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Biophysical Insight into the SARS-CoV2 Spike–ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach

  • Hamid Hadi-Alijanvand
    Hamid Hadi-Alijanvand
    Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences, Zanjan 45137-66731, Iran
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  • , 
  • Luisa Di Paola*
    Luisa Di Paola
    Unit of Chemical-Physics Fundamentals in Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Roma, via Álvaro del Portillo 21, Rome 00128, Italy
    *Email: [email protected]
    More by Luisa Di Paola
    Orcidhttps://orcid.org/0000-0001-5329-8689
  • , 
  • Guang Hu*
    Guang Hu
    Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
    *Email: [email protected]. Phone: +39 (06) 225419634
    More by Guang Hu
    Orcidhttps://orcid.org/0000-0002-8754-1541
  • , 
  • David M. Leitner
    David M. Leitner
    Department of Chemistry, University of Nevada, Reno 89557, Nevada, United States
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    Orcidhttps://orcid.org/0000-0002-3105-818X
  • , 
  • Gennady M. Verkhivker
    Gennady M. Verkhivker
    Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange 92866, California, United States
    Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine 92618, California, United States
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    Orcidhttps://orcid.org/0000-0002-4507-4471
  • , 
  • Peixin Sun
    Peixin Sun
    Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
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  • , 
  • Humanath Poudel
    Humanath Poudel
    Department of Chemistry, University of Nevada, Reno 89557, Nevada, United States
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  • , and 
  • Alessandro Giuliani
    Alessandro Giuliani
    Environmental and Health Department, Istituto Superiore di Sanità, Rome 00161, Italy
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    Orcidhttps://orcid.org/0000-0002-4640-804X
Cite this: ACS Omega 2022, 7, 20, 17024–17042
Publication Date (Web):May 10, 2022
https://doi.org/10.1021/acsomega.2c00154

Copyright © 2022 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike–ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike–ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.

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Introduction

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The COVID19 pandemic urgently needs a therapeutic solution. Emerging in early 2020, the pandemic seems to be here to stay and coexist with humanity for the foreseeable future. It is necessary to find reliable therapeutic solutions based on a deep understanding of molecular mechanisms underlying virus infection and spreading. (1) The spike protein has become the main molecular target of therapy (2) and vaccination strategies (3) due to its central role in the very early stage of the virus infection.
Its mutations strongly characterize SARS-CoV2 (the virus responsible for COVID19) with respect to genetic homologues (such as SARS-CoV), and they all come in the direction of optimizing the binding of the spike protein with its main receptor, human angiotensin converting enzyme 2 (ACE2). Because of the widespread presence of human ACE2 in many tissues (it is widely present in lungs, kidney, and gut), COVID19 acts with a multisystemic pathology.
The specific nature of the spike–ACE2 interaction has been largely explored since the pandemic outbreak, revealing special features awarding SARS-CoV2 with an unprecedented infectivity with respect to similar CoV (SARS-CoV and MERS-CoV), invoking a step change in spike flexibility as the main driver for improved infectivity and, more in general, providing insight on the very successful interaction with the receptor ACE2 (4) also in the perspective of identification of epitopes as antibodies targeting. (5) The key role played by the spike–ACE2 interaction is also reflected in the mutational escape to antibodies upon mutation, which involves mainly the receptor binding domain (RBD) and receptor binding motif (RBM) of the spike protein. (5)
Recently, the role of COVID19 in igniting iron dysregulation and hemoglobinopathy has been evidenced, pointing to an intermingled concurrence of infectious mechanisms and severe hematological pathological changes (e.g., blood clots).
Hepcidin plays a central role in iron metabolism and is involved in different mechanisms activated by COVID19; its serum values are highly altered in COVID 19 patients and have been used to predict COVID19 prognosis. (6) Hepcidin is the main hormone in iron regulation, allowing iron retention in cells during inflammation. For that, it belongs to the molecular machinery of native immune systems, whose role is more and more evident in the different stages of the inflammatory response to infection threats. (7) A recent work invokes a mimetic behavior of spike protein to explain the low level in critically ill patients. (8) Human hepcidin is a 25-residue peptide, produced by a furin cleavage of the prohormone form. Incidentally, furin also plays a central role in SARS-CoV2 infection, since furin cleavage of the spike–ACE2 complex allows virus entry. So hepcidin and spike protein compete with the same enzyme, furin, for their activity.
Conversely, hepcidin belongs to the family of beta-hairpin peptides, with known antimicrobial properties that depend on the number of disulfide bonds stabilizing the peptide (the more, the better). In this family, hepcidin has the largest number of disulfide bonds (four) and is positioned as the potentially most effective antimicrobial peptide in its family. Hepcidin exhibits antifungal activity against Candida albicans, Aspergillus fumigatus, and Aspergillus niger and antibacterial activity against Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, and group B Streptococcus. (9)
Eventually, Ehsani et al. demonstrated that the cytoplasmatic tail of the SARS-CoV2 spike protein shows a strong sequence similarity with hepcidin. (10)
For all these reasons, we decided to investigate the molecular interaction of human hepcidin with different forms of SARS-CoV2. We performed docking of human hepcidin with the closed, intermediate, and open forms of spike protein and with the complex spike–ACE2.
We also identified the allosteric sites in the open and closed conformations, to detect possible active regions for interactions with hepcidin, in the perspective of verifying its activity as an allosteric drug. The allosteric drug paradigm has been emerging as a novel landscape in drug discovery, requiring us also to better define the protein–protein interactions role in allostery of oligomeric proteins, such as spike and its complexes with ACE2. (11)
We found that different computational approaches, stemming from diverse hypotheses on protein conformational changes and binding, converge in suggesting a potentially effective interaction between hepcidin and spike protein. These results add a “molecular” layer to the multifaceted role of hepcidin in COVID19 pathogenesis.

Materials and Methods

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Structural Data and Molecular Docking

We performed the analysis of the complex SARS-CoV2 spike–ACE2 structure available as Protein Data Bank (PDB) from the Web site of Zhang’s laboratory (12) that we have already used in our previous work; (13) before the analysis, structures were equilibrated by means of the adaptive tempering molecular dynamics (AT-MD) technique, as thoroughly explained below (see the next section).
To test the effect of hepcidin on the stability and affinity of the different forms of the spike protein, we used the Web server SWARM to perform the protein–peptide docking, which has been previously evaluated as the most reliable with respect to the reproduction of native conformations of peptide–protein complexes. (14) Upon docking, we performed an all-atom molecular dynamics to equilibrate the spike–ACE2-hepcidin complex. We performed all the analysis on the equilibrium conformation resulting from the molecular dynamics simulation.
We report also the energy parameters of the complexes as computed through the PDBePISA web server (https://www.ebi.ac.uk/pdbe/pisa/); energy parameters of assemblies are explained in detail later.
We performed an all-atom molecular dynamics on the two complexes, namely, spike + ACE2 and spike + ACE2 + hepcidin. We report the structure of the equilibrated forms of the spike–ACE2 complex, with and without hepcidin (see Figure 1); hepcidin binds in a region partially overlapping with the Allosteric Modulation Region (AMR) we identified in our previous study. (13)

Figure 1

Figure 1. Complex of the spike protein of SARS-CoV2 with the human receptor ACE2 (in yellow). (A) Complex spike–ACE2; (B) complex spike–ACE2 docked with the hepcidin (blue surface).

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Allosteric Region Identification by Protein Contact Networks Clustering

In previous works, (13,15,16) we demonstrated the presence of an allosteric site in the spike protein, with respect to the binding with human ACE2, its main receptor. In this work, we apply again the same methodology to derive the main feature of the complexes (with and without hepcidin) so to disclose the role of this peptide in the complex of spike with the human receptor ACE2. We applied the clustering analysis on the structures shown in Figure 1 and to all the frames of the molecular dynamics of equilibration of the docked complex (spike protein + ACE2 + hepcidin), in order to identify guiding variables in the dynamics. The methodology of network clustering is based on the representation of protein molecular structures as networks (Protein Contact Networks (PCNs)): single residues are the network nodes, while network edges are the active contacts between residues. We define “active contacts” as those corresponding to a distance between residues between 4 and 8 Å (significant noncovalent interactions). (17)
The mathematical representation of the PCN is the adjacency matrix, defined as
(1)
The basic descriptor of the network connectivity is the node degree defined as
(2)
The average degree (adeg) of the network is the average value of ki over the number N of residues. Network shortest paths identify shortcuts in signal transmission through networks. The shortest path spij between the ith and jth nodes describes the lowest number of links to be trasversed for connecting the two nodes. This metrics plays a central role in identifying routes of signal transmission in allosteric regulation, as demonstrated also in other computational approaches. (16,18) In this work, we apply Dijkstra’s algorithm (19) to compute the matrix of shortest paths between residue pairs.
The functional properties of proteins rely on their modularity, implying that different modules (domains) inside the protein structure interact with each other and with the surrounding environment to allow protein physiological function. This interaction between regions of the protein structure is at the very basis of the allosteric mechanism, which in turn is responsible for the regulatory nature of protein activity.
The network formalism allows one also to identify functional modules inside the protein molecule, linked to the interaction pathways (link paths) throughout the molecule.
In this work, we apply network clustering based on the spectral decomposition of the Laplacian matrix, defined as
(3)
where D is the degree matrix, that is, a diagonal matrix whose generic non-null element Dii = ki. Applying the eigenvalue decomposition on the Laplacian L, the network clustering is a binary, hierarchical method based on the Fiedler vector v2, that is, the eigenvector corresponding to the second minor eigenvalue. The sign of its components assigns residues to two different clusters at each clustering iteration. The final result is a so-called “crispy” clustering partition, meaning a partition of nodes (residues) to different clusters. The number of clusters (power of 2) is assigned as the input of the algorithm.
We demonstrated that this method efficiently identifies functional modules in protein structure, when compared to geometrical methods of clustering like k-means. (20)
On the basis of the clustering partition (which assigns residues to different clusters, which putatively correspond to protein functional modules), it is possible to compute for each ith residue the participation coefficient, defined as (21,22)
(4)
where ksi is the node intracluster degree, that is, the number of contacts of the node (residue) with nodes belonging to the same cluster, whereas ki is the total node degree.
On the one side, P values range from 0 to 1; null values correspond to nodes very central in the clusters, not participating at all in the communication with other clusters; on the other side, high values of P (>0.75) identify nodes (residues) that spent most of their contacts with residues belonging to clusters other than their own.
We previously applied the method to identify functional regions related to ligand binding (23) and to protein–protein interaction. (24)
We additionally performed an analysis of the interface between spike and ACE2, to quantify the effect of the hepcidin binding, starting from the PCN formalism.
We introduced the interchain degree as the number of contacts that each residue (possibly) shares with residues belonging to other chains. Once we define a given interface, we set two chains, and the corresponding interchain degree identifies the contacts between residues belonging to the two chains. In this work, we focus on the interface between the spike protomer and the ACE2 ectodomain (for instance, chain C of the trimeric spike and the chain D─ACE2 ectodomain─in the spike–ACE2 complex).
Nodes (residues) endowed with a high interchain degree are defined as PCN hotspots at the protein complex interface, and we hypothesized that they play a pivotal role in protein–protein interactions, therefore somehow corresponding to interface hotspots. (25)
To characterize the overall interface strength with respect to single chains, we computed the overall interchain degree, for example, the sum of interchain degree of residues belonging to a single chain, with reference to a given interface. The average value over the number of residues participating on the interface was defined as the average interchain degree. These two descriptors were used, for instance, in this case study, in the comparison of interfaces occurring in the analyzed complexes.

Interface Analysis

Thermodynamic Framework of Protein–Protein Interfaces Analysis

According to the theoretical framework for the PDBePisa server, (26) the complex formation strictly depends on its interaction with the solvent environment.
Given an assembly A = A1, A2, ..., An made up of n subunits Ai, its stability is defined by the standard free energy of Gibbs of dissociation
(5)
where ΔGint is the binding energy of subunits Ai, and is the entropy change upon dissociation. depends upon the ensemble of subunits (dissociation pattern); generally, it is referred to the dissociation pattern that corresponds to the minimum of . Unstable complexes show negative values of .
The binding energy of subunits in solution is given by
(6)
where ΔGSOLV(A) is the change of free energy of solvation of the complex, ΔGSOLV(Ai) is the change of free energy of solvation for the single subunit Ai, and ΔGCONT(Ai, Aj) and ΔGES(Ai, Aj) are, respectively, the contact-dependent (main hydrogen bonds) and the electrostatic contribution of interaction between the subunits Ai and Aj.
It is possible also to define the solvation free energy gain upon formation of the interface between two subunits Ai and Aj ΔGSOLV(Ai, Aj) as the difference in total solvation energies of isolated and interfacing structures; negative ΔGSOLV(Ai, Aj) values correspond to hydrophobic interfaces, or positive protein affinity. The solvation energy for a macromolecular assembly A is computed considering the solvation occurs after a cavity C formation of volume V inside the solvent environment.
(7)
Here, V stands for bulk solvent, and all energies are computed as the difference between vacuum and solvent environments. ΔGCONT(A, V) and ΔGCONT(C, V) include contact-dependent between bulk solvent V, the macromolecular assembly A, and the cavity C.
The contact-dependent free energy change between two subunits is defined as
(8)
where , , and are, respectively, the number of hydrogen bonds, salt bridges, and disulfide bonds between the two subunits Ai and Aj.
The main contribution of the ΔGDISS(A) is the entropic term TΔSDISS(A), that is, the loss of entropy of the solvent upon complex formation, due to the loss of mobility of bound solvent molecules.

Topological Analysis of Interface

We adopted the geometrical descriptors of protein interfaces according to the method of Mei et al. (24) (see Figure 2).

Figure 2

Figure 2. Interface between two chains. In chain A, the length of the peptide segment participating in the interface accounts for seven residues (solid and empty blue bullets), and three of them are directly involved in four links (solid blue bullets). Analogously, chain B accounts for 12 residues in the interface, three of which are in direct contact with residues in chain A.

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Specifically, given the interface between two chains in the complex, we defined the following.
1.

The total number of residues Q for each chain in the interface; this number is in general lower than the above-mentioned total interface degree, due to residues participating to the interface by multiple links.

2.

The length of the peptide segment involved in the interface R.

3.

The interface “roughness” Q/R.

4.

The interface amino acid range IAR = R/N, where N is the total number of residues in the chain.

Figure 2 depicts the interface between two chains: the length of the peptide segment in chain A is seven residues, three of which (solid blue bullets) are involved directly in links with residues of chain B (solid red bullets), and thus the interface roughness is 3/7 = 0.43; the overall degree of the interface in chain A is 4 (all green dashed lines from solid blue bullets). Conversely, chain B spends 12 residues in the interface (solid and empty red bullets), only three of which are directly involved in four links (Q/R = 3/12 = 0.25). We introduced energy descriptors including the topological description provided by the protein contact networks method. Starting from the consideration that the interaction energy is higher if the contact distance is smaller, we introduced a weight for each contact defined as
(9)
which is also the generic element of the interface energy matrix E, defined as
(10)
For each interface, we sorted out the corresponding minor of the interface energy matrix E (corresponding as indices to the rectangular minors of the adjacency matrix), and we introduced the overall interface energy EINT as the sum of eij for each of the active links at the interface and the average value ⟨EINT⟩ over the whole number of residues at the interface. We also analyzed the single residue contribution to the interface energy, defining the value as follows.
(11)
We projected values of onto the complex structure (represented as ribbons and colored as heat map b-factor blue-red) using an in-house Python script, according to the method described in Di Paola et al. (27)

Elastic Networks Models

The anisotropic network model (ANM) and the Gaussian network model (GNM) are two kinds of elastic network models (ENMs) for performing a normal-mode analysis on low-frequency modes of proteins and their complexes. In ANM (28) each amino acid residue is represented by a node placed at its α-carbon coordinate. Residue pairs that fall within a cutoff distance rc of 15 Å are connected by harmonic springs of constant force γ. For a network composed of N nodes, the total potential energy is a summation over all springs in the system, and the (3N × 3N) Hessian matrix H is constructed based on the minimum energy structures that are the same as the crystal structures. Diagonalization of H yields (3N – 6) nonzero eigenvalues (λk) with corresponding eigenvectors (uk). Starting with the first nonzero eigenvalue, λk are sorted in ascending order and also the corresponding uk. The similarity between the ANM eigenvector sets described by two different states/conformations of a system can be calculated according to the following equation. (29)
(12)
Here, the inner product (uivj) quantifies the individual overlap between the ith and jth eigenvectors belonging to the different sets. The overlapk parameter quantifies the overall correspondence between the first k modes of the sets. In GNM, instead of H, the (N × N) Kirchhoff/connectivity matrix is constructed from the structural coordinates with a cutoff distance of 10 Å. eigenvalue decomposition yields (N – 1) nonzero modes for a folded structure. In our work, the GNM is used for evaluating the residue mean-square fluctuations of proteins. Both GNM and ANM analyses were performed by using the ProDy package. (30)

Anisotropic Network Model (ANM) Generated Structural Ensembles of the Complexes

To create coarse-grain ensembles of the structures for the protein complexes in the current study, we utilized the ANM approach, (28,31) in which nonbonded interactions between residues are modeled by elastic springs. We consider first 20 motion levels of protein dynamics that require the lowest energy for deformation. The nonbonded interactions are modeled in this study as a six-step energy function where the spring stiffness is changed in discrete steps. To sample long-range interactions, we consider interaction distances between C-α atoms up to 50 Å. The generated models are extended to all atoms of the residues. The ANM-generated ensembles cover global motions of the complexes. We use Prody 1.10.11 to perform ANM computations. (30)

Perturbation Response Scanning Analysis

The Perturbation Response Scanning (PRS) approach is based on the linear response theory and allows one to evaluate residue displacements in response to external forces. (32) The PRS technique was combined with protein dynamics based on cross-correlations calculated from ANM by constructing the Hessian matrix H.
The 3N-dimensional vector ΔR of node displacements in response to the application of a perturbation (a 3N-dimensional force vector F obeys Hooke’s law F = H × ΔR. The idea in PRS is to exert a force of a given magnitude on the network, one residue at a time, and observe the response of the overall network. The force exerted on residue i is expressed as
(13)
and the resulting response is
(14)
ΔR(i) is a 3N-dimensional vector that describes the deformation of all the residues (in N blocks of dimension 3, each) in response to F(i). A metric for the response of residue k is the magnitude of the kth block of ΔR(i) averaged over multiple F(i) expressed as the ikth element of the N × N PRS matrix, SPRS. The elements of SPRS refer to unit (or uniform) perturbing force. The response to unit deformation at each perturbation site is obtained by dividing each row by its diagonal value.
(15)
The average effect of the perturbed effector site i on all other residues is computed by averaging over all sensors (receivers) residues j and can be expressed as . The effector profile describes the average effect that local perturbation in the effector site i has on all other residues. The maxima along the effector and sensor profiles would correspond to functional mobile residues that undergo allosteric structural change.

SEPAS-Affinity Prediction Method

Using Modeler, (33) we repaired some missed atoms in the spike–ACE2 complex. The repaired three-dimensional (3D) structures are used for performing blind flexible docking, adaptive tempering all atom MD simulations, and ANM. The repaired 3D structure of the complex (ABCD) is composed of SARS-CoV-2 trimeric spike protein (ABC) in association with ACE2 ectodomain (D). One of spike RBDs is in the up conformation. The PDB identification (ID) of the 3D structure of Hepcidin25 (H) is 1M4F.

Affinity Prediction by SEPAS

There are some firmed methods to predict the binding affinity between protein subunits: in conventional methods, a 3D structure of the protein complex is necessary to perform a prediction. To predict the effect of Hepcidin25 on the binding affinity of the SARS-CoV2 spike for ACE2, we used a recently introduced algorithm for the prediction of interaction affinities by using a single subunit. (13,34,35) SEPAS supposes the stability of protein assemblies is encoded in the mechanical softness of binding patches. (36) It is a monomer-based predictor of affinity between protein subunits. By providing a binding patch region on one partner of the desired protein complex, SEPAS performs an affinity prediction using the detected relation between the binding patch stiffness and the stability of the PPIs. The ensembles of the desired complexes generated by ANM or AT-MD simulations in the current work are the inputs of SEPAS.

Adaptive Tempering Molecular Dynamics (AT-MD) Simulations

The structures passed preparation steps before production runs. After minimization, the structures were heated gradually to 300 K using a conventional MD simulation protocol. The Generalized Born implicit solvent (GBIS) is selected to speed up the simulations. (37) In GBIS computations we consider the hydrophobic effects by considering the changes in the accessible surface area (ASA) of the molecules implemented in NAMD 2.13. (38) The utilized force fields in conventional MD and AT-MD simulations are CHARMM 22 and 27. (39,40) To sample faster the conformation landscape of the complex, we run the AT-MD simulation for 2 × 106 steps as one of the history-dependent versions of accelerated MD simulations for the temperature range of 300–320 K. In AT-MD, the potential energy of the system is averaged until the current step; if the current energy of the system is higher than the average energy then the Langevin thermostat adapts the system temperature by rising the current state temperature. (41) Consequently, the system samples faster the conformational space by jumping local shallow energy wells. The structures of the systems are selected every 2 ps of AT-MD simulations for a downstream analysis. In conventional MD, AT-MD, and targeted molecular dynamics (TMD) simulations, we set the time step to 2 fs and do Langevin dynamics to a constant temperature where Langevin damping is set to 1. In regular MD the Langevin temperature is set to 300 K. In AT-MD we let the system temperature fluctuate between 300 and 320 K. We utilized GBIS as an implicit water model, so no pressure control was needed. The utilized force fields in conventional MD and AT-MD simulations are CHARMM 22 and 27, respectively.

Affinity Prediction Using the MM-GBSA Approach

We used a single trajectory-based molecular mechanics with generalised Born and surface area solvation (MM-GBSA) to predict the binding affinity. (42,43) In this method, we use ensembles of ACE2 (D), trimeric spike (ABC), the ABCD complex, trimeric spike with Hepcidin25 in AMR (ABCH), and the ABCDH complex for performing the MM-GBSA affinity predictions. Every noted ensemble is created by sets of 2 × 106 steps of all-atom AT-MD simulations in GBIS implicit solvent using NAMD 2.13. In GBIS-based simulations, we consider the hydrophobic effect by considering changes in the ASA of residues along simulations. The binding free energy, ΔG (association D), of ACE2 (D) to the trimeric spike while Hepcidin25 is bonded to AMR is computed considering following equation.
(16)

Flexible and Blind Protein–Protein Docking

To predict the most probable binding sites of Hepcidin25 on a monomeric spike or trimeric spike protein of SARS-CoV2 with or without ACE2, we utilize SWARM docking. (44,45) By considering the normal modes of ligand and receptor subunits of the desired complex, the flexibility of subunits at the levels of thermal motion energy is implemented in SWARM docking. The best top 10 solutions of standard or democratic clusters are considered.

Targeted Molecular Dynamics (TMD) Generated Structures for the Transition of Spike from Closed to Open States

There are crystallized trimeric spike structures in both “closed” and “open” conformations. To study the properties of the spike along the transition pathway, we utilized the TMD approach. (38) General simulation conditions are described in the AT-MD section. In TMD, a 3D structure is guided toward a final “target” state using steering forces. In brief, the root-mean-square (RMS) distance between the current coordinates and the target structure defines the external force. The force on a system is defined by the gradient of the potential defined as follows.
(17)
The spring constant scaled by atoms of the TMD group and is presented as Z in the equation. RMS stands for the instantaneous deviation of the structure from the target final state, and RMS′ evolves linearly from the starting structure to the target structure over the simulation time. The starting structure of the spike in the closed state is derived from the structure with PDB ID 6vxx after minimization is performed and AT-MD production steps are performed in the GBIS condition. The target structure is the spike with one chain in the open state that is derived from the structure with PDB ID 6vsb after minimization and AT-MD production steps are performed in the GBIS environment. To sample the spike structures from closed to open states we perform duplicated 3 × 106 steps of TMD simulations.

Energy Transport Networks

To further support the identification of the AMR, we analyze the transport of energy across the spike–ACE2 complex. The eigenmodes of the ANM of the spike–ACE2 complex can be analyzed to locate energy-transport networks in the complex. We briefly summarize here how the normal modes of the complex, obtained for the ANM, can be used to identify pathways along which energy transport takes place. These pathways may include a combination of energy transfer along the main chain of the spike protein or ACE2 and energy transfer across noncovalent contacts. Details of the method are provided elsewhere.46–48 The nodes of the network are the residues of the spike protein and ACE2 that form the complex. The weights are expressed in terms of the matrix elements of the energy current operator S, which in harmonic approximation can be written in terms of the Hessian matrix H and eigenmodes e of the complex, which we model with the ANM. The matrix elements of S are used to calculate the mode diffusivity for energy transfer between a pair of residues, namely, A and A′. The energy diffusion coefficient DAA′ is computed in terms of the mode diffusivities. Specifically, we computed the first 1000 modes of the ANM for the spike–ACE2 complex, and we used modes 900–1000 to compute DAA′, which we take as the sum of all for those modes. The modes need to be of sufficiently short wavelength so that the energy transfer between residues can be described via a Markov model approach. The lowest-frequency modes are of too long wavelength to satisfy this model. Modes in the 900–1000 range, also other modes lower than these, yield results that, upon analysis of the network, do not vary with the selected modes. The time constant τAA′ between A and A′ per degree of freedom is calculated as
(18)
where dAA′ is the Euclidean distance between A and A′, which is taken as the center of mass distance.
Local energy diffusion occurs along a path between these two center-of-mass of regions A and A′, essentially along a one-dimensional path; thus, we include the factor of 2. We found that an analysis of the network of residues weighted by the time constants for energy transfer between residue pairs yields useful insights into residues and regions of a protein that mediate allosteric control. (48−50) We identify the shortest paths in time between a residue pair of the complex. Residues that lie along the shortest paths have been identified as contributing to allosteric regulation, as these residues control information flow in the complex. (50−52) Since our network is comprised of time constants for energy transfer between residues, we determine residues that lie along the shortest paths in time. This is quantified by the quantity C for node (or residue) v as
(19)
where N is the number of nodes in the network, and σst(v) takes the value of 1 when the shortest path in time that links nodes s and t passes through node v and is otherwise 0. We report the residues with the largest values of C and compare with the previously identified AMR.

Overview of Computational Workflow

Figure 3 reports the scheme of the general overflow of the multifaceted computational approach.

Figure 3

Figure 3. General workflow of the multifaceted computational approach to the analysis of the allosteric behavior of the spike–ACE2 complex in the perspective of inhibition by hepcidin.

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Results and Discussion

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In our previous work we reported for the first time the presence of an Allosteric Modulation Region in the SARS-COV2 spike protein. The introduced region was mapped on the spike in its open state while it was associated with ACE2. In the current study we seek to find the AMR in different conformational states of spike protein. Allosteric drugs modulate the function of the desired proteins over the distance between the drug-binding site region and the protein’s functional site. In the case of SARS-COV2 spike protein, finding allosteric effectors that modulate the binding and dynamic properties of the spike is of interest. We suggest AMR as a possible binding site for allosteric drugs. To this end, we perform multidimensional computations to study the effect hepcidin–AMR interactions and the consequence of the interactions on spike dynamics and its affinity for ACE2.
The performed simulations in the current work are presented in Figure S1. As presented in the Methods section, the trimeric spike bonded to ACE2 (ABCD) and the trimeric spike bonded to ACE2 and hepcidin (ABCDH) are passed relaxation steps by performing AT-MD simulations. To define the optimized structure of the complexes, we measure changes in the root-mean-square deviation (RMSD) and ASA for the systems (Figure S2). These optimized complexes are used in ANM, TMD, and MM-GBIS computations.

Network Clustering of Spike in Open and Closed States

We report results for the application of the network clustering methodology to the identification of functionally active regions in the free forms of the SARS-CoV2 spike protein (open/closed conformation). In a previous work, we identified with the same methodology a putative allosteric site in the spike proteins of SARS-CoV and SARS-CoV2 with regard to their binding with ACE2. (13)Figure 4 reports the results of network clustering (two clusters solution) on the closed conformation of the SARS-CoV2 spike protein: in the closed conformation, the two clusters represent two blocks whose probable role is to contribute to the stability of the trimeric structure of the spike. The corresponding active regions (in red in Figure 4B) lie in the inner core of the trimeric structure. Similarly, Figure 5 reports the network clustering results (two clusters) for the open conformation of the SARS-CoV2 spike protein: the clustering partition and the corresponding active region (nonnull P values) are completely different from those of the closed conformation. The region opening to become prone to the binding of the spike protein of the virus with human receptor ACE2 is a cluster in its own; the rest of the protein structure is the second cluster. The active region is located in the hinge of the opening part of the protein structure. Thus, in the open conformation, the active region residues contribute to the protein activity rather than to its stability. The dramatic change in the structural-functional role of the same residues going from closed to open conformation is a signature of the relevant allosteric properties of the protein. (53)

Figure 4

Figure 4. Network clustering of closed conformation of the SARS-CoV2 spike protein: (A) the two clusters in the closed conformation are reported in green and red; (B) the active region (P > 0) in the two clusters partition.

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Figure 5

Figure 5. Network clustering of open (1-up) conformation of the SARS-CoV2 spike protein: (A) the two clusters in the closed conformation are reported in green and red; (B) the active region (P > 0) in the two clusters partition.

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Comparison of Conformational Dynamics of S Proteins in Three States

Collective motions are essential for protein functions including allosteric interactions. Herein, the ANM method was used to compare and analyze the collective motions of the SARS-CoV2 spike proteins in closed, open, and ACE2 bound states. The overlap maps of the 10 softest modes of S proteins between closed and open states as well as open and bound states are shown in Figure 6A,B. During these two conformational changes, most of the global modes were not conserved, along with a weaker correlation (<|0.6|) and reordering of modes. Two notable matching modes were found: mode 2 in the closed state corresponds to mode 3 in the open state, with an overlap of 0.86. Examination of the individual modes showed that the S protein induced high fluctuations at two receptor-binding domains still at the “down” position. In addition, mode 2 in the open state corresponds to mode 4 in the bound state, showing the “up” and bounded RBD motions. This clearly suggests that the dynamics of the S protein is largely affected by both the opening of the RBD and the binding of the ACE2 protein, underlying the RBD-dependent dynamics. We further calculated and compared the square fluctuations for S proteins among three different states, based on the first 20 ANM modes, to show how the dynamics difference happened. As a result, the dynamics difference of closed, open, and bound S proteins can be captured mostly at the RBD region (Figure 6C). It can be seen that, going from the closed to the open state, the fluctuation of up RBD becomes much higher compared with down RBD, while the other regions maintain similar fluctuations. The high plasticity of up RBD in open state can promote its binding with the host receptor ACE2. From the open state to the ACE2-bound state, the fluctuation of the up RBD becomes smaller clearly, as the binding process will make the RBD, especially the binding interface, more stable as expected. Besides, the binding of ACE2 not only leads to decreased flexibility in the RBD but also to increased flexibility of all other regions at the distal sites from the binding interface. This may suggest that the ACE2 binding can also affect residues in distal sites by modulating long-range allosteric communications caused by the conformational change of binding interface (RBD). In summary, S protein undergoes a spectrum of conformational changes going from the more stable down to the more flexible up conformational switch that can promote binding with the host receptor ACE2 and the small stability in a bound complex state. The conformational study of S trimers shows that the dynamical coupling range varies widely among different conformations, while these different binding conformations may lead to long-range allosteric communications.

Figure 6

Figure 6. Intrinsic dynamics of S proteins in closed, open, and bound states. (A) Overlap of 10 ANM modes between the closed and open states. (B) Overlap of 10 ANM modes between the open and bound states. (C) The square fluctuations of S proteins in three states based on the first ANM modes. The bounded RBD is most stable in the closed state (blue) but has the largest flexibility in the open state (green). The bounded RBD in the complex state has the lowest stability (red).

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The S-ACE2 Complex Shows the Highest Allosteric Potential

In ENM global motions, the residues with lower fluctuations form the hinge sites critical for the protein functions and allosteric regulations. Herein, the local minima of mean-square fluctuations based on the three GNM slowest modes are predicted as hinge sites, which are listed in Table 1. In all states, the distribution of hinge sites is relatively uniform along a sequence. Especially for the closed state, hinge sites distributed on each chain with a quite large degree of symmetry. Here, we focus on the hinge sites of S monomers that share the conformational change among three states. As shown in Figure 6A, the predicted hinge sites of the closed state are mainly located at nonfunctional domains but occupy some topologically important regions including protein–protein interactions between each S monomer and interfacial residues between S1 and S2 domains. Most of these hinges in the closed state are maintained, but hinge sites in the N-terminal domain (NTD) shift to RBD in the open state (Figure 6b).
Table 1. Hinge Sites in the SARS-CoV2 Spike–ACE2 Complex
chainABCD
hinge sites317N, 326I, 474Q, 475A, 477S, 479P, 485G, 487N, 488C, 533L, 543F, 545G, 578D, 582L, 595 V, 612Y, 620 V, 621P, 623A, 643F, 650L, 654Z, 664I, 673S, 693I, 730S, 735S, 742I, 743C, 746D, 761T, 780Z, 783A, 854 K, 856N, 858L, 860 V, 873Y, 949L, 986P, 991 V, 1005Q, 1058H14Q, 33T, 37Y, 55F, 56L, 59F, 221S, 272P, 295P, 309Z, 322P, 590C, 698S, 752L, 753L, 758S, 971G, 983R, 990Z, 1146D320 V, 369Y, 371S, 472I, 489Y, 591S, 658N, 662C, 667G, 671C, 695Y, 703N, 730S, 776 K, 826 V, 866T, 948L, 966L, 996L, 1013I, 1015A, 1146D19S, 31K
Such a hinge shift of RBD reveals implications for the further binding process. After the binding of the ACE2 receptor, a similar trend was found with most of these hinges in the open state being maintained. The most important finding is that there are several hinge sites present at the AMR in the bound complex, including Asp578, Leu582, Ile742, and Asn856 (the red circle in Figure 7c), all with P > 0.8. On the basis of these findings, we proposed that the conserved hinge sites in both closed and open states may alter the transitions of up and down forms, and the hinge sites located the AMR in the complex state may allosterically impact RBD-ACE2 binding.

Figure 7

Figure 7. Allosteric properties of S proteins in closed, open, and bound states. (a–c) Distributions of the hinge sites (green beads) based on the first three GNM modes. (d) Comparison of effectiveness for three S proteins. (e) Effectiveness profiles for three S proteins, while their predicted AMR are labeled with black stars.

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On the basis of an ANM calculation, the perturbation-response scanning (PRS) approach was employed to quantify the allosteric effect of each residue in the prtein structures on all other residues in response to an external perturbation. In our previous study, (13) we showed that effectiveness is a good indicator to describe the allosteric properties of AMR and identified the key residues for which perturbation provokes structural dynamical changes at a long distance. Here, we also adapted effectiveness based on a PRS calculation to describe the allosteric potential of S proteins in closed, open, and bound states.
As shown in Figure 7D, the box plot shows that S proteins in closed and open states show similar effectiveness values, but the S protein in the bound state had a statistically higher effectiveness than the other two states (under p < 2.2 × 10–6 by Wilcoxon signed-ranked test), which shows that the S protein in the bound or “complex” state has higher allosteric potential. The allosteric profiles based on PRS would give provide more insight into the signaling ability of each residue (Figure 7E).
As we can see, S proteins with closed and open states display very similar profiles with relatively low effectiveness values, while the predicted AMRs (black stars) in both states show some valleys with very small signaling ability. But in the bound state, the S protein presents a more marked allosteric profile, while some peaks are mainly located at some functional domains, including cleavages and heptad repeats of three S monomers. In particular, additional peaks were found at the predicted AMR in the S-ACE2 complex. Our previous results suggest that the SARS-CoV2 AMR has stronger allosteric character than the AMR in SARS-CoV, and our current findings suggest this region in the S-ACE2 complex still has higher allosteric potential than two unbound states.
In the S-ACE2 complex, the AMR corresponds to hinge sites and has higher allosteric potential, thus the modulation of conformational dynamics of a binding interface by an allosteric ligand interaction at the AMR. Since the hinge sites and the AMR in the S-ACE2 complex also provide some potential drug binding sites, we recommend ongoing efforts to dock hepcidin peptide at the AMR in the “complex-like” state, not the “apo-like” state, to inhibit the binding interface.

Energy Flow in Spike–Protein Complexes

We compare the AMR identified by an analysis of contact networks for the spike–ACE2 complex with results of an analysis of energy-transport networks computed for the same complex. We specifically determine residues that mediate energy transport in the complex. As discussed above, this is quantified by C for node (or residue) v (see eq 19).
We computed C for all residues of the complex and plotted those residues with the largest values in Figure 8. Five residues of the spike–ACE2 complex stand out with values of C that are at least 70% larger than any other values computed. Those residues, which all have values of C between 0.175 and 0.195, are identified in red in Figure 6. The relatively large values of C for these residues indicate they lie along a much larger number of shortest energy-transport paths between any two residues of the complex than do other residues. Also plotted in Figure 6 are the residues identified as having the largest values of the participation coefficients (4). All of these residues, those found from the energy-transport network analysis and those identified with the largest participation coefficients, lie in the same region of the complex, identified in earlier work as an Allosteric Modulation Region. (13) We have seen in earlier work on allosteric proteins that residues that control energy flow through the network play a central role in allosteric regulation; (23,54) a mutation of such sites can alter the allosteric control. (48,49,51)

Figure 8

Figure 8. Spike–ACE2 complex, with chain A, B, and C shown in green, cyan and magenta, respectively, and the ACE2 ectodomain in yellow. The five residues identified as having the largest influence on energy transport in the complex are indicated in red. They all lie in the AMR, previously identified as containing the residues with the largest participation number in the complex, labeled in dark blue.

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Hepcidin-Binding Changes the Network Clustering in Spike

In the following, we report the results of network clustering on the spike–ACE2 complex, with and without hepcidin for the sake of comparison. Figure 9 reports the network clustering for the equilibrated form of the spike–ACE2 complex.

Figure 9

Figure 9. Network clustering of the equilibrated form of the spike–ACE2 complex. (a) Cluster partition; (b) participation coefficient P map; (c) complex chains.

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The cluster partition is very similar to that found by Di Paola et al.; (13) the detailed list of AMR residues (P > 0) is provided in Table 2: they are all in chain C carrying the RBD in direct contact with ACE2. Only ARG328 is endowed with relevant intermodule communication competencies (P ≥ 0.75), reported in italic font type in Table 2.
Table 2. Allosteric Modulation Region (AMR) in the Equilibrated Spike–ACE2 Complexa
namepositionP
VAL3270.44
ARG3280.89
PHE3290.19
PRO3300.17
ASN3310.56
CYS5250.13
PRO5270.51
LYS5280.31
LYS5290.36
SER5300.44
a

All residues are located in the C chain. In italic the most competent residue (Arg 328) in intermodule communication (P ≥ 0.75).

Similarly, Table 3 reports the identity, position, and participation coefficient P of residues in the AMR for the equilibrated conformation of the complex spike + ACE2 + hepcidin. After performing blind and flexible docking between HPC and ACE2-bonded spike chain C, we find that HPC binds preferentially to AMR.
Table 3. Allosteric Modulation Region in the Equilibrated Spike–ACE2 + Hepcidin Complexa
namepositionP
PHE3420.61
ASN3430.36
ALA3440.36
THR3450.56
ARG3460.31
TRP3530.61
ASN3540.84
ARG3550.31
LYS3560.27
PHE3740.40
SER3750.56
THR3760.56
ASP3980.51
SER3990.66
PHE4000.33
VAL4010.19
VAL4070.23
PRO4120.56
LYS4240.19
LEU4250.56
PRO4260.64
ASP4270.75
THR4300.19
VAL4330.19
ILE4340.36
ALA4350.64
TRP4360.80
ASN4370.75
TYR5080.1736
ARG5090.5950
VAL5100.79
VAL5110.3306
VAL5120.1900
a

All residues are located in the C chain. In italic font type the most competent residues in intermodule communication (P ≥ 0.75) are given.

The presence of hepcidin strongly changes the allosteric modulation scenario: in this complex, we found 33 residues (more than 3 times the number in the complex without hepcidin) all in the C chain, with five residues with P ≥ 0.75 (15%). The average value of P in the complex without hepcidin is ∼0.001; in the presence of hepcidