/Tk, 4th ed.; Prentice Hall/PTR: Upper Saddle River, NJ, 2003.
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Schnaufer, A.; Panigrahi, A. K.; Panicucci, B.; Igo, R. P., Jr.; Salavati, R.; Stuart, K. An RNA Ligase Essential for RNA Editing and Survival of the Bloodstream Form of Trypanosoma brucei Science 2001, 291, 2159– 2162
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Rusche, L. N.; Huang, C. E.; Piller, K. J.; Hemann, M.; Wirtz, E.; Sollner-Webb, B. The two RNA ligases of the Trypanosoma brucei RNA editing complex: cloning the essential band IV gene and identifying the band V gene Mol. Cell. Biol. 2001, 21, 979– 989
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The two RNA ligases of the Trypanosoma brucei RNA editing complex: cloning the essential band IV gene and identifying the band V gene
Rusche, Laura N.; Huang, Catherine E.; Piller, Kenneth J.; Hemann, Michael; Wirtz, Elizabeth; Sollner-Webb, Barbara
Molecular and Cellular Biology (2001), 21 (4), 979-989CODEN: MCEBD4; ISSN:0270-7306. (American Society for Microbiology)
Kinetoplastid RNA editing is a posttranscriptional insertion and deletion of U residues in mitochondrial transcripts that involves RNA ligase. A complex of seven different polypeptides purified from Trypanosoma brucei mitochondria that catalyzes accurate RNA editing contains RNA ligases of ∼57 kDa (band IV) and ∼50 kDa (band V). From a partial amino acid sequence, cDNA and genomic clones of band IV were isolated, making it the first cloned component of the minimal RNA editing complex. It is indeed an RNA ligase, for when expressed in Escherichia coli, the protein autoadenylylates and catalyzes RNA joining. Overexpression studies revealed that T. brucei can regulate of total band IV protein at the level of translation or protein stability, even upon massively increased mRNA levels. The protein's mitochondrial targeting was confirmed by its location, size when expressed in T. brucei and E. coli, and N-terminal sequence. Importantly, genetic knockout studies demonstrated that the gene for band IV is essential in procyclic trypanosomes. The band IV and band V RNA ligases of the RNA editing complex therefore serve different functions. The authors also identified the gene for band V RNA ligase, a protein much more homologous to band IV than to other known ligases.
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Durrant, J. D.; Hall, L.; Swift, R. V.; Landon, M.; Schnaufer, A.; Amaro, R. E. Novel Naphthalene-Based Inhibitors of Trypanosoma brucei RNA Editing Ligase 1 PLoS Neglected Trop. Dis. 2010, 4, e803
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Durrant, J. D.; Friedman, A. J.; McCammon, J. A. CrystalDock: a novel approach to fragment-based drug design J. Chem. Inf. Model. 2011, 51, 2573– 2580
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CrystalDock: A Novel Approach to Fragment-Based Drug Design
Durrant, Jacob D.; Friedman, Aaron J.; McCammon, J. Andrew
Journal of Chemical Information and Modeling (2011), 51 (10), 2573-2580CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
We present a novel algorithm called CrystalDock that analyzes a mol. pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound exptl. structures. Germane fragments from the crystallog. or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, resp. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.
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Craig, I. R.; Pfleger, C.; Gohlke, H.; Essex, J. W.; Spiegel, K. Pocket-Space Maps To Identify Novel Binding-Site Conformations in Proteins J. Chem. Inf. Model. 2011, 51, 2666– 2679
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Pocket-Space Maps To Identify Novel Binding-Site Conformations in Proteins
Craig, Ian R.; Pfleger, Christopher; Gohlke, Holger; Essex, Jonathan W.; Spiegel, Katrin
Journal of Chemical Information and Modeling (2011), 51 (10), 2666-2679CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
The identification of novel binding-site conformations can greatly assist the progress of structure-based ligand design projects. Diverse pocket shapes drive medicinal chem. to explore a broader chem. space and thus present addnl. opportunities to overcome key drug discovery issues such as potency, selectivity, toxicity, and pharmacokinetics. We report a new automated approach to diverse pocket selection, PocketAnalyzerPCA, which applies principal component anal. and clustering to the output of a grid-based pocket detection algorithm. Since the approach works directly with pocket shape descriptors, it is free from some of the problems hampering methods that are based on proxy shape descriptors, e.g. a set of at. positional coordinates. The approach is tech. straightforward and allows simultaneous anal. of mutants, isoforms, and protein structures derived from multiple sources with different residue numbering schemes. The PocketAnalyzerPCA approach is illustrated by the compilation of diverse sets of pocket shapes for aldose reductase and viral neuraminidase. In both cases this allows identification of novel computationally derived binding-site conformations that are yet to be obsd. crystallog. Indeed, known inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby demonstrating the utility of PocketAnalyzerPCA for rationalizing and improving the understanding of the mol. basis of protein-ligand interaction and bioactivity. A Python program implementing the PocketAnalyzerPCA approach is available for download under an open-source license (http://sourceforge.net/projects/papca/ or http://cpclab.uni-duesseldorf.de/downloads).
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Ghersi, D.; Sanchez, R. Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures J. Struct. Funct. Genomics 2011, 12, 109– 117
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Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures
Ghersi, Dario; Sanchez, Roberto
Journal of Structural and Functional Genomics (2011), 12 (2), 109-117CODEN: JSFGAW; ISSN:1345-711X. (Springer)
A review. Structural genomics projects have revealed structures for a large no. of proteins of unknown function. Understanding the interactions between these proteins and their ligands would provide an initial step in their functional characterization. Binding site identification methods are a fast and cost-effective way to facilitate the characterization of functionally important protein regions. In this review we describe our recently developed methods for binding site identification in the context of existing methods. The advantage of energy-based approaches is emphasized, since they provide flexibility in the identification and characterization of different types of binding sites.
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Perot, S.; Sperandio, O.; Miteva, M. A.; Camproux, A. C.; Villoutreix, B. O. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery Drug Discovery Today 2010, 15, 656– 667
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Paramo, T.; East, A.; Garzon, D.; Ulmschneider, M. B.; Bond, P. J. Efficient Characterization of Protein Cavities within Molecular Simulation Trajectories: trj_cavity J. Chem. Theory Comput. 2014, 10, 2151– 2164
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Efficient Characterization of Protein Cavities within Molecular Simulation Trajectories: trj_cavity
Paramo, Teresa; East, Alexandra; Garzon, Diana; Ulmschneider, Martin B.; Bond, Peter J.
Journal of Chemical Theory and Computation (2014), 10 (5), 2151-2164CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
Protein cavities and tunnels are crit. in detg. phenomena such as ligand binding, mol. transport, and enzyme catalysis. Mol. dynamics (MD) simulations enable the exploration of the flexibility and conformational plasticity of protein cavities, extending the information available from static exptl. structures relevant to, for example, drug design. Here, the authors present a new tool (trj_cavity) implemented within the GROMACS (www.gromacs.org) framework for the rapid identification and characterization of cavities detected within MD trajectories. Trj_cavity is optimized for usability and computational efficiency and is applicable to the time-dependent anal. of any cavity topol., and optional specialized descriptors can be used to characterize, for example, protein channels. Its novel grid-based algorithm performs an efficient neighbor search whose calcn. time is linear with system size, and a comparison of performance with other widely used cavity anal. programs reveals an orders-of-magnitude improvement in the computational cost. To demonstrate its potential for revealing novel mechanistic insights, trj_cavity has been used to analyze long-time scale simulation trajectories for three diverse protein cavity systems. This has helped to reveal, resp., the lipid binding mechanism in the deep hydrophobic cavity of a sol. mite-allergen protein, Der p 2; a means for shuttling carbohydrates between the surface-exposed substrate-binding and catalytic pockets of a multidomain, membrane-proximal pullulanase, PulA; and the structural basis for selectivity in the transmembrane pore of a voltage-gated sodium channel (NavMs), embedded within a lipid bilayer environment. Trj_cavity is available for download under an open-source license (http://sourceforge.net/projects/trjcavity). A simplified, GROMACS-independent version may also be compiled.
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LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins
Hendlich M; Rippmann F; Barnickel G
Journal of molecular graphics & modelling (1997), 15 (6), 359-63, 389 ISSN:1093-3263.
LIGSITE is a new program for the automatic and time-efficient detection of pockets on the surface of proteins that may act as binding sites for small molecule ligands. Pockets are identified with a series of simple operations on a cubic grid. Using a set of receptor-ligand complexes we show that LIGSITE is able to identify the binding sites of small molecule ligands with high precision. The main advantage of LIGSITE is its speed. Typical search times are in the range of 5 to 20 s for medium-sized proteins. LIGSITE is therefore well suited for identification of pockets in large sets of proteins (e.g., protein families) for comparative studies. For graphical display LIGSITE produces VRML representations of the protein-ligand complex and the binding site for display with a VRML viewer such as WebSpace from SGI.
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Stahl, Martin; Taroni, Chiara; Schneider, Gisbert; Hoffmann, F.
Protein Engineering (2000), 13 (2), 83-88CODEN: PRENE9; ISSN:0269-2139. (Oxford University Press)
An automated computer-based method for mapping of protein surface cavities was developed and applied to a set of 176 metalloproteinases contg. Zn2+ cations in their active sites. With very few exceptions, the cavity search routine detected the active site among the 5 largest cavities and produced reasonable active site surfaces. Cavities were described by means of solvent-accessible surface patches. For a given protein, these patches were calcd. in three steps: (1) definition of cavity atoms forming surface cavities by a grid-based technique; (2) generation of solvent accessible surfaces; (3) assignment of an accessibility value and a generalized atom type to each surface point. Topol. correlation vectors were generated from the set of surface points forming the cavities, and projected onto the plane by a self-organizing network. The resulting map of 865 enzyme cavities displayed clusters of active sites that were clearly sepd. from the other cavities. It was demonstrated that both fully automated recognition of active sites, and prediction of enzyme class could be performed for novel protein structures at high accuracy.
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Zhihua Nie, Pi Liu, Yu Wang, Xuan Guo, Zijian Tan, Jie Shen, Zijing Tang, Jianping Lin, Jibin Sun, Ping Zheng, Leilei Zhu. Directed Evolution and Rational Design of Mechanosensitive Channel MscCG2 for Improved Glutamate Excretion Efficiency. Journal of Agricultural and Food Chemistry 2021, 69 (51) , 15660-15669. https://doi.org/10.1021/acs.jafc.1c07086
Xuan Wu, Yu Chen, Xin Wang, Wanqing Wei, Yong Liang. Origin of Site Selectivity in Toluene Hydroxylation by Cytochrome P450 Enzymes. The Journal of Organic Chemistry 2021, 86 (19) , 13768-13773. https://doi.org/10.1021/acs.joc.1c01295
Yuma Goto, Shun Omagari, Ryuma Sato, Takuya Yamakado, Ryo Achiwa, Nilanjan Dey, Kensuke Suga, Martin Vacha, Shohei Saito. Dynamic Polymer Free Volume Monitored by Single-Molecule Spectroscopy of a Dual Fluorescent Flapping Dopant. Journal of the American Chemical Society 2021, 143 (35) , 14306-14313. https://doi.org/10.1021/jacs.1c06428
Terra Sztain, Rommie Amaro, J. Andrew McCammon. Elucidation of Cryptic and Allosteric Pockets within the SARS-CoV-2 Main Protease. Journal of Chemical Information and Modeling 2021, 61 (7) , 3495-3501. https://doi.org/10.1021/acs.jcim.1c00140
Daniel J. Evans, Remy A. Yovanno, Sanim Rahman, David W. Cao, Morgan Q. Beckett, Milan H. Patel, Afif F. Bandak, Albert Y. Lau. Finding Druggable Sites in Proteins Using TACTICS. Journal of Chemical Information and Modeling 2021, 61 (6) , 2897-2910. https://doi.org/10.1021/acs.jcim.1c00204
Michel F. Sanner, Khalid Zoghebi, Samara Hanna, Saghar Mozaffari, Simin Rahighi, Rakesh K. Tiwari, Keykavous Parang. Cyclic Peptides as Protein Kinase Inhibitors: Structure–Activity Relationship and Molecular Modeling. Journal of Chemical Information and Modeling 2021, 61 (6) , 3015-3026. https://doi.org/10.1021/acs.jcim.1c00320
Sandra Codony, Carla Calvó-Tusell, Elena Valverde, Sílvia Osuna, Christophe Morisseau, M. Isabel Loza, José Brea, Concepción Pérez, María Isabel Rodríguez-Franco, Javier Pizarro-Delgado, Rubén Corpas, Christian Griñán-Ferré, Mercè Pallàs, Coral Sanfeliu, Manuel Vázquez-Carrera, Bruce D. Hammock, Ferran Feixas, Santiago Vázquez. From the Design to the In Vivo Evaluation of Benzohomoadamantane-Derived Soluble Epoxide Hydrolase Inhibitors for the Treatment of Acute Pancreatitis. Journal of Medicinal Chemistry 2021, 64 (9) , 5429-5446. https://doi.org/10.1021/acs.jmedchem.0c01601
Mrityunjay Singh, Mitul Srivastava, Sharad R. Wakode, Shailendra Asthana. Elucidation of Structural Determinants Delineates the Residues Playing Key Roles in Differential Dynamics and Selective Inhibition of Sirt1–3. Journal of Chemical Information and Modeling 2021, 61 (3) , 1105-1124. https://doi.org/10.1021/acs.jcim.0c01193
André Fischer, Florian Häuptli, Markus A. Lill, Martin Smieško. Computational Assessment of Combination Therapy of Androgen Receptor-Targeting Compounds. Journal of Chemical Information and Modeling 2021, 61 (2) , 1001-1009. https://doi.org/10.1021/acs.jcim.0c01194
André Fischer, Gabriela Frehner, Markus A. Lill, Martin Smieško. Conformational Changes of Thyroid Receptors in Response to Antagonists. Journal of Chemical Information and Modeling 2021, 61 (2) , 1010-1019. https://doi.org/10.1021/acs.jcim.0c01403
Ryan H. B. Smith, Zaigham M. Khan, Peter Man-Un Ung, Alex P. Scopton, Lisa Silber, Seshat M. Mack, Alexander M. Real, Avner Schlessinger, Arvin C. Dar. Type II Binders Targeting the “GLR-Out” Conformation of the Pseudokinase STRADα. Biochemistry 2021, 60 (4) , 289-302. https://doi.org/10.1021/acs.biochem.0c00714
Zi-Jing Zhao, Xin-Ying Gao, Jia-Cheng Zeng, Shao-Long Zhang, Xian-Mai Meng, Yan-Jun Shen, Xie-Huang Sheng. Theoretical Insights into the Cotransport Mechanism of GSH with Anticancer Drugs by MRP1. The Journal of Physical Chemistry B 2020, 124 (44) , 9803-9811. https://doi.org/10.1021/acs.jpcb.0c06662
Jingyuan Zhuang, Fan Zhang, Jingwei Zhou, Wenbin Deng, Ruibo Wu. Residue-Orientation-Dependent Dynamics and Selectivity of Active Pocket in Microbe Class I Terpene Cyclases. Journal of Chemical Information and Modeling 2020, 60 (10) , 4985-4994. https://doi.org/10.1021/acs.jcim.0c00159
Sandra Codony, Eugènia Pujol, Javier Pizarro, Ferran Feixas, Elena Valverde, M. Isabel Loza, José M. Brea, Elena Saez, Julen Oyarzabal, Antonio Pineda-Lucena, Belén Pérez, Concepción Pérez, María Isabel Rodríguez-Franco, Rosana Leiva, Sílvia Osuna, Christophe Morisseau, Bruce D. Hammock, Manuel Vázquez-Carrera, Santiago Vázquez. 2-Oxaadamant-1-yl Ureas as Soluble Epoxide Hydrolase Inhibitors: In Vivo Evaluation in a Murine Model of Acute Pancreatitis. Journal of Medicinal Chemistry 2020, 63 (17) , 9237-9257. https://doi.org/10.1021/acs.jmedchem.0c00310
Guido Todde, Ran Friedman. Pattern and Dynamics of FLT3 Duplications. Journal of Chemical Information and Modeling 2020, 60 (8) , 4005-4020. https://doi.org/10.1021/acs.jcim.0c00544
Dimitris Gazgalis, Mehreen Zaka, Bilal Haider Abbasi, Diomedes E. Logothetis, Mihaly Mezei, Meng Cui. Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery. ACS Omega 2020, 5 (24) , 14297-14307. https://doi.org/10.1021/acsomega.0c00522
Jacob D. Durrant, Sarah E. Kochanek, Lorenzo Casalino, Pek U. Ieong, Abigail C. Dommer, Rommie E. Amaro. Mesoscale All-Atom Influenza Virus Simulations Suggest New Substrate Binding Mechanism. ACS Central Science 2020, 6 (2) , 189-196. https://doi.org/10.1021/acscentsci.9b01071
Alexander J. Bryer, Jodi A. Hadden-Perilla, John E. Stone, Juan R. Perilla. High-Performance Analysis of Biomolecular Containers to Measure Small-Molecule Transport, Transbilayer Lipid Diffusion, and Protein Cavities. Journal of Chemical Information and Modeling 2019, 59 (10) , 4328-4338. https://doi.org/10.1021/acs.jcim.9b00324
Emilia P. Barros, Jamie M. Schiffer, Anastassia Vorobieva, Jiayi Dou, David Baker, Rommie E. Amaro. Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations. Journal of Chemical Theory and Computation 2019, 15 (10) , 5703-5715. https://doi.org/10.1021/acs.jctc.9b00483
Damien Monet, Nathan Desdouits, Michael Nilges, Arnaud Blondel. mkgridXf: Consistent Identification of Plausible Binding Sites Despite the Elusive Nature of Cavities and Grooves in Protein Dynamics. Journal of Chemical Information and Modeling 2019, 59 (8) , 3506-3518. https://doi.org/10.1021/acs.jcim.9b00103
Sami T. Kurkinen, Sakari Lätti, Olli T. Pentikäinen, Pekka A. Postila. Getting Docking into Shape Using Negative Image-Based Rescoring. Journal of Chemical Information and Modeling 2019, 59 (8) , 3584-3599. https://doi.org/10.1021/acs.jcim.9b00383
Ana R. Calixto, Cátia Moreira, Anna Pabis, Carsten Kötting, Klaus Gerwert, Till Rudack, Shina C.L. Kamerlin. GTP Hydrolysis Without an Active Site Base: A Unifying Mechanism for Ras and Related GTPases. Journal of the American Chemical Society 2019, 141 (27) , 10684-10701. https://doi.org/10.1021/jacs.9b03193
Guido Todde, Ran Friedman. Activation and Inactivation of the FLT3 Kinase: Pathway Intermediates and the Free Energy of Transition. The Journal of Physical Chemistry B 2019, 123 (26) , 5385-5394. https://doi.org/10.1021/acs.jpcb.9b01567
Jeffrey R. Wagner, Özlem Demir, Michael A. Carpenter, Hideki Aihara, Daniel A. Harki, Reuben S. Harris, Rommie E. Amaro. Determinants of Oligonucleotide Selectivity of APOBEC3B. Journal of Chemical Information and Modeling 2019, 59 (5) , 2264-2273. https://doi.org/10.1021/acs.jcim.8b00427
Rodrigo Cossio-Pérez, Gustavo Pierdominici-Sottile, Pablo Sobrado, Juliana Palma. Molecular Dynamics Simulations of Substrate Release from Trypanosoma cruzi UDP-Galactopyranose Mutase. Journal of Chemical Information and Modeling 2019, 59 (2) , 809-817. https://doi.org/10.1021/acs.jcim.8b00675
Ge Qu, Mingxing Fu, Lili Zhao, Beibei Liu, Pi Liu, Wenchao Fan, Jun-An Ma, Zhoutong Sun. Computational Insights into the Catalytic Mechanism of Bacterial Carboxylic Acid Reductase. Journal of Chemical Information and Modeling 2019, 59 (2) , 832-841. https://doi.org/10.1021/acs.jcim.8b00763
Yonca Ural-Blimke, Ali Flayhan, Jan Strauss, Vasileios Rantos, Kim Bartels, Rolf Nielsen, Els Pardon, Jan Steyaert, Jan Kosinski, Esben M. Quistgaard, Christian Löw. Structure of Prototypic Peptide Transporter DtpA from E. coli in Complex with Valganciclovir Provides Insights into Drug Binding of Human PepT1. Journal of the American Chemical Society 2019, 141 (6) , 2404-2412. https://doi.org/10.1021/jacs.8b11343
Clarisse G. Ricci, J. Andrew McCammon. Heterogeneous Solvation in Distinctive Protein–Protein Interfaces Revealed by Molecular Dynamics Simulations. The Journal of Physical Chemistry B 2018, 122 (49) , 11695-11701. https://doi.org/10.1021/acs.jpcb.8b07773
Tavina L. Offutt, Pek U. Ieong, Özlem Demir, Rommie E. Amaro. Dynamics and Molecular Mechanisms of p53 Transcriptional Activation. Biochemistry 2018, 57 (46) , 6528-6537. https://doi.org/10.1021/acs.biochem.8b01005
Pengfei Li, Alexander V. Soudackov, Sharon Hammes-Schiffer. Impact of Mutations on the Binding Pocket of Soybean Lipoxygenase: Implications for Proton-Coupled Electron Transfer. The Journal of Physical Chemistry Letters 2018, 9 (22) , 6444-6449. https://doi.org/10.1021/acs.jpclett.8b02945
Lingyun Wang, Feng Yan. Trans and Cis Conformations of the Antihypertensive Drug Valsartan Respectively Lock the Inactive and Active-like States of Angiotensin II Type 1 Receptor: A Molecular Dynamics Study. Journal of Chemical Information and Modeling 2018, 58 (10) , 2123-2130. https://doi.org/10.1021/acs.jcim.8b00364
André Fischer, Charleen G. Don, Martin Smieško. Molecular Dynamics Simulations Reveal Structural Differences among Allelic Variants of Membrane-Anchored Cytochrome P450 2D6. Journal of Chemical Information and Modeling 2018, 58 (9) , 1962-1975. https://doi.org/10.1021/acs.jcim.8b00080
Bing Xie, John D. Clark, David D. L. Minh. Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles. Journal of Chemical Information and Modeling 2018, 58 (9) , 1915-1925. https://doi.org/10.1021/acs.jcim.8b00314
Fu-Long Li, Xu-Dong Kong, Qi Chen, Yu-Cong Zheng, Qin Xu, Fei-Fei Chen, Li-Qiang Fan, Guo-Qiang Lin, Jiahai Zhou, Hui-Lei Yu, Jian-He Xu. Regioselectivity Engineering of Epoxide Hydrolase: Near-Perfect Enantioconvergence through a Single Site Mutation. ACS Catalysis 2018, 8 (9) , 8314-8317. https://doi.org/10.1021/acscatal.8b02622
Yao Nie, Shanshan Wang, Yan Xu, Shenggan Luo, Yi-Lei Zhao, Rong Xiao, Gaetano T. Montelione, John F. Hunt, Thomas Szyperski. Enzyme Engineering Based on X-ray Structures and Kinetic Profiling of Substrate Libraries: Alcohol Dehydrogenases for Stereospecific Synthesis of a Broad Range of Chiral Alcohols. ACS Catalysis 2018, 8 (6) , 5145-5152. https://doi.org/10.1021/acscatal.8b00364
Ting Shi, Lanxuan Liu, Wentao Tao, Shenggan Luo, Shuobing Fan, Xiao-Lei Wang, Linquan Bai, Yi-Lei Zhao. Theoretical Studies on the Catalytic Mechanism and Substrate Diversity for Macrocyclization of Pikromycin Thioesterase. ACS Catalysis 2018, 8 (5) , 4323-4332. https://doi.org/10.1021/acscatal.8b01156
Fangfang Fan, Nanhao Chen, Yongheng Wang, Ruibo Wu, and Zexing Cao . QM/MM and MM MD Simulations on the Pyrimidine-Specific Nucleoside Hydrolase: A Comprehensive Understanding of Enzymatic Hydrolysis of Uridine. The Journal of Physical Chemistry B 2018, 122 (3) , 1121-1131. https://doi.org/10.1021/acs.jpcb.7b10524
Zhoutong Sun, Lian Wu, Marco Bocola, H. C. Stephen Chan, Richard Lonsdale, Xu-Dong Kong, Shuguang Yuan, Jiahai Zhou, and Manfred T. Reetz . Structural and Computational Insight into the Catalytic Mechanism of Limonene Epoxide Hydrolase Mutants in Stereoselective Transformations. Journal of the American Chemical Society 2018, 140 (1) , 310-318. https://doi.org/10.1021/jacs.7b10278
Claire Colas, Masayuki Masuda, Kazuaki Sugio, Seiji Miyauchi, Yongjun Hu, David E. Smith, and Avner Schlessinger . Chemical Modulation of the Human Oligopeptide Transporter 1, hPepT1. Molecular Pharmaceutics 2017, 14 (12) , 4685-4693. https://doi.org/10.1021/acs.molpharmaceut.7b00775
Giuseppina La Sala, Sergio Decherchi, Marco De Vivo, and Walter Rocchia . Allosteric Communication Networks in Proteins Revealed through Pocket Crosstalk Analysis. ACS Central Science 2017, 3 (9) , 949-960. https://doi.org/10.1021/acscentsci.7b00211
Jeffrey R. Wagner, Jesper Sørensen, Nathan Hensley, Celia Wong, Clare Zhu, Taylor Perison, and Rommie E. Amaro . POVME 3.0: Software for Mapping Binding Pocket Flexibility. Journal of Chemical Theory and Computation 2017, 13 (9) , 4584-4592. https://doi.org/10.1021/acs.jctc.7b00500
Stefan G. Krimmer, Jonathan Cramer, Johannes Schiebel, Andreas Heine, and Gerhard Klebe . How Nothing Boosts Affinity: Hydrophobic Ligand Binding to the Virtually Vacated S1′ Pocket of Thermolysin. Journal of the American Chemical Society 2017, 139 (30) , 10419-10431. https://doi.org/10.1021/jacs.7b05028
Stefano Motta and Laura Bonati . Modeling Binding with Large Conformational Changes: Key Points in Ensemble-Docking Approaches. Journal of Chemical Information and Modeling 2017, 57 (7) , 1563-1578. https://doi.org/10.1021/acs.jcim.7b00125
Lane W. Votapka, Benjamin R. Jagger, Alexandra L. Heyneman, and Rommie E. Amaro . SEEKR: Simulation Enabled Estimation of Kinetic Rates, A Computational Tool to Estimate Molecular Kinetics and Its Application to Trypsin–Benzamidine Binding. The Journal of Physical Chemistry B 2017, 121 (15) , 3597-3606. https://doi.org/10.1021/acs.jpcb.6b09388
Tavina L. Offutt, Robert V. Swift, and Rommie E. Amaro . Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations. Journal of Chemical Information and Modeling 2016, 56 (10) , 1923-1935. https://doi.org/10.1021/acs.jcim.6b00261
Peter Man-Un Ung, Wenxin Song, Lili Cheng, Xinbin Zhao, Hailin Hu, Ligong Chen, and Avner Schlessinger . Inhibitor Discovery for the Human GLUT1 from Homology Modeling and Virtual Screening. ACS Chemical Biology 2016, 11 (7) , 1908-1916. https://doi.org/10.1021/acschembio.6b00304
Jeffrey R. Wagner, Christopher T. Lee, Jacob D. Durrant, Robert D. Malmstrom, Victoria A. Feher, and Rommie E. Amaro . Emerging Computational Methods for the Rational Discovery of Allosteric Drugs. Chemical Reviews 2016, 116 (11) , 6370-6390. https://doi.org/10.1021/acs.chemrev.5b00631
Robert V. Swift, Siti A. Jusoh, Tavina L. Offutt, Eric S. Li, and Rommie E. Amaro . Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles. Journal of Chemical Information and Modeling 2016, 56 (5) , 830-842. https://doi.org/10.1021/acs.jcim.5b00684
Lucía Álvarez, Ariel Lewis-Ballester, Adrián Roitberg, Darío A. Estrin, Syun-Ru Yeh, Marcelo A. Marti, Luciana Capece. Structural Study of a Flexible Active Site Loop in Human Indoleamine 2,3-Dioxygenase and Its Functional Implications. Biochemistry 2016, 55 (19) , 2785-2793. https://doi.org/10.1021/acs.biochem.6b00077
Kevin K. K. Leung and Brian H. Shilton . Binding of DNA-Intercalating Agents to Oxidized and Reduced Quinone Reductase 2. Biochemistry 2015, 54 (51) , 7438-7448. https://doi.org/10.1021/acs.biochem.5b00884
Cheng Shang, Chris S. Whittleston, Kyle H. Sutherland-Cash, and David J. Wales . Analysis of the Contrasting Pathogenicities Induced by the D222G Mutation in 1918 and 2009 Pandemic Influenza A Viruses. Journal of Chemical Theory and Computation 2015, 11 (5) , 2307-2314. https://doi.org/10.1021/ct5010565
Sirish Kaushik Lakkaraju, Wenbo Yu, E. Prabhu Raman, Alena V. Hershfeld, Lei Fang, Deepak A. Deshpande, and Alexander D. MacKerell, Jr. . Mapping Functional Group Free Energy Patterns at Protein Occluded Sites: Nuclear Receptors and G-Protein Coupled Receptors. Journal of Chemical Information and Modeling 2015, 55 (3) , 700-708. https://doi.org/10.1021/ci500729k
Rajesh Singh, Navjeet Ahalawat, and Rajesh K. Murarka . Activation of Corticotropin-Releasing Factor 1 Receptor: Insights from Molecular Dynamics Simulations. The Journal of Physical Chemistry B 2015, 119 (7) , 2806-2817. https://doi.org/10.1021/jp509814n
Paola Moyano-Gómez, Jukka V. Lehtonen, Olli T. Pentikäinen, Pekka A. Postila. Building shape-focused pharmacophore models for effective docking screening. Journal of Cheminformatics 2024, 16 (1) https://doi.org/10.1186/s13321-024-00857-6
Zhi-Zheng Wang, Min-Jie Cao, Junjie Yan, Jin Dong, Mo-Xian Chen, Jing-Fang Yang, Jian-Hong Li, Rui-Ning Ying, Yang-Yang Gao, Li Li, Ya-Nan Leng, Yuan Tian, Kamalani Achala H. Hewage, Rong-Jie Pei, Zhi-You Huang, Ping Yin, Jian-Kang Zhu, Ge-Fei Hao, Guang-Fu Yang. Stabilization of dimeric PYR/PYL/RCAR family members relieves abscisic acid-induced inhibition of seed germination. Nature Communications 2024, 15 (1) https://doi.org/10.1038/s41467-024-52426-y
Yiyang Sun, Yinian Tang, Jing Zhou, Bingchen Guo, Feiyan Yuan, Bo Yao, Yang Yu, Chun Li. Computational design of myoglobin-based carbene transferases for monoterpene derivatization. Biochemical and Biophysical Research Communications 2024, 722 , 150160. https://doi.org/10.1016/j.bbrc.2024.150160
Baerlike Wujieti, Xinping Feng, Erxia Liu, Deqing Li, Mingtian Hao, Luqi Zhou, Wei Cui. A theoretical study on the activity and selectivity of IDO/TDO inhibitors. Physical Chemistry Chemical Physics 2024, 26 (23) , 16747-16764. https://doi.org/10.1039/D3CP06036E
A. W. Peshala Amarajeewa, Aslihan Özcan, Alveena Mukhtiar, Xu Ren, Qianyu Wang, Pemra Ozbek, Malgorzata A. Garstka, Onur Serçinoğlu. Polymorphism in F pocket affects peptide selection and stability of type 1 diabetes‐associated HLA‐B39 allotypes. European Journal of Immunology 2024, 163 https://doi.org/10.1002/eji.202350683
Gustavo Avelar Molina, Luis Felipe Santos Mendes, Carlos Alessandro Fuzo, Antonio José Costa‐Filho, Richard John Ward. Mapping secondary substrate‐binding sites on the GH11 xylanase from Bacillus subtilis. FEBS Letters 2024, 598 (3) , 363-376. https://doi.org/10.1002/1873-3468.14799
Anupam Anand Ojha, Lane William Votapka, Rommie Elizabeth Amaro. QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations. Chemical Science 2023, 14 (45) , 13159-13175. https://doi.org/10.1039/D3SC04195F
Arie van Wieren, Jacob D. Durrant, Sudipta Majumdar. Computational and experimental analyses of alanine racemase suggest new avenues for developing allosteric small‐molecule antibiotics. Drug Development Research 2023, 84 (5) , 999-1007. https://doi.org/10.1002/ddr.22068
Zhongbiao Tan, Gang Chen, Silu Chen, Jiaoyang Zhang, Junjie Liu, Xincheng Ma, Hualan Liao, Zhixuan Hu, Feiyin Ge, Fang Ju, Hao Shi, Muhammad Bilal. Engineering lipase at the molecular scale for cleaner biodiesel production - A review. Molecular Catalysis 2023, 546 , 113271. https://doi.org/10.1016/j.mcat.2023.113271
Belal O. Al-Najjar, Fadi G. Saqallah. Unlocking the Conformational Changes of P2Y12: Exploring an Acridinone Compound’s Effect on Receptor Activity and Conformation. Molecules 2023, 28 (9) , 3878. https://doi.org/10.3390/molecules28093878
Ashish Kumar Agrahari, Mitul Srivastava, Mrityunjay Singh, Shailendra Asthana. SARS-CoV-2 envelope protein attain K ac mediated dynamical interaction network to adopt ‘histone mimic’ at BRD4 interface. Journal of Biomolecular Structure and Dynamics 2023, 2007 , 1-15. https://doi.org/10.1080/07391102.2023.2188430
Ali Rasouli, Qin Yu, Sepehr Dehghani-Ghahnaviyeh, Po-Chao Wen, Julia Kowal, Kaspar P. Locher, Emad Tajkhorshid. Differential dynamics and direct interaction of bound ligands with lipids in multidrug transporter ABCG2. Proceedings of the National Academy of Sciences 2023, 120 (1) https://doi.org/10.1073/pnas.2213437120
Amar Pratap Singh, Rakesh Kumar, Dinesh Gupta. Structural insights into the mechanism of human methyltransferase hPRMT4. Journal of Biomolecular Structure and Dynamics 2022, 40 (21) , 10821-10834. https://doi.org/10.1080/07391102.2021.1950567
Jia Duan, Dan-Dan Shen, Tingting Zhao, Shimeng Guo, Xinheng He, Wanchao Yin, Peiyu Xu, Yujie Ji, Li-Nan Chen, Jinyu Liu, Huibing Zhang, Qiufeng Liu, Yi Shi, Xi Cheng, Hualiang Jiang, H. Eric Xu, Yan Zhang, Xin Xie, Yi Jiang. Molecular basis for allosteric agonism and G protein subtype selectivity of galanin receptors. Nature Communications 2022, 13 (1) https://doi.org/10.1038/s41467-022-29072-3
Jingjing Wang, Meng Wu, Zhangcheng Chen, Lijie Wu, Tian Wang, Dongmei Cao, Huan Wang, Shenhui Liu, Yueming Xu, Fei Li, Junlin Liu, Na Chen, Suwen Zhao, Jianjun Cheng, Sheng Wang, Tian Hua. The unconventional activation of the muscarinic acetylcholine receptor M4R by diverse ligands. Nature Communications 2022, 13 (1) https://doi.org/10.1038/s41467-022-30595-y
Yann Waltenspühl, Janosch Ehrenmann, Santiago Vacca, Cristian Thom, Ohad Medalia, Andreas Plückthun. Structural basis for the activation and ligand recognition of the human oxytocin receptor. Nature Communications 2022, 13 (1) https://doi.org/10.1038/s41467-022-31325-0
Àlex L. González, Daniel Fernández-Remacha, José Ignacio Borrell, Jordi Teixidó, Roger Estrada-Tejedor. Cognate RNA-Binding Modes by the Alternative-Splicing Regulator MBNL1 Inferred from Molecular Dynamics. International Journal of Molecular Sciences 2022, 23 (24) , 16147. https://doi.org/10.3390/ijms232416147
Anita Kumari, Lovika Mittal, Mitul Srivastava, Shailendra Asthana. Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations. Journal of Biomolecular Structure and Dynamics 2022, 40 (19) , 9287-9305. https://doi.org/10.1080/07391102.2021.1927844
Vishnu Nayak Badavath, Abhishek Thakur, Deepak Shilkar, Chandrani Nath, Orlando Acevedo, Gulberk Ucar, Venkatesan Jayaprakash. Brain permeable curcumin-based pyrazoline analogs: MAO inhibitory and antioxidant activity. Journal of Molecular Structure 2022, 1268 , 133681. https://doi.org/10.1016/j.molstruc.2022.133681
Lingyun Wang, Lingling Wang, Feng Yan. Understanding the molecular mechanism of endothelin ETA receptor selecting isopeptides endothelin-1 and -3. Biophysical Journal 2022, 121 (13) , 2490-2502. https://doi.org/10.1016/j.bpj.2022.06.006
Erich Hellemann, Jennifer L. Walker, Mitchell A. Lesko, Dakshayini G. Chandrashekarappa, Martin C. Schmidt, Allyson F. O’Donnell, Jacob D. Durrant, . Novel mutation in hexokinase 2 confers resistance to 2-deoxyglucose by altering protein dynamics. PLOS Computational Biology 2022, 18 (3) , e1009929. https://doi.org/10.1371/journal.pcbi.1009929
David Horn, . A profile of research on the parasitic trypanosomatids and the diseases they cause. PLOS Neglected Tropical Diseases 2022, 16 (1) , e0010040. https://doi.org/10.1371/journal.pntd.0010040
Xinheng He, Jia Duan, Yujie Ji, Lifen Zhao, Hualiang Jiang, Yi Jiang, H. Eric Xu, Xi Cheng. Hinge region mediates signal transmission of luteinizing hormone and chorionic gonadotropin receptor. Computational and Structural Biotechnology Journal 2022, 20 , 6503-6511. https://doi.org/10.1016/j.csbj.2022.11.039
Quynh N. Vo, Paween Mahinthichaichan, Jana Shen, Christopher R. Ellis. How μ-opioid receptor recognizes fentanyl. Nature Communications 2021, 12 (1) https://doi.org/10.1038/s41467-021-21262-9
Hyo Jin Gim, Jiyong Park, Michael E. Jung, K. N. Houk. Conformational dynamics of androgen receptors bound to agonists and antagonists. Scientific Reports 2021, 11 (1) https://doi.org/10.1038/s41598-021-94707-2
Marius Gonse Zoh, Jean-Marc Bonneville, Jordan Tutagata, Frederic Laporte, Behi K. Fodjo, Chouaibou S. Mouhamadou, Christabelle Gba Sadia, Justin McBeath, Frederic Schmitt, Sebastian Horstmann, Stephane Reynaud, Jean-Philippe David. Experimental evolution supports the potential of neonicotinoid-pyrethroid combination for managing insecticide resistance in malaria vectors. Scientific Reports 2021, 11 (1) https://doi.org/10.1038/s41598-021-99061-x
Wei Xiong, Bo Liu, Yujiao Shen, Keju Jing, Thomas R. Savage. Protein engineering design from directed evolution to de novo synthesis. Biochemical Engineering Journal 2021, 174 , 108096. https://doi.org/10.1016/j.bej.2021.108096
Jiming Chen, Alexandra White, David C. Nelson, Diwakar Shukla. Role of substrate recognition in modulating strigolactone receptor selectivity in witchweed. Journal of Biological Chemistry 2021, 297 (4) , 101092. https://doi.org/10.1016/j.jbc.2021.101092
Qing-Hu Mo, Meng-Qi Yan, Xian-Li Zhou, Qin Luo, Xi-Shan Huang, Cheng-Qin Liang. Phloroglucinol derivatives rhotomensones A-G from Rhodomyrtus tomentosa. Phytochemistry 2021, 190 , 112890. https://doi.org/10.1016/j.phytochem.2021.112890
Tao Cai, Hongbing Sun, Jing Qiao, Leilei Zhu, Fan Zhang, Jie Zhang, Zijing Tang, Xinlei Wei, Jiangang Yang, Qianqian Yuan, Wangyin Wang, Xue Yang, Huanyu Chu, Qian Wang, Chun You, Hongwu Ma, Yuanxia Sun, Yin Li, Can Li, Huifeng Jiang, Qinhong Wang, Yanhe Ma. Cell-free chemoenzymatic starch synthesis from carbon dioxide. Science 2021, 373 (6562) , 1523-1527. https://doi.org/10.1126/science.abh4049
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Abstract
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Figure 1
Figure 1. POVME 2.0 graphical user interface.
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Figure 2
Figure 2. A graphical summary of the POVME 2.0 algorithm. A) The user defines an inclusion region. B) The user defines an exclusion region. C) The portion of the inclusion region that is not also in the exclusion region is flooded with equidistant points. D) Any of the points that are close to receptor atoms are deleted. E) Any points outside the convex hull are optionally deleted. F) The user can optionally define a contiguous-points region. G) All points that are not contiguous with that region are similarly deleted.
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Figure 3
Figure 3. Volumetric density maps of the REL1 active site. Some regions of the protein have been removed to facilitate visualization. A) The crystallographic pose of the bound ATP molecule. Crystallographic water molecules indicate the location of a secondary binding pocket that is transiently accessible from the ATP-binding pocket. B) The region of the binding pocket identified as “open” at least 95% of the time when the trajectory was aligned by the active-site C_α atoms. C) The same region when the trajectory was aligned by the C_α atoms of the whole protein. D) The region of the binding pocket identified as “open” at least 25% of the time when the active-site-C_α alignment was again used.
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Figure 4
Figure 4. POVME 1.0 and 2.0 benchmarks. The graph shows benchmark REL1 pocket volumes as a function of simulation time. POVME 1.0 and 2.0 give nearly the same volume measurements (in black). When the POVME 2.0 convex-hull option is enabled, the volumes are smaller (in gray). The bottom panel, generated using the 1XDN crystal structure, illustrates the difference. When the convex-hull option is enabled, the region of the binding pocket is more accurately captured (solid gray) than when it is deactivated (black wireframe). Some portions of the protein have been removed to facilitate visualization.
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References
This article references 86 other publications.
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  A computer program, VOIDOO, is described which can be employed in the study of cavities such as they occur in macromolecular structures (in particular, in proteins). The program can be used to detect unknown cavities or to delineate known cavities, either of which may be connected to the outside of the molecule or molecular assembly under study. Optionally, output files can be requested that contain a description of the shape of the cavity which can be displayed by the crystallographic modelling program O. Additionally, VOIDOO can be used to calculate the volume of a molecule and to create a file containing data pertaining to the surface of the molecule which can also be displayed using O. Examples of the use of VOIDOO are given for P2 myelin protein, cellular retinol-binding protein and cellobiohydrolase II. Finally, operational definitions to discern different types of cavity are introduced and guidelines for assessing the accuracy and improving the comparability of cavity calculations are given.
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  The SURFNET program generates mol. surfaces and gaps between surfaces from 3D coordinates supplied in a PDB-format file. The gap regions can correspond to the voids between two or more mols., or to the internal cavities and surface grooves within a single mol. The program is particularly useful in clearly delineating the regions of the active site of a protein. It can also generate 3D contour surfaces of the d. distributions of any set of 3D data points. All output surfaces can be viewed interactively, along with the mols. or data points in question, using some of the best-known mol. modeling packages. In addn., PostScript output is available, and the generated surfaces can be rendered using various other graphics packages.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXpslentbs%253D&md5=cd68217416ecf26be347bec9a1788837
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  Researchers engaged in computer-aided drug design often wish to measure the vol. of a ligand-binding pocket in order to predict pharmacol. We have recently developed a simple algorithm, called POVME (POcket Vol. MEasurer), for this purpose. POVME is Python implemented, fast, and freely available. To demonstrate its utility, we use the new algorithm to study three members of the matrix-metalloproteinase family of proteins. Despite the structural similarity of these proteins, differences in binding-pocket dynamics are easily identified.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsFOnsrw%253D&md5=3eedaede6cb9bb248b041dc6f220a657
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  Tunnels and channels facilitate the transport of small mols., ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chem. properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromol. structures. Herein we present a new version of CAVER enabling automatic anal. of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calcn. and clustering of pathways. A trajectory from a mol. dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the anal. of mol. dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estd. the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that anal. of mol. dynamics simulation is essential for the estn. of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochem. phenomena in the area of mol. transport, mol. recognition and enzymic catalysis. The software is freely available as a multiplatform command-line application online.
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  A new pocket detection protocol successfully identified transient pockets on the protein surfaces of BCL-XL, IL-2, and MDM2. Because the native inhibitor binding pocket was absent or only partly detectable in the unbound proteins, these crystal structures were used as starting points for 10 ns long mol. dynamics simulations. Trajectory snapshots were scanned for cavities on the protein surface using the program PASS. The detected cavities were clustered to det. several distinct transient pockets. They all opened within 2.5 ps, and most of them appeared multiple times. All three systems gave similar results overall. At the native binding site, pockets of similar size compared with a known inhibitor bound could be obsd. for all three systems. AutoDock could successfully place inhibitor mols. into these transient pockets with less than 2 Å rms deviation from their crystal structures, suggesting this protocol as a viable tool to identify transient ligand binding pockets on protein surfaces.
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  PASS (Putative Active Sites with Spheres) is a simple computational tool that uses geometry to characterize regions of buried vol. in proteins and to identify positions likely to represent binding sites based upon the size, shape, and burial extent of these vols. Its utility as a predictive tool for binding site identification is tested by predicting known binding sites of proteins in the PDB using both complexed macromols. and their corresponding apo-protein structures. The results indicate that PASS can serve as a front-end to fast docking. The main utility of PASS lies in the fact that it can analyze a moderate-size protein (∼30 kDa) in under 20 s, which makes it suitable for interactive mol. modeling, protein database anal., and aggressive virtual screening efforts. As a modeling tool, PASS (i) rapidly identifies favorable regions of the protein surface, (ii) simplifies visualization of residues modulating binding in these regions, and (iii) provides a means of directly visualizing buried vol., which is often inferred indirectly from curvature in a surface representation. PASS produces output in the form of std. PDB files, which are suitable for any modeling package, and provides script files to simplify visualization in Cerius2, InsightII, MOE, Quanta, RasMol, and Sybyl. PASS is freely available to all.
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  Fpocket: an open source platform for ligand pocket detection
  Le Guilloux Vincent; Schmidtke Peter; Tuffery Pierre
  BMC bioinformatics (2009), 10 (), 168 ISSN:.
  BACKGROUND: Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces. RESULTS: Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster. CONCLUSION: Fpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at http://fpocket.sourceforge.net.
  >> More from SciFinder ^®
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12. 12
  Schmidtke, P.; Bidon-Chanal, A.; Luque, F. J.; Barril, X. MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories Bioinformatics 2011, 27, 3276– 3285
  12
  MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories
  Schmidtke, Peter; Bidon-Chanal, Axel; Luque, F. Javier; Barril, Xavier
  Bioinformatics (2011), 27 (23), 3276-3285CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
  Motivation: A variety of pocket detection algorithms are now freely or com. available to the scientific community for the anal. of static protein structures. However, since proteins are dynamic entities, enhancing the capabilities of these programs for the straightforward detection and characterization of cavities taking into account protein conformational ensembles should be valuable for capturing the plasticity of pockets, and therefore allow gaining insight into structure-function relationships. Results: This article describes a new method, called MDpocket, providing a fast, free and open-source tool for tracking small mol. binding sites and gas migration pathways on mol. dynamics (MDs) trajectories or other conformational ensembles. MDpocket is based on the fpocket cavity detection algorithm and a valuable contribution to existing anal. tools. The capabilities of MDpocket are illustrated for three relevant cases: (i) the detection of transient subpockets using an ensemble of crystal structures of HSP90; (ii) the detection of known xenon binding sites and migration pathways in myoglobin; and (iii) the identification of suitable pockets for mol. docking in P38 Map kinase. Availability: MDpocket is free and open-source software and can be downloaded at http://fpocket.sourceforge.net. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.
  >> More from SciFinder ^®
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13. 13
  Halgren, T. A. Identifying and Characterizing Binding Sites and Assessing Druggability J. Chem. Inf. Model. 2009, 49, 377– 389
  13
  Identifying and Characterizing Binding Sites and Assessing Druggability
  Halgren, Thomas A.
  Journal of Chemical Information and Modeling (2009), 49 (2), 377-389CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  Identification and characterization of binding sites is key in the process of structure-based drug design. In some cases there may not be any information about the binding site for a target of interest. In other cases, a putative binding site has been identified by computational or exptl. means, but the druggability of the target is not known. Even when a site for a given target is known, it may be desirable to find addnl. sites whose targeting could produce a desired biol. response. A new program, called SiteMap, is presented for identifying and analyzing binding sites and for predicting target druggability. In a large-scale validation, SiteMap correctly identifies the known binding site as the top-ranked site in 86% of the cases, with best results (>98%) coming for sites that bind ligands with subnanomolar affinity. In addn., a modified version of the score employed for binding-site identification allows SiteMap to accurately classify the druggability of proteins as measured by their ability to bind passively absorbed small mols. tightly. In characterizing binding sites, SiteMap provides quant. and graphical information that can help guide efforts to critically assess virtual hits in a lead-discovery application or to modify ligand structure to enhance potency or improve phys. properties in a lead-optimization context.
  >> More from SciFinder ^®
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  New method for fast and accurate binding-site identification and analysis
  Halgren, Tom
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  Structure-based drug design seeks to exploit the structure of protein-ligand or protein-protein binding sites, but the site is not always known at the outset. Even when the site is known, the researcher may wish to identify alternative prospective binding sites that may result in different biol. effects or new class of compds. It is also vital in lead optimization to clearly understand the degree to which known binders or docking hits satisfy or violate complementarity to the receptor. SiteMap is a new technique for identifying potential binding sites and for predicting their druggability in lead-discovery applications and for characterizing binding sites and critically assessing prospective ligands in lead-optimization applications. In large-scale validation tests, SiteMap correctly identifies the known binding site in > 96% of the cases, with best results (> 98%) coming for sites that bind ligands tightly. It also accurately distinguishes between sites that bind ligands and sites that don't. In binding-site anal., SiteMap provides a wealth of quant. and graphical information that can help guide efforts to modify ligand structure to enhance potency or improve phys. properties. These attributes allow SiteMap to nicely complement techniques such as docking and computational lead optimization in structure-base drug design.
  >> More from SciFinder ^®
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  Pocket-Based Drug Design: Exploring Pocket Space
  Zheng, Xiliang; Gan, Lin Feng; Wang, Erkang; Wang, Jin
  AAPS Journal (2013), 15 (1), 228-241CODEN: AJAOB6; ISSN:1550-7416. (Springer)
  A review. The identification and application of druggable pockets of targets play a key role in in silico drug design, which is a fundamental step in structure-based drug design. Herein, some recent progresses and developments of the computational anal. of pockets have been covered. Also, the pockets at the protein-protein interfaces (PPI) have been considered to further explore the pocket space for drug discovery. We have presented two case studies targeting the kinetic pockets generated by normal mode anal. and mol. dynamics method, resp., in which we focus upon incorporating the pocket flexibility into the two-dimensional virtual screening with both affinity and specificity. We applied the specificity and affinity (SPA) score to quant. est. affinity and evaluate specificity using the intrinsic specificity ratio (ISR) as a quant. criterion. In one of two cases, we also included some applications of pockets located at the dimer interfaces to emphasize the role of PPI in drug discovery. This review will attempt to summarize the current status of this pocket issue and will present some prospective avenues of further inquiry.
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  Amaro, R. E.; Swift, R. V.; Votapka, L.; Li, W. W.; Walker, R. C.; Bush, R. M. Mechanism of 150-cavity formation in influenza neuraminidase Nat. Commun. 2011, 2, 388
  18
  Mechanism of 150-cavity formation in influenza neuraminidase
  Amaro Rommie E; Swift Robert V; Votapka Lane; Li Wilfred W; Walker Ross C; Bush Robin M
  Nature communications (2011), 2 (), 388 ISSN:.
  The recently discovered 150-cavity in the active site of group-1 influenza A neuraminidase (NA) proteins provides a target for rational structure-based drug development to counter the increasing frequency of antiviral resistance in influenza. Surprisingly, the 2009 H1N1 pandemic virus (09N1) neuraminidase was crystalized without the 150-cavity characteristic of group-1 NAs. Here we demonstrate, through a total sum of 1.6 μs of biophysical simulations, that 09N1 NA exists in solution preferentially with an open 150-cavity. Comparison with simulations using avian N1, human N2 and 09N1 with a I149V mutation and an extensive bioinformatics analysis suggests that the conservation of a key salt bridge is crucial in the stabilization of the 150-cavity across both subtypes. This result provides an atomic-level structural understanding of the recent finding that antiviral compounds designed to take advantage of contacts in the 150-cavity can inactivate both 2009 H1N1 pandemic and avian H5N1 viruses.
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19. 19
  Baron, R.; Vellore, N. A. LSD1/CoREST is an allosteric nanoscale clamp regulated by H3-histone-tail molecular recognition Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 12509– 12514
  There is no corresponding record for this reference.
20. 20
  Fuchs, J. E.; Huber, R. G.; Von Grafenstein, S.; Wallnoefer, H. G.; Spitzer, G. M.; Fuchs, D.; Liedl, K. R. Dynamic Regulation of Phenylalanine Hydroxylase by Simulated Redox Manipulation PLoS One 2012, 7, e53005
  There is no corresponding record for this reference.
21. 21
  Sinko, W.; de Oliveira, C.; Williams, S.; Van Wynsberghe, A.; Durrant, J. D.; Cao, R.; Oldfield, E.; McCammon, J. A. Applying Molecular Dynamics Simulations to Identify Rarely Sampled Ligand-bound Conformational States of Undecaprenyl Pyrophosphate Synthase, an Antibacterial Target Chem. Biol. Drug Des. 2011, 77, 412– 420
  21
  Applying molecular dynamics simulations to identify rarely sampled ligand-bound conformational states of undecaprenyl pyrophosphate synthase, an antibacterial target
  Sinko, William; de Oliveira, Cesar; Williams, Sarah; Van Wynsberghe, Adam; Durrant, Jacob D.; Cao, Rong; Oldfield, Eric; McCammon, J. Andrew
  Chemical Biology & Drug Design (2011), 77 (6), 412-420CODEN: CBDDAL; ISSN:1747-0277. (Wiley-Blackwell)
  Undecaprenyl pyrophosphate synthase is a cis-prenyltransferase enzyme, which is required for cell wall biosynthesis in bacteria. Undecaprenyl pyrophosphate synthase is an attractive target for antimicrobial therapy. We performed long mol. dynamics simulations and docking studies on undecaprenyl pyrophosphate synthase to investigate its dynamic behavior and the influence of protein flexibility on the design of undecaprenyl pyrophosphate synthase inhibitors. We also describe the first x-ray crystallog. structure of Escherichia coli apo-undecaprenyl pyrophosphate synthase. The mol. dynamics simulations indicate that undecaprenyl pyrophosphate synthase is a highly flexible protein, with mobile binding pockets in the active site. By carrying out docking studies with exptl. validated undecaprenyl pyrophosphate synthase inhibitors using high- and low-populated conformational states extd. from the mol. dynamics simulations, we show that structurally dissimilar compds. can bind preferentially to different and rarely sampled conformational states. By performing structural analyses on the newly obtained apo-undecaprenyl pyrophosphate synthase and other crystal structures previously published, we show that the changes obsd. during the mol. dynamics simulation are very similar to those seen in the crystal structures obtained in the presence or absence of ligands. We believe that this is the first time that a rare "expanded pocket" state, key to drug design and verified by crystallog., has been extd. from a mol. dynamics simulation.
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22. 22
  Lindert, S.; McCammon, J. A. Dynamics of Plasmodium falciparum enoyl-ACP reductase and implications on drug discovery Protein Sci. 2012, 21, 1734– 1745
  22
  Dynamics of Plasmodium falciparum enoyl-ACP reductase and implications on drug discovery
  Lindert, Steffen; McCammon, J. Andrew
  Protein Science (2012), 21 (11), 1734-1745CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
  Enoyl-acyl carrier protein reductase (ENR) is a crucial enzyme in the type II fatty acid synthesis pathway of many pathogens such as Plasmodium falciparum, the etiol. agent of the most severe form of malaria. Because of its essential function of fatty acid double bond redn. and the absence of a human homolog, PfENR is an interesting drug target. Although extensive knowledge of the protein structure has been gathered over the last decade, comparatively little remains known about the dynamics of this crucial enzyme. Here, we perform extensive mol. dynamics simulations of tetrameric PfENR in different states of cofactor and ligand binding, and with a variety of different ligands bound. A pocket-vol. anal. is also performed, and virtual screening is used to identify potential druggable hotspots. The implications of the results for future drug-discovery projects are discussed.
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23. 23
  Boechi, L.; de Oliveira, C. A.; Da Fonseca, I.; Kizjakina, K.; Sobrado, P.; Tanner, J. J.; McCammon, J. A. Substrate-dependent dynamics of UDP-galactopyranose mutase: Implications for drug design Protein Sci. 2013, 22, 1490– 1501
  23
  Substrate-dependent dynamics of UDP-galactopyranose mutase: Implications for drug design
  Boechi, Leonardo; de Oliveira, Cesar Augusto F.; Da Fonseca, Isabel; Kizjakina, Karina; Sobrado, Pablo; Tanner, John J.; McCammon, J. Andrew
  Protein Science (2013), 22 (11), 1490-1501CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
  Trypanosoma cruzi is the causative agent of Chagas disease, a neglected tropical disease that represents one of the major health challenges of the Latin American countries. Successful efforts were made during the last few decades to control the transmission of this disease, but there is still no treatment for the 10 million adults in the chronic phase of the disease. In T. cruzi, as well as in other pathogens, the flavoenzyme UDP-galactopyranose mutase (UGM) catalyzes the conversion of UDP-galactopyranose to UDP-galactofuranose, a precursor of the cell surface β-galactofuranose that is involved in the virulence of the pathogen. The fact that UGM is not present in humans makes inhibition of this enzyme a good approach in the design of new Chagas therapeutics. By performing a series of computer simulations of T. cruzi UGM in the presence or absence of an active site ligand, we address the mol. details of the mechanism that controls the uptake and retention of the substrate. The simulations suggest a modular mechanism in which each moiety of the substrate controls the flexibility of a different protein loop. Furthermore, the calcns. indicate that interactions with the substrate diphosphate moiety are esp. important for stabilizing the closed active site. This hypothesis is supported with kinetics measurements of site-directed mutants of T. cruzi UGM. Our results extend our knowledge of UGM dynamics and offer new alternatives for the prospective design of drugs.
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24. 24
  Wu, Y.; Qin, G. R.; Gao, F.; Liu, Y.; Vavricka, C. J.; Qi, J. X.; Jiang, H. L.; Yu, K. Q.; Gao, G. F. Induced opening of influenza virus neuraminidase N2 150-loop suggests an important role in inhibitor binding Sci. Rep. 2013, 3, 1551
  24
  Induced opening of influenza virus neuraminidase N2 150-loop suggests an important role in inhibitor binding
  Wu, Yan; Qin, Guangrong; Gao, Feng; Liu, Yue; Vavricka, Christopher J.; Qi, Jianxun; Jiang, Hualiang; Yu, Kunqian; Gao, George F.
  Scientific Reports (2013), 3 (), 1551, 8 pp.CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)
  The recently discovered 150-cavity (formed by loop residues 147-152, N2 numbering) adjacent to the enzymic active site of Group 1 influenza A neuraminidase (NA) has introduced a novel target for the design of next-generation NA inhibitors. However, only Group 1 NAs, with the exception of the 2009 pandemic H1N1 NA, possess a 150-cavity, and no 150-cavity has been obsd. in Group 2 NAs. The role of the 150-cavity played in enzymic activity and inhibitor binding is not well understood. Here, oseltamivir carboxylate can induce opening of the rigid closed N2 150-loop and provide a novel mechanism for 150-loop movement using mol. dynamics simulations. The authors' results provide the structural and biophys. basis of the open form of 150-loop and illustrates that the inherent flexibility and the ligand induced flexibility of the 150-loop should be taken into consideration for future drug design.
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25. 25
  Han, N. Y.; Mu, Y. G. Plasticity of 150-Loop in Influenza Neuraminidase Explored by Hamiltonian Replica Exchange Molecular Dynamics Simulations PLoS One 2013, 8, e60995
  There is no corresponding record for this reference.
26. 26
  Schultes, S.; Nijmeijer, S.; Engelhardt, H.; Kooistra, A. J.; Vischer, H. F.; de Esch, I. J. P.; Haaksma, E. E. J.; Leurs, R.; de Graaf, C. Mapping histamine H-4 receptor-ligand binding modes MedChemComm 2013, 4, 193– 204
  There is no corresponding record for this reference.
27. 27
  Li, P.; Chen, Z.; Xu, H.; Sun, H.; Li, H.; Liu, H.; Yang, H.; Gao, Z.; Jiang, H.; Li, M. The gating charge pathway of an epilepsy-associated potassium channel accommodates chemical ligands Cell Res. 2013, 23, 1106– 1118
  27
  The gating charge pathway of an epilepsy-associated potassium channel accommodates chemical ligands
  Li, Ping; Chen, Zhuxi; Xu, Haiyan; Sun, Haifeng; Li, Hao; Liu, Hong; Yang, Huaiyu; Gao, Zhaobing; Jiang, Hualiang; Li, Min
  Cell Research (2013), 23 (9), 1106-1118CODEN: CREEB6; ISSN:1001-0602. (NPG Nature Asia-Pacific)
  Voltage-gated potassium (Kv) channels derive their voltage sensitivity from movement of gating charges in voltage-sensor domains (VSDs). The gating charges translocate through a phys. pathway in the VSD to open or close the channel. Previous studies showed that the gating charge pathways of Shaker and Kv1.2-2.1 chimeric channels are occluded, forming the structural basis for the focused elec. field and gating charge transfer center. Here, we show that the gating charge pathway of the voltage-gated KCNQ2 potassium channel, activity redn. of which causes epilepsy, can accommodate various small mol. ligands. Combining mutagenesis, mol. simulation and electrophysiol. recording, a binding model for the probe activator, ztz240, in the gating charge pathway was defined. This information was used to establish a docking-based virtual screening assay targeting the defined ligand-binding pocket. Nine activators with five new chemotypes were identified, and in vivo expts. showed that three ligands binding to the gating charge pathway exhibit significant anti-epilepsy activity. Identification of various novel activators by virtual screening targeting the pocket supports the presence of a ligand-binding site in the gating charge pathway. The capability of the gating charge pathway to accommodate small mol. ligands offers new insights into the gating charge pathway of the therapeutically relevant KCNQ2 channel.
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28. 28
  Kekenes-Huskey, P. M.; Metzger, V. T.; Grant, B. J.; McCammon, J. A. Calcium binding and allosteric signaling mechanisms for the sarcoplasmic reticulum Ca2+ATPase Protein Sci. 2012, 21, 1429– 1443
  28
  Calcium binding and allosteric signaling mechanisms for the sarcoplasmic reticulum Ca2+ ATPase
  Kekenes-Huskey, Peter M.; Metzger, Vincent T.; Grant, Barry J.; McCammon, J. Andrew
  Protein Science (2012), 21 (10), 1429-1443CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
  The sarcoplasmic reticulum Ca2+ ATPase (SERCA) is a membrane-bound pump that utilizes ATP to drive calcium ions from the myocyte cytosol against the higher calcium concn. in the sarcoplasmic reticulum. Conformational transitions assocd. with Ca2+-binding are important to its catalytic function. We have identified collective motions that partition SERCA crystallog. structures into multiple catalytically-distinct states using principal component anal. Using Brownian dynamics simulations, we demonstrate the important contribution of surface-exposed, polar residues in the diffusional encounter of Ca2+. Mol. dynamics simulations indicate the role of Glu309 gating in binding Ca2+, as well as subsequent changes in the dynamics of SERCA's cytosolic domains. Together these data provide structural and dynamical insights into a multistep process involving Ca2+ binding and catalytic transitions.
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29. 29
  Bung, N.; Pradhan, M.; Srinivasan, H.; Bulusu, G. Structural Insights into E. coli Porphobilinogen Deaminase during Synthesis and Exit of 1-Hydroxymethylbilane PLoS Comput. Biol. 2014, 10, e1003484
  There is no corresponding record for this reference.
30. 30
  Torres, R.; Swift, R. V.; Chim, N.; Wheatley, N.; Lan, B. S.; Atwood, B. R.; Pujol, C.; Sankaran, B.; Bliska, J. B.; Amaro, R. E.; Goulding, C. W. Biochemical, Structural and Molecular Dynamics Analyses of the Potential Virulence Factor RipA from Yersinia pestis PLoS One 2011, 6, e25084
  There is no corresponding record for this reference.
31. 31
  Grant, B. J.; Lukman, S.; Hocker, H. J.; Sayyah, J.; Brown, J. H.; McCammon, J. A.; Gorfe, A. A. Novel Allosteric Sites on Ras for Lead Generation PLoS One 2011, 6, e25711
  There is no corresponding record for this reference.
32. 32
  Mowrey, D. D.; Liu, Q.; Bondarenko, V.; Chen, Q.; Seyoum, E.; Xu, Y.; Wu, J.; Tang, P. Insights into Distinct Modulation of alpha 7 and alpha 7 beta 2 Nicotinic Acetylcholine Receptors by the Volatile Anesthetic Isoflurane J. Biol. Chem. 2013, 288, 35793– 35800
  There is no corresponding record for this reference.
33. 33
  Yi-Xin, A.; Jun-Rui, L.; Chun-Wei, X.; Jiang-Bei, M.; Xu-Yun, Y.; He, Z. Simulated Mechanism of Triclosan in Modulating the Active Site and Loop of FabI by Computer Acta Phys.-Chim. Sin. 2014, 30, 559– 568
  There is no corresponding record for this reference.
34. 34
  Blachly, P. G.; de Oliveira, C. A. F.; Williams, S. L.; McCammon, J. A. Utilizing a Dynamical Description of IspH to Aid in the Development of Novel Antimicrobial Drugs PLoS Comput. Biol. 2013, 9, e1003395
  34
  Utilizing a dynamical description of IspH to aid in the development of novel antimicrobial drugs
  Blachly, Patrick G.; de Oliveira, Cesar A. F.; Williams, Sarah L.; Andrew McCammon, J.
  PLoS Computational Biology (2013), 9 (12), e1003395/1-e1003395/13, 13 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
  The nonmevalonate pathway is responsible for isoprenoid prodn. in microbes, including H. pylori, M. tuberculosis and P. falciparum, but is nonexistent in humans, thus providing a desirable route for antibacterial and antimalarial drug discovery. We coordinate a structural study of IspH, a [4Fe-4S] protein responsible for converting HMBPP to IPP and DMAPP in the ultimate step in the nonmevalonate pathway. By performing accelerated mol. dynamics simulations on both substrate-free and HMBPP-bound [Fe4S4]2+ IspH, we elucidate how substrate binding alters the dynamics of the protein. Using principal component anal., we note that while substrate-free IspH samples various open and closed conformations, the closed conformation obsd. exptl. for HMBPP-bound IspH is inaccessible in the absence of HMBPP. In contrast, simulations with HMBPP bound are restricted from accessing the open states sampled by the substrate-free simulations. Further investigation of the substrate-free simulations reveals large fluctuations in the HMBPP binding pocket, as well as allosteric pocket openings - both of which are achieved through the hinge motions of the individual domains in IspH. Coupling these findings with solvent mapping and various structural analyses reveals alternative druggable sites that may be exploited in future drug design efforts.
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35. 35
  Demir, O.; Amaro, R. E. Elements of Nucleotide Specificity in the Trypanosoma brucei Mitochondrial RNA Editing Enzyme RET2 J. Chem. Inf. Model. 2012, 52, 1308– 1318
  35
  Elements of Nucleotide Specificity in the Trypanosoma brucei Mitochondrial RNA Editing Enzyme RET2
  Demir, Ozlem; Amaro, Rommie E.
  Journal of Chemical Information and Modeling (2012), 52 (5), 1308-1318CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  The causative agent of African sleeping sickness, Trypanosoma brucei, undergoes an unusual mitochondrial RNA editing process that is essential for its survival. RNA editing terminal uridylyl transferase 2 of T. brucei (TbRET2) is an indispensable component of the editosome machinery that performs this editing. TbRET2 is required to maintain the vitality of both the insect and bloodstream forms of the parasite, and with its high-resoln. crystal structure, it poses as a promising pharmaceutical target. Neither the exclusive requirement of UTP for catalysis, nor the RNA primer preference of TbRET2 is well-understood. Using all-atom explicitly solvated mol. dynamics (MD) simulations, we investigated the effect of UTP binding on TbRET2 structure and dynamics, as well as the determinants governing TbRET2's exclusive UTP preference. Through our investigations of various nucleoside triphosphate substrates (NTPs), we show that UTP preorganizes the binding site through an extensive water-mediated H-bonding network, bringing Glu424 and Arg144 side chains to an optimum position for RNA primer binding. In contrast, cytosine 5'-triphosphate (CTP) and ATP cannot achieve this preorganization and thus preclude productive RNA primer binding. Addnl., we have located ligand-binding hot spots of TbRET2 based on the MD conformational ensembles and computational fragment mapping. TbRET2 reveals different binding pockets in the apo and UTP-bound MD simulations, which could be targeted for inhibitor design.
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36. 36
  Mowrey, D.; Cheng, M. H.; Liu, L. T.; Willenbring, D.; Lu, X. H.; Wymore, T.; Xu, Y.; Tang, P. Asymmetric Ligand Binding Facilitates Conformational Transitions in Pentameric Ligand-Gated Ion Channels J. Am. Chem. Soc. 2013, 135, 2172– 2180
  There is no corresponding record for this reference.
37. 37
  Bustamante, J. P.; Abbruzzetti, S.; Marcelli, A.; Gauto, D.; Boechi, L.; Bonamore, A.; Boffi, A.; Bruno, S.; Feis, A.; Foggi, P.; Estrin, D. A.; Viappiani, C. Ligand Uptake Modulation by Internal Water Molecules and Hydrophobic Cavities in Hemoglobins J. Phys. Chem. B 2014, 118, 1234– 1245
  37
  Ligand Uptake Modulation by Internal Water Molecules and Hydrophobic Cavities in Hemoglobins
  Bustamante, Juan P.; Abbruzzetti, Stefania; Marcelli, Agnese; Gauto, Diego; Boechi, Leonardo; Bonamore, Alessandra; Boffi, Alberto; Bruno, Stefano; Feis, Alessandro; Foggi, Paolo; Estrin, Dario A.; Viappiani, Cristiano
  Journal of Physical Chemistry B (2014), 118 (5), 1234-1245CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
  Internal water mols. play an active role in ligand uptake regulation, since displacement of retained water mols. from protein surfaces or cavities by incoming ligands can promote favorable or disfavorable effects over the global binding process. Detection of these water mols. by x-ray crystallog. is difficult given their positional disorder and low occupancy. In this work, we employ a combination of mol. dynamics simulations and ligand rebinding over a broad time range to shed light into the role of water mols. in ligand migration and binding. Computational studies on the unliganded structure of the thermostable truncated Hb from Thermobifida fusca (Tf-trHbO) show that a water mol. is in the vicinity of the iron heme, stabilized by WG8 with the assistance of YCD1, exerting a steric hindrance for binding of an exogenous ligand. Mutation of WG8 to F results in a significantly lower stabilization of this water mol. and in subtle dynamical structural changes that favor ligand binding, as obsd. exptl. Water is absent from the fully hydrophobic distal cavity of the triple mutant YB10F-YCD1F-WG8F (3F), due to the lack of residues capable of stabilizing it nearby the heme. In agreement with these effects on the barriers for ligand rebinding, over 97% of the photodissociated ligands are rebound within a few nanoseconds in the 3F mutant case. Our results demonstrate the specific involvement of water mols. in shaping the energetic barriers for ligand migration and binding.
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38. 38
  Selvam, B.; Porter, S. L.; Tikhonova, I. G. Addressing Selective Polypharmacology of Antipsychotic Drugs Targeting the Bioaminergic Receptors through Receptor Dynamic Conformational Ensembles J. Chem. Inf. Model. 2013, 53, 1761– 1774
  38
  Addressing Selective Polypharmacology of Antipsychotic Drugs Targeting the Bioaminergic Receptors through Receptor Dynamic Conformational Ensembles
  Selvam, Balaji; Porter, Simon L.; Tikhonova, Irina G.
  Journal of Chemical Information and Modeling (2013), 53 (7), 1761-1774CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  Selective polypharmacol., where a drug acts on multiple rather than a single mol. target involved in a disease, emerges to develop a structure-based system biol. approach to design drugs selectively targeting a disease-active protein network. We focus on the bioaminergic receptors that belong to the group of G-protein-coupled receptors (GPCRs) and represent targets for therapeutic agents against schizophrenia and depression. Among them, it has been shown that the serotonin (5-HT2A and 5-HT6) and dopamine (D2 and D3) receptors induce a cognition-enhancing effect (group 1), while the histamine (H1) and serotonin (5-HT2C) receptors lead to metabolic side effects and the 5-HT2B serotonin receptor causes pulmonary hypertension (group 2). Thus, the problem arises to develop an approach that allows identifying drugs targeting only the disease-active receptors, i.e. group 1. The recent release of several crystal structures of the bioaminergic receptors, involving the D3 and H1 receptors, provides the possibility to model the structures of all receptors and initiate a study of the structural and dynamic context of selective polypharmacol. In this work, we use mol. dynamics simulations to generate a conformational space of the receptors and subsequently characterize its binding properties applying mol. probe mapping. All-against-all comparison of the generated probe maps of the selected diverse conformations of all receptors with the Tanimoto similarity coeff. (Tc) enable the sepn. of the receptors of group 1 from group 2. The pharmacophore built based on the Tc-selected receptor conformations, using the multiple probe maps discovers structural features that can be used to design mols. selective toward the receptors of group 1. The importance of several predicted residues to ligand selectivity is supported by the available mutagenesis and ligand structure-activity relationship studies. In addn., the Tc-selected conformations of the receptors for group 1 show good performance in isolation of known ligands from a random decoy. Our computational structure-based protocol to tackle selective polypharmacol. of antipsychotic drugs could be applied for other diseases involving multiple drug targets, such as oncol. and infectious disorders.
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39. 39
  Weinreb, V.; Li, L.; Chandrasekaran, S. N.; Koehl, P.; Delarue, M.; Carter, C. W., Jr. Enhanced Amino Acid Selection in Fully Evolved Tryptophanyl-tRNA Synthetase, Relative to Its Urzyme, Requires Domain Motion Sensed by the D1 Switch, a Remote Dynamic Packing Motif J. Biol. Chem. 2014, 289, 4367– 4376
  There is no corresponding record for this reference.
40. 40
  Li, J. N.; Jonsson, A. L.; Beuming, T.; Shelley, J. C.; Voth, G. A. Ligand-Dependent Activation and Deactivation of the Human Adenosine A(2A) Receptor J. Am. Chem. Soc. 2013, 135, 8749– 8759
  40
  Ligand-Dependent Activation and Deactivation of the Human Adenosine A2A Receptor
  Li, Jianing; Jonsson, Amanda L.; Beuming, Thijs; Shelley, John C.; Voth, Gregory A.
  Journal of the American Chemical Society (2013), 135 (23), 8749-8759CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  G-protein-coupled receptors (GPCRs) are membrane proteins with crit. functions in cellular signal transduction, representing a primary class of drug targets. Acting by direct binding, many drugs modulate GPCR activity and influence the signaling pathways assocd. with numerous diseases. However, complete details of ligand-dependent GPCR activation/deactivation are difficult to obtain from expts. Therefore, it remains unclear how ligands modulate a GPCR's activity. To elucidate the ligand-dependent activation/deactivation mechanism of the human adenosine A2A receptor (AA2AR), a member of the class A GPCRs, we performed large-scale unbiased mol. dynamics and metadynamics simulations of the receptor embedded in a membrane. At the at. level, we have obsd. distinct structural states that resemble the active and inactive states. In particular, we noted key structural elements changing in a highly concerted fashion during the conformational transitions, including six conformational states of a tryptophan (Trp2466.48). Our findings agree with a previously proposed view that, during activation, this tryptophan residue undergoes a rotameric transition that may be coupled to a series of coherent conformational changes, resulting in the opening of the G-protein binding site. Further, metadynamics simulations provide quant. evidence for this mechanism, suggesting how ligand binding shifts the equil. between the active and inactive states. Our anal. also proposes that a few specific residues are assocd. with agonism/antagonism, affinity, and selectivity, and suggests that the ligand-binding pocket can be thought of as having three distinct regions, providing dynamic features for structure-based design. Addnl. simulations with AA2AR bound to a novel ligand are consistent with our proposed mechanism. Generally, our study provides insights into the ligand-dependent AA2AR activation/deactivation in addn. to what has been found in crystal structures. These results should aid in the discovery of more effective and selective GPCR ligands.
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41. 41
  Baron, R.; McCammon, J. A. Molecular Recognition and Ligand Association Annu. Rev. Phys. Chem. 2013, 64, 151– 175
  41
  Molecular recognition and ligand association
  Baron, Riccardo; McCammon, J. Andrew
  Annual Review of Physical Chemistry (2013), 64 (), 151-175CODEN: ARPLAP; ISSN:0066-426X. (Annual Reviews Inc.)
  We review recent developments in our understanding of mol. recognition and ligand assocn., focusing on two major viewpoints: (a) studies that highlight new phys. insight into the mol. recognition process and the driving forces detg. thermodn. signatures of binding and (b) recent methodol. advances in applications to protein-ligand binding. In particular, we highlight the challenges posed by compensating enthalpic and entropic terms, competing solute and solvent contributions, and the relevance of complex configurational ensembles comprising multiple protein, ligand, and solvent intermediate states. As more complete physics is taken into account, computational approaches increase their ability to complement exptl. measurements, by providing a microscopic, dynamic view of ensemble-averaged exptl. observables. Physics-based approaches are increasingly expanding their power in pharmacol. applications.
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42. 42
  Ariga, K.; Ito, H.; Hill, J. P.; Tsukube, H. Molecular recognition: from solution science to nano/materials technology Chem. Soc. Rev. 2012, 41, 5800– 5835
  42
  Molecular recognition: from solution science to nano/materials technology
  Ariga, Katsuhiko; Ito, Hiroshi; Hill, Jonathan P.; Tsukube, Hiroshi
  Chemical Society Reviews (2012), 41 (17), 5800-5835CODEN: CSRVBR; ISSN:0306-0012. (Royal Society of Chemistry)
  A review. In the 25 years since its Nobel Prize in chem., supramol. chem. based on mol. recognition has been paid much attention in scientific and technol. fields. Nanotechnol. and the related areas seek breakthrough methods of nanofabrication based on rational organization through assembly of constituent mols. Advanced biochem., medical applications, and environmental and energy technologies also depend on the importance of specific interactions between mols. In those current fields, mol. recognition is now being re-evaluated. In this review, we re-examine current trends in mol. recognition from the viewpoint of the surrounding media, that is (i) the soln. phase for development of basic science and mol. design advances; (ii) at nano/materials interfaces for emerging technologies and applications. The first section of this review includes mol. recognition frontiers, receptor design based on combinatorial approaches, org. capsule receptors, metallo-capsule receptors, helical receptors, dendrimer receptors, and the future design of receptor architectures. The following section summarizes topics related to mol. recognition at interfaces including fundamentals of mol. recognition, sensing and detection, structure formation, mol. machines, mol. recognition involving polymers and related materials, and mol. recognition processes in nanostructured materials.
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43. 43
  Kahraman, A.; Morris, R. J.; Laskowski, R. A.; Thornton, J. M. Shape variation in protein binding pockets and their ligands J. Mol. Biol. 2007, 368, 283– 301
  43
  Shape Variation in Protein Binding Pockets and their Ligands
  Kahraman, Abdullah; Morris, Richard J.; Laskowski, Roman A.; Thornton, Janet M.
  Journal of Molecular Biology (2007), 368 (1), 283-301CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
  A common assumption about the shape of protein binding pockets is that they are related to the shape of the small ligand mols. that can bind there. But to what extent is that assumption true Here we use a recently developed shape matching method to compare the shapes of protein binding pockets to the shapes of their ligands. We find that pockets binding the same ligand show greater variation in their shapes than can be accounted for by the conformational variability of the ligand. This suggests that geometrical complementarity in general is not sufficient to drive mol. recognition. Nevertheless, we show when considering only shape and size that a significant proportion of the recognition power of a binding pocket for its ligand resides in its shape. Addnl., we observe a "buffer zone" or a region of free space between the ligand and protein, which results in binding pockets being on av. three times larger than the ligand that they bind.
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44. 44
  Seddon, G.; Lounnas, V.; McGuire, R.; van den Bergh, T.; Bywater, R. P.; Oliveira, L.; Vriend, G. Drug design for ever, from hype to hope J. Comput.-Aided Mol. Des. 2012, 26, 137– 150
  There is no corresponding record for this reference.
45. 45
  Meng, X. Y.; Zhang, H. X.; Mezei, M.; Cui, M. Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery Curr. Comput.-Aided Drug Des. 2011, 7, 146– 157
  45
  Molecular docking: A powerful approach for structure-based drug discovery
  Meng, Xuan-Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng
  Current Computer-Aided Drug Design (2011), 7 (2), 146-157CODEN: CCDDAS; ISSN:1573-4099. (Bentham Science Publishers Ltd.)
  A review. Mol. docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available mol. docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor mol. docking approaches, esp. those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential soln. to flexible receptor docking problems. Three application examples of mol. docking approaches for drug discovery are provided.
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46. 46
  Golbraikh, A.; Wang, X. S.; Zhu, H.; Tropsha, A. Predictive QSAR Modeling: Methods and Applications in Drug Discovery and Chemical Risk Assessment. In Handbook of Computational Chemistry; Leszczynski, J., Ed.; Springer: Dordrecht, Netherlands, 2012; pp 1309– 1342.
  There is no corresponding record for this reference.
47. 47
  Liang, J.; Edelsbrunner, H.; Woodward, C. Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design Protein Sci. 1998, 7, 1884– 1897
  47
  Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design
  Liang, Jie; Edelsbrunner, Herbert; Woodward, Clare
  Protein Science (1998), 7 (9), 1884-1897CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
  Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the vol. and area of pockets and cavities; and the area and circumference of mouth openings. CAST anal. of over 100 proteins has been carried out; proteins examd. include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a no. of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 102-103 Å3. Statistical anal. reveals that the no. of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a no. of instructive exceptions. Ligand vol. and binding site vol. are somewhat correlated when binding site vol. is ≤700 Å3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as addnl. binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Biol 1:55-58). Anal. of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Anal. of the FK506 binding protein, and of compds. developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST anal. of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 Å3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.
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48. 48
  Rush, T. S.; Grant, J. A.; Mosyak, L.; Nicholls, A. A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction J. Med. Chem. 2005, 48, 1489– 1495
  48
  A Shape-Based 3-D Scaffold Hopping Method and Its Application to a Bacterial Protein-Protein Interaction
  Rush, Thomas S., III; Grant, J. Andrew; Mosyak, Lidia; Nicholls, Anthony
  Journal of Medicinal Chemistry (2005), 48 (5), 1489-1495CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  In this paper, the authors describe the first prospective application of the shape-comparison program ROCS (Rapid Overlay of Chem. Structures) to find new scaffolds for small mol. inhibitors of the ZipA-FtsZ protein-protein interaction, a proposed antibacterial target. The shape comparisons are made relative to the crystallog. detd., bioactive conformation of a high-throughput screening (HTS) hit. The use of ROCS led to the identification of a set of novel, weakly binding inhibitors with scaffolds presenting synthetic opportunities to further optimize biol. affinity and lacking development issues assocd. with the HTS lead. These ROCS-identified scaffolds would have been missed using other structural similarity approaches such as ISIS 2D fingerprints. X-ray crystallog. anal. of one of the new inhibitors bound to ZipA reveals that the shape comparison approach very accurately predicted the binding mode. These exptl. results validate this use of ROCS for chemotype switching or "lead hopping" and suggest that it is of general interest for lead identification in drug discovery endeavors.
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49. 49
  Wirth, M.; Volkamer, A.; Zoete, V.; Rippmann, F.; Michielin, O.; Rarey, M.; Sauer, W. H. B. Protein pocket and ligand shape comparison and its application in virtual screening J. Comput.-Aided Mol. Des. 2013, 27, 511– 524
  There is no corresponding record for this reference.
50. 50
  Hawkins, P. C. D.; Skillman, A. G.; Nicholls, A. Comparison of shape-matching and docking as virtual screening tools J. Med. Chem. 2007, 50, 74– 82
  50
  Comparison of Shape-Matching and Docking as Virtual Screening Tools
  Hawkins, Paul C. D.; Skillman, A. Geoffrey; Nicholls, Anthony
  Journal of Medicinal Chemistry (2007), 50 (1), 74-82CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  Ligand docking is a widely used approach in virtual screening. In recent years a large no. of publications have appeared in which docking tools are compared and evaluated for their effectiveness in virtual screening against a wide variety of protein targets. These studies have shown that the effectiveness of docking in virtual screening is highly variable due to a large no. of possible confounding factors. Another class of method that has shown promise in virtual screening is the shape-based, ligand-centric approach. Several direct comparisons of docking with the shape-based tool ROCS have been conducted using data sets from some of these recent docking publications. The results show that a shape-based, ligand-centric approach is more consistent than, and often superior to, the protein-centric approach taken by docking.
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51. 51
  Distinto, S.; Esposito, F.; Kirchmair, J.; Cardia, M. C.; Gaspari, M.; Maccioni, E.; Alcaro, S.; Markt, P.; Wolber, G.; Zinzula, L.; Tramontano, E. Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach Eur. J. Med. Chem. 2012, 50, 216– 229
  There is no corresponding record for this reference.
52. 52
  LaLonde, J. M.; Elban, M. A.; Courter, J. R.; Sugawara, A.; Soeta, T.; Madani, N.; Princiotto, A. M.; Do Kwon, Y.; Kwong, P. D.; Schon, A.; Freire, E.; Sodroski, J.; Smith, A. B. Design, synthesis and biological evaluation of small molecule inhibitors of CD4-gp120 binding based on virtual screening Bioorg. Med. Chem. 2011, 19, 91– 101
  There is no corresponding record for this reference.
53. 53
  Tuccinardi, T.; Ortore, G.; Santos, M. A.; Marques, S. M.; Nuti, E.; Rossello, A.; Martinelli, A. Multitemplate Alignment Method for the Development of a Reliable 3D-QSAR Model for the Analysis of MMP3 Inhibitors J. Chem. Inf. Model. 2009, 49, 1715– 1724
  53
  Multitemplate Alignment Method for the Development of a Reliable 3D-QSAR Model for the Analysis of MMP3 Inhibitors
  Tuccinardi, Tiziano; Ortore, Gabriella; Santos, M. Amelia; Marques, Sergio M.; Nuti, Elisa; Rossello, Armando; Martinelli, Adriano
  Journal of Chemical Information and Modeling (2009), 49 (7), 1715-1724CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  A ligand-based 3D-QSAR study for the identification of MMP3 inhibitors was developed by applying an innovative alignment method capable of taking into account information obtained from available X-ray MMP3 structures. Comparison of the obtained model with data recently published using a docking-based alignment method indicated that the ligand-based 3D-QSAR model provided better predictive ability. A second external test set of 106 MMP3 inhibitors further confirmed the predictive ability of the 3D-QSAR model. Finally, certain iminodiacetyl-based hydroxamate-benzenesulfonamide conjugates, which were predicted to be active by the 3D-QSAR model, were tested in vitro for MMP3 inhibition; some provided low nanomolar activity. As such, the results suggest that the multi-template alignment method is capable of improving the quality of 3D-QSAR models and therefore could be applied to the study of other systems. Furthermore, since MMP3 is an important target toward the treatment of arthritis, this model could be applied to the design of new active MMP3 inhibitors.
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54. 54
  Nicholls, A.; McGaughey, G. B.; Sheridan, R. P.; Good, A. C.; Warren, G.; Mathieu, M.; Muchmore, S. W.; Brown, S. P.; Grant, J. A.; Haigh, J. A.; Nevins, N.; Jain, A. N.; Kelley, B. Molecular Shape and Medicinal Chemistry: A Perspective J. Med. Chem. 2010, 53, 3862– 3886
  54
  Molecular Shape and Medicinal Chemistry: A Perspective
  Nicholls, Anthony; McGaughey, Georgia B.; Sheridan, Robert P.; Good, Andrew C.; Warren, Gregory; Mathieu, Magali; Muchmore, Steven W.; Brown, Scott P.; Grant, J. Andrew; Haigh, James A.; Nevins, Neysa; Jain, Ajay N.; Kelley, Brian
  Journal of Medicinal Chemistry (2010), 53 (10), 3862-3886CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  A review article with 111 refs. summarized perspectives of mol. shape and medicinal chem. in drug screening.
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55. 55
  Osguthorpe, D. J.; Sherman, W.; Hagler, A. T. Exploring Protein Flexibility: Incorporating Structural Ensembles From Crystal Structures and Simulation into Virtual Screening Protocols J. Phys. Chem. B 2012, 116, 6952– 6959
  There is no corresponding record for this reference.
56. 56
  Osguthorpe, D. J.; Sherman, W.; Hagler, A. T. Generation of Receptor Structural Ensembles for Virtual Screening Using Binding Site Shape Analysis and Clustering Chem. Biol. Drug Des. 2012, 80, 182– 193
  56
  Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering
  Osguthorpe, David J.; Sherman, Woody; Hagler, Arnold T.
  Chemical Biology & Drug Design (2012), 80 (2), 182-193CODEN: CBDDAL; ISSN:1747-0277. (Wiley-Blackwell)
  Accounting for protein flexibility is an essential yet challenging component of structure-based virtual screening. Whereas an ideal approach would account for full protein and ligand flexibility during the virtual screening process, this is currently intractable using available computational resources. An alternative is ensemble docking, where calcns. are performed on a set of individual rigid receptor conformations and the results combined. The primary challenge assocd. with this approach is the choice of receptor structures to use for the docking calcns. In this work, we show that selection of a small set of structures based on clustering on binding site vol. overlaps provides an efficient and effective way to account for protein flexibility in virtual screening. We first apply the method to crystal structures of cyclin-dependent kinase 2 and HIV protease and show that virtual screening for ensembles of four cluster representative structures yields consistently high enrichments and diverse actives. We then apply the method to a structural ensemble of the androgen receptor generated with mol. dynamics and obtain results that are in agreement with those from the crystal structures of cyclin-dependent kinase 2 and HIV protease. This work provides a step forward in the incorporation of protein flexibility into structure-based virtual screening.
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57. 57
  Ben Nasr, N.; Guillemain, H.; Lagarde, N.; Zagury, J. F.; Montes, M. Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query J. Chem. Inf. Model. 2013, 53, 293– 311
  57
  Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query
  Ben Nasr, Nesrine; Guillemain, Helene; Lagarde, Nathalie; Zagury, Jean-Francois; Montes, Matthieu
  Journal of Chemical Information and Modeling (2013), 53 (2), 293-311CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a ref., whose choice requires retrospective enrichment studies on benchmarking databases which consume addnl. resources. In the present study, the authors have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their vol. and opening, the authors show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site vol. is lower than 350 A3, the structure with the smaller vol. should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion addnl. to its vol. as the most closed structures should not be preferred in the large systems. These "binding site properties-based" guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available.
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58. 58
  Nichols, S. E.; Swift, R. V.; Amaro, R. E. Rational Prediction with Molecular Dynamics for Hit Identification Curr. Top. Med. Chem. 2012, 12, 2002– 2012
  58
  Rational prediction with molecular dynamics for hit identification
  Nichols, Sara E.; Swift, Robert V.; Amaro, Rommie E.
  Current Topics in Medicinal Chemistry (Sharjah, United Arab Emirates) (2012), 12 (18), 2002-2012CODEN: CTMCCL; ISSN:1568-0266. (Bentham Science Publishers Ltd.)
  A review. Although the motions of proteins are fundamental for their function, for pragmatic reasons, the consideration of protein elasticity has traditionally been neglected in drug discovery and design. This review details protein motion, its relevance to biomol. interactions and how it can be sampled using mol. dynamics simulations. Within this context, two major areas of research in structure-based prediction that can benefit from considering protein flexibility, binding site detection and mol. docking, are discussed. Basic classification metrics and statistical anal. techniques, which can facilitate performance anal., are also reviewed. With hardware and software advances, mol. dynamics in combination with traditional structure-based prediction methods can potentially reduce the time and costs involved in the hit identification pipeline.
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59. 59
  Ascher, D.; Dubois, P. F.; Hinsen, K.; James, J. H.; Oliphant, T. Numerical Python; UCRL-MA-128569 ed.; Lawrence Livermore National Laboratory: Livermore, CA, 1999.
  There is no corresponding record for this reference.
60. 60
  Dubois, P. F. Extending Python with Fortran Comput. Sci. Eng. 1999, 1, 66– 73
  There is no corresponding record for this reference.
61. 61
  Jones, E.; Oliphant, T.; Peterson, P. Others SciPy: Open Source Scientific Tools for Python, 0.11.0; 2001.
  There is no corresponding record for this reference.
62. 62
  Oliphant, T. E. Guide to NumPy; Brigham Young University: Provo, UT, 2006.
  There is no corresponding record for this reference.
63. 63
  Peterson, P. F2PY: a tool for connecting Fortran and Python programs Int. J. Comput. Sci. Eng. 2009, 4, 296– 305
  There is no corresponding record for this reference.
64. 64
  Humphrey, W.; Dalke, A.; Schulten, K. VMD: visual molecular dynamics J. Mol. Graphics 1996, 14, 33– 38
  64
  VDM: visual molecular dynamics
  Humphrey, William; Dalke, Andrew; Schulten, Klaus
  Journal of Molecular Graphics (1996), 14 (1), 33-8, plates, 27-28CODEN: JMGRDV; ISSN:0263-7855. (Elsevier)
  VMD is a mol. graphics program designed for the display and anal. of mol. assemblies, in particular, biopolymers such as proteins and nucleic acids. VMD can simultaneously display any no. of structures using a wide variety of rendering styles and coloring methods. Mols. are displayed as one or more "representations," in which each representation embodies a particular rendering method and coloring scheme for a selected subset of atoms. The atoms displayed in each representation are chosen using an extensive atom selection syntax, which includes Boolean operators and regular expressions. VMD provides a complete graphical user interface for program control, as well as a text interface using the Tcl embeddable parser to allow for complex scripts with variable substitution, control loops, and function calls. Full session logging is supported, which produces a VMD command script for later playback. High-resoln. raster images of displayed mols. may be produced by generating input scripts for use by a no. of photorealistic image-rendering applications. VMD has also been expressly designed with the ability to animate mol. dynamics (MD) simulation trajectories, imported either from files or from a direct connection to a running MD simulation. VMD is the visualization component of MDScope, a set of tools for interactive problem solving in structural biol., which also includes the parallel MD program NAMD, and the MDCOMM software used to connect the visualization and simulation programs, VMD is written in C++, using an object-oriented design; the program, including source code and extensive documentation, is freely available via anonymous ftp and through the World Wide Web.
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65. 65
  Akl, S. G.; Toussaint, G. T. In Efficient convex hull algorithms for pattern recognition applications, Proc. 4th. Int. Joint Conf. on Pattern Recognition (Kyoto, Japan), 1978; pp 483– 487.
  There is no corresponding record for this reference.
66. 66
  Deng, J.; Schnaufer, A.; Salavati, R.; Stuart, K. D.; Hol, W. G. High resolution crystal structure of a key editosome enzyme from Trypanosoma brucei: RNA editing ligase 1 J. Mol. Biol. 2004, 343, 601– 613
  There is no corresponding record for this reference.
67. 67
  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 2006, 65, 712– 725
  67
  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.
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68. 68
  Horn, H. W.; Swope, W. C.; Pitera, J. W.; Madura, J. D.; Dick, T. J.; Hura, G. L.; Head-Gordon, T. Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew J. Chem. Phys. 2004, 120, 9665– 9678
  68
  Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew
  Horn, Hans W.; Swope, William C.; Pitera, Jed W.; Madura, Jeffry D.; Dick, Thomas J.; Hura, Greg L.; Head-Gordon, Teresa
  Journal of Chemical Physics (2004), 120 (20), 9665-9678CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  A re-parameterization of the std. TIP4P water model for use with Ewald techniques is introduced, providing an overall global improvement in water properties relative to several popular nonpolarizable and polarizable water potentials. Using high precision simulations, and careful application of std. anal. corrections, we show that the new TIP4P-Ew potential has a d. max. at ∼1°, and reproduces exptl. bulk-densities and the enthalpy of vaporization, ΔHvap, from -37.5 to 127° at 1 atm with an abs. av. error of less than 1%. Structural properties are in very good agreement with X-ray scattering intensities at temps. between 0 and 77° and dynamical properties such as self-diffusion coeff. are in excellent agreement with expt. The parameterization approach used can be easily generalized to rehabilitate any water force field using available exptl. data over a range of thermodn. points.
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69. 69
  Meagher, K. L.; Redman, L. T.; Carlson, H. A. Development of polyphosphate parameters for use with the AMBER force field J. Comput. Chem. 2003, 24, 1016– 1025
  69
  Development of polyphosphate parameters for use with the AMBER force field
  Meagher, Kristin L.; Redman, Luke T.; Carlson, Heather A.
  Journal of Computational Chemistry (2003), 24 (9), 1016-1025CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  Accurate force fields are essential for reproducing the conformational and dynamic behavior of condensed-phase systems. The popular AMBER force field has parameters for monophosphates, but they do not extend well to polyphorylated mols. such as ADP and ATP. This work presents parameters for the partial charges, atom types, bond angles, and torsions in simple polyphosphorylated compds. The parameters are based on MO calcns. of methyldiphosphate and methyltriphosphate at the RHF/6-31+G* level. The new parameters were fit to the entire potential energy surface (not just min.) with an RMSD of 0.62 kcal/mol. This is exceptional agreement and a significant improvement over the current parameters that produce a potential surface with an RMSD of 7.8 kcal/mol to that of the ab initio calcns. Testing has shown that the parameters are transferable and capable of reproducing the gas-phase conformations of inorg. diphosphate and triphosphate. Also, the parameters are an improvement over existing parameters in the condensed phase as shown by minimizations of ATP bound in several proteins. These parameters are intended for use with the existing AMBER 94/99 force field, and they will permit users to apply AMBER to a wider variety of important enzymic systems.
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70. 70
  Allner, O.; Nilsson, L.; Villa, A. Magnesium Ion-Water Coordination and Exchange in Biomolecular Simulations J. Chem. Theory Comput. 2012, 8, 1493– 1502
  70
  Magnesium Ion-Water Coordination and Exchange in Biomolecular Simulations
  Allner, Olof; Nilsson, Lennart; Villa, Alessandra
  Journal of Chemical Theory and Computation (2012), 8 (4), 1493-1502CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  Magnesium ions have an important role in the structure and folding mechanism of RNA systems. To properly simulate these biophys. processes, the applied mol. models should reproduce, among other things, the kinetic properties of the ions in water soln. Here, the authors have studied the kinetics of the binding of magnesium ions with water mols. and nucleic acid systems using mol. dynamics simulation. The authors have validated the parameters used in biomol. force fields, such as AMBER and CHARMM, for Mg2+ ions and also for the biol. relevant ions Na+, K+, and Ca2+ together with three different water models (TIP3P, SPC/E, and TIP5P). Mg2+ ions have a slower exchange rate than Na+, K+, and Ca2+ in agreement with the exptl. trend, but the simulated value underestimates the exptl. obsd. Mg2+-water exchange rate by several orders of magnitude, irresp. of the force field and water model. A new set of parameters for Mg2+ was developed to reproduce the exptl. kinetic data. This set also leads to better reprodn. of structural data than existing models. The authors have applied the new parameter set to Mg2+ binding with a monophosphate model system and with the purine riboswitch, add A-riboswitch. In line with the Mg2+-water results, the newly developed parameters show a better description of the structure and kinetics of the Mg2+-phosphate binding than all other models. The characterization of the ion binding to the riboswitch system shows that the new parameter set does not affect the global structure of the RNA system or the no. of ions involved in direct or indirect binding. A slight decrease in the no. of water-bridged contacts between A-riboswitch and the Mg2+ ion is obsd. The results support the ability of the newly developed parameters to improve the kinetic description of the Mg2+ and phosphate ions and their applicability in nucleic acid simulation.
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71. 71
  Joung, I. S.; Cheatham, T. E. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations J. Phys. Chem. B 2008, 112, 9020– 9041
  71
  Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations
  Joung, In Suk; Cheatham, Thomas E.
  Journal of Physical Chemistry B (2008), 112 (30), 9020-9041CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)
  Alkali (Li+, Na+, K+, Rb+, and Cs+) and halide (F-, Cl-, Br-, and I-) ions play an important role in many biol. phenomena, roles that range from stabilization of biomol. structure, to influence on biomol. dynamics, to key physiol. influence on homeostasis and signaling. To properly model ionic interaction and stability in atomistic simulations of biomol. structure, dynamics, folding, catalysis, and function, an accurate model or representation of the monovalent ions is critically necessary. A good model needs to simultaneously reproduce many properties of ions, including their structure, dynamics, solvation, and moreover both the interactions of these ions with each other in the crystal and in soln. and the interactions of ions with other mols. At present, the best force fields for biomols. employ a simple additive, nonpolarizable, and pairwise potential for at. interaction. In this work, the authors describe their efforts to build better models of the monovalent ions within the pairwise Coulombic and 6-12 Lennard-Jones framework, where the models are tuned to balance crystal and soln. properties in Ewald simulations with specific choices of well-known water models. Although it has been clearly demonstrated that truly accurate treatments of ions will require inclusion of nonadditivity and polarizability (particularly with the anions) and ultimately even a quantum mech. treatment, the authors' goal was to simply push the limits of the additive treatments to see if a balanced model could be created. The applied methodol. is general and can be extended to other ions and to polarizable force-field models. The authors' starting point centered on observations from long simulations of biomols. in salt soln. with the AMBER force fields where salt crystals formed well below their soly. limit. The likely cause of the artifact in the AMBER parameters relates to the naive mixing of the Smith and Dang chloride parameters with AMBER-adapted Aqvist cation parameters. To provide a more appropriate balance, the authors reoptimized the parameters of the Lennard-Jones potential for the ions and specific choices of water models. To validate and optimize the parameters, the authors calcd. hydration free energies of the solvated ions and also lattice energies (LE) and lattice consts. (LC) of alkali halide salt crystals. This is the first effort that systematically scans across the Lennard-Jones space (well depth and radius) while balancing ion properties like LE and LC across all pair combinations of the alkali ions and halide ions. The optimization across the entire monovalent series avoids systematic deviations. The ion parameters developed, optimized, and characterized were targeted for use with some of the most commonly used rigid and nonpolarizable water models, specifically TIP3P, TIP4PEW, and SPC/E. In addn. to well reproducing the soln. and crystal properties, the new ion parameters well reproduce binding energies of the ions to water and the radii of the first hydration shells.
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72. 72
  Kale, L.; Skeel, R.; Bhandarkar, M.; Brunner, R.; Gursoy, A.; Krawetz, N.; Phillips, J.; Shinozaki, A.; Varadarajan, K.; Schulten, K. NAMD2: greater scalability for parallel molecular dynamics J. Comput. Phys. 1999, 151, 283– 312
  72
  NAMD2: Greater Scalability for Parallel Molecular Dynamics
  Kale, Laxmikant; Skeel, Robert; Bhandarkar, Milind; Brunner, Robert; Gursoy, Attila; Krawetz, Neal; Phillips, James; Shinozaki, Aritomo; Varadarajan, Krishnan; Schulten, Klaus
  Journal of Computational Physics (1999), 151 (1), 283-312CODEN: JCTPAH; ISSN:0021-9991. (Academic Press)
  Mol. dynamics programs simulate the behavior of biomol. systems, leading to understanding of their functions. However, the computational complexity of such simulations is enormous. Parallel machines provide the potential to meet this computational challenge. To harness this potential, it is necessary to develop a scalable program. It is also necessary that the program be easily modified by application-domain programmers. The NAMD2 program presented in this paper seeks to provide these desirable features. It uses spatial decompn. combined with force decompn. to enhance scalability. It uses intelligent periodic load balancing, so as to maximally utilize the available compute power. It is modularly organized, and implemented using Charm++, a parallel C++ dialect, so as to enhance its modifiability. It uses a combination of numerical techniques and algorithms to ensure that energy drifts are minimized, ensuring accuracy in long running calcns. NAMD2 uses a portable run-time framework called Converse that also supports interoperability among multiple parallel paradigms. As a result, different components of applications can be written in the most appropriate parallel paradigms. NAMD2 runs on most parallel machines including workstation clusters and has yielded speedups in excess of 180 on 220 processors. This paper also describes the performance obtained on some benchmark applications. (c) 1999 Academic Press.
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73. 73
  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
  73
  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.
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74. 74
  Shipman, J. W. Tkinter reference: a GUI for Python; New Mexico Tech Computer Center: Socorro, NM, 2010.
  There is no corresponding record for this reference.
75. 75
  Welch, B. B.; Jones, K. Practical programming in Tcl/Tk, 4th ed.; Prentice Hall/PTR: Upper Saddle River, NJ, 2003.
  There is no corresponding record for this reference.
76. 76
  Schnaufer, A.; Panigrahi, A. K.; Panicucci, B.; Igo, R. P., Jr.; Salavati, R.; Stuart, K. An RNA Ligase Essential for RNA Editing and Survival of the Bloodstream Form of Trypanosoma brucei Science 2001, 291, 2159– 2162
  There is no corresponding record for this reference.
77. 77
  Rusche, L. N.; Huang, C. E.; Piller, K. J.; Hemann, M.; Wirtz, E.; Sollner-Webb, B. The two RNA ligases of the Trypanosoma brucei RNA editing complex: cloning the essential band IV gene and identifying the band V gene Mol. Cell. Biol. 2001, 21, 979– 989
  77
  The two RNA ligases of the Trypanosoma brucei RNA editing complex: cloning the essential band IV gene and identifying the band V gene
  Rusche, Laura N.; Huang, Catherine E.; Piller, Kenneth J.; Hemann, Michael; Wirtz, Elizabeth; Sollner-Webb, Barbara
  Molecular and Cellular Biology (2001), 21 (4), 979-989CODEN: MCEBD4; ISSN:0270-7306. (American Society for Microbiology)
  Kinetoplastid RNA editing is a posttranscriptional insertion and deletion of U residues in mitochondrial transcripts that involves RNA ligase. A complex of seven different polypeptides purified from Trypanosoma brucei mitochondria that catalyzes accurate RNA editing contains RNA ligases of ∼57 kDa (band IV) and ∼50 kDa (band V). From a partial amino acid sequence, cDNA and genomic clones of band IV were isolated, making it the first cloned component of the minimal RNA editing complex. It is indeed an RNA ligase, for when expressed in Escherichia coli, the protein autoadenylylates and catalyzes RNA joining. Overexpression studies revealed that T. brucei can regulate of total band IV protein at the level of translation or protein stability, even upon massively increased mRNA levels. The protein's mitochondrial targeting was confirmed by its location, size when expressed in T. brucei and E. coli, and N-terminal sequence. Importantly, genetic knockout studies demonstrated that the gene for band IV is essential in procyclic trypanosomes. The band IV and band V RNA ligases of the RNA editing complex therefore serve different functions. The authors also identified the gene for band V RNA ligase, a protein much more homologous to band IV than to other known ligases.
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78. 78
  Durrant, J. D.; Hall, L.; Swift, R. V.; Landon, M.; Schnaufer, A.; Amaro, R. E. Novel Naphthalene-Based Inhibitors of Trypanosoma brucei RNA Editing Ligase 1 PLoS Neglected Trop. Dis. 2010, 4, e803
  There is no corresponding record for this reference.
79. 79
  Durrant, J. D.; Friedman, A. J.; McCammon, J. A. CrystalDock: a novel approach to fragment-based drug design J. Chem. Inf. Model. 2011, 51, 2573– 2580
  79
  CrystalDock: A Novel Approach to Fragment-Based Drug Design
  Durrant, Jacob D.; Friedman, Aaron J.; McCammon, J. Andrew
  Journal of Chemical Information and Modeling (2011), 51 (10), 2573-2580CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  We present a novel algorithm called CrystalDock that analyzes a mol. pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound exptl. structures. Germane fragments from the crystallog. or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, resp. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.
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80. 80
  Craig, I. R.; Pfleger, C.; Gohlke, H.; Essex, J. W.; Spiegel, K. Pocket-Space Maps To Identify Novel Binding-Site Conformations in Proteins J. Chem. Inf. Model. 2011, 51, 2666– 2679
  80
  Pocket-Space Maps To Identify Novel Binding-Site Conformations in Proteins
  Craig, Ian R.; Pfleger, Christopher; Gohlke, Holger; Essex, Jonathan W.; Spiegel, Katrin
  Journal of Chemical Information and Modeling (2011), 51 (10), 2666-2679CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  The identification of novel binding-site conformations can greatly assist the progress of structure-based ligand design projects. Diverse pocket shapes drive medicinal chem. to explore a broader chem. space and thus present addnl. opportunities to overcome key drug discovery issues such as potency, selectivity, toxicity, and pharmacokinetics. We report a new automated approach to diverse pocket selection, PocketAnalyzerPCA, which applies principal component anal. and clustering to the output of a grid-based pocket detection algorithm. Since the approach works directly with pocket shape descriptors, it is free from some of the problems hampering methods that are based on proxy shape descriptors, e.g. a set of at. positional coordinates. The approach is tech. straightforward and allows simultaneous anal. of mutants, isoforms, and protein structures derived from multiple sources with different residue numbering schemes. The PocketAnalyzerPCA approach is illustrated by the compilation of diverse sets of pocket shapes for aldose reductase and viral neuraminidase. In both cases this allows identification of novel computationally derived binding-site conformations that are yet to be obsd. crystallog. Indeed, known inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby demonstrating the utility of PocketAnalyzerPCA for rationalizing and improving the understanding of the mol. basis of protein-ligand interaction and bioactivity. A Python program implementing the PocketAnalyzerPCA approach is available for download under an open-source license (http://sourceforge.net/projects/papca/ or http://cpclab.uni-duesseldorf.de/downloads).
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81. 81
  Ghersi, D.; Sanchez, R. Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures J. Struct. Funct. Genomics 2011, 12, 109– 117
  81
  Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures
  Ghersi, Dario; Sanchez, Roberto
  Journal of Structural and Functional Genomics (2011), 12 (2), 109-117CODEN: JSFGAW; ISSN:1345-711X. (Springer)
  A review. Structural genomics projects have revealed structures for a large no. of proteins of unknown function. Understanding the interactions between these proteins and their ligands would provide an initial step in their functional characterization. Binding site identification methods are a fast and cost-effective way to facilitate the characterization of functionally important protein regions. In this review we describe our recently developed methods for binding site identification in the context of existing methods. The advantage of energy-based approaches is emphasized, since they provide flexibility in the identification and characterization of different types of binding sites.
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82. 82
  Perot, S.; Sperandio, O.; Miteva, M. A.; Camproux, A. C.; Villoutreix, B. O. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery Drug Discovery Today 2010, 15, 656– 667
  There is no corresponding record for this reference.
83. 83
  Paramo, T.; East, A.; Garzon, D.; Ulmschneider, M. B.; Bond, P. J. Efficient Characterization of Protein Cavities within Molecular Simulation Trajectories: trj_cavity J. Chem. Theory Comput. 2014, 10, 2151– 2164
  83
  Efficient Characterization of Protein Cavities within Molecular Simulation Trajectories: trj_cavity
  Paramo, Teresa; East, Alexandra; Garzon, Diana; Ulmschneider, Martin B.; Bond, Peter J.
  Journal of Chemical Theory and Computation (2014), 10 (5), 2151-2164CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  Protein cavities and tunnels are crit. in detg. phenomena such as ligand binding, mol. transport, and enzyme catalysis. Mol. dynamics (MD) simulations enable the exploration of the flexibility and conformational plasticity of protein cavities, extending the information available from static exptl. structures relevant to, for example, drug design. Here, the authors present a new tool (trj_cavity) implemented within the GROMACS (www.gromacs.org) framework for the rapid identification and characterization of cavities detected within MD trajectories. Trj_cavity is optimized for usability and computational efficiency and is applicable to the time-dependent anal. of any cavity topol., and optional specialized descriptors can be used to characterize, for example, protein channels. Its novel grid-based algorithm performs an efficient neighbor search whose calcn. time is linear with system size, and a comparison of performance with other widely used cavity anal. programs reveals an orders-of-magnitude improvement in the computational cost. To demonstrate its potential for revealing novel mechanistic insights, trj_cavity has been used to analyze long-time scale simulation trajectories for three diverse protein cavity systems. This has helped to reveal, resp., the lipid binding mechanism in the deep hydrophobic cavity of a sol. mite-allergen protein, Der p 2; a means for shuttling carbohydrates between the surface-exposed substrate-binding and catalytic pockets of a multidomain, membrane-proximal pullulanase, PulA; and the structural basis for selectivity in the transmembrane pore of a voltage-gated sodium channel (NavMs), embedded within a lipid bilayer environment. Trj_cavity is available for download under an open-source license (http://sourceforge.net/projects/trjcavity). A simplified, GROMACS-independent version may also be compiled.
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84. 84
  Hendlich, M.; Rippmann, F.; Barnickel, G. LIGSITE: Automatic and efficient detection of potential small molecule-binding sites in proteins J. Mol. Graphics Modell. 1997, 15, 359– 363
  84
  LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins
  Hendlich M; Rippmann F; Barnickel G
  Journal of molecular graphics & modelling (1997), 15 (6), 359-63, 389 ISSN:1093-3263.
  LIGSITE is a new program for the automatic and time-efficient detection of pockets on the surface of proteins that may act as binding sites for small molecule ligands. Pockets are identified with a series of simple operations on a cubic grid. Using a set of receptor-ligand complexes we show that LIGSITE is able to identify the binding sites of small molecule ligands with high precision. The main advantage of LIGSITE is its speed. Typical search times are in the range of 5 to 20 s for medium-sized proteins. LIGSITE is therefore well suited for identification of pockets in large sets of proteins (e.g., protein families) for comparative studies. For graphical display LIGSITE produces VRML representations of the protein-ligand complex and the binding site for display with a VRML viewer such as WebSpace from SGI.
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85. 85
  Stahl, M.; Taroni, C.; Schneider, G. Mapping of protein surface cavities and prediction of enzyme class by a self-organizing neural network Protein Eng. 2000, 13, 83– 88
  85
  Mapping of protein surface cavities and prediction of enzyme class by a self-organizing neural network
  Stahl, Martin; Taroni, Chiara; Schneider, Gisbert; Hoffmann, F.
  Protein Engineering (2000), 13 (2), 83-88CODEN: PRENE9; ISSN:0269-2139. (Oxford University Press)
  An automated computer-based method for mapping of protein surface cavities was developed and applied to a set of 176 metalloproteinases contg. Zn2+ cations in their active sites. With very few exceptions, the cavity search routine detected the active site among the 5 largest cavities and produced reasonable active site surfaces. Cavities were described by means of solvent-accessible surface patches. For a given protein, these patches were calcd. in three steps: (1) definition of cavity atoms forming surface cavities by a grid-based technique; (2) generation of solvent accessible surfaces; (3) assignment of an accessibility value and a generalized atom type to each surface point. Topol. correlation vectors were generated from the set of surface points forming the cavities, and projected onto the plane by a self-organizing network. The resulting map of 865 enzyme cavities displayed clusters of active sites that were clearly sepd. from the other cavities. It was demonstrated that both fully automated recognition of active sites, and prediction of enzyme class could be performed for novel protein structures at high accuracy.
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86. 86
  Barber, C. B.; Dobkin, D. P.; Huhdanpaa, H. The Quickhull algorithm for convex hulls ACM Trans. Math. Software 1996, 22, 469– 483
  There is no corresponding record for this reference.
Supporting Information
Supporting Information
The Supporting Information contains two files. The first contains Figures S1 and S2. Figure S1 shows the POVME volumetric density maps generated when the REL1 trajectory was aligned by all active-site atoms and the atoms of the bound ligand. Figure S2 shows the pocket volumes calculated over the course of a REL1 molecular dynamics simulation, using several different software packages. The second file, Text S1, contains a tutorial that shows how VMD can be used to align a trajectory. The same tutorial also shows how to save a trajectory in the multiframe PDB format for subsequent POVME analysis. This material is available free of charge via the Internet at http://pubs.acs.org.
- ct500381c_si_001.pdf (374.05 kb)
- ct500381c_si_002.pdf (737.58 kb)
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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.

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Journal of Chemical Theory and Computation

Abstract

Introduction

Figure 1

Materials and Methods

The POVME Algorithm

1) Aligning the Trajectory

2) Defining a Region That Encompasses All Trajectory Binding Pockets

Figure 2

3) Removing Points That Are near Receptor Atoms

4) Removing Points Outside the Receptor’s Convex Hull

5) Removing Points That Are Not Contiguous with the Primary Pocket

POVME Output

The POVME Pocket ID Algorithm

Test System: RNA Editing Ligase 1

Results and Discussion

Test Case: Trypanosoma brucei RNA Editing Ligase 1 (REL1)

Figure 3

Benchmarking/Insights into Use

Figure 4

Software Comparison

Pocket Identification

Volume Measurements

Execution Time

Program Input and Output

Conclusion

Supporting Information

Terms & Conditions

Author Information

Acknowledgment

References

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Abstract

Figure 1

Figure 2

Figure 3

Figure 4

References

Supporting Information

Supporting Information

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POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics
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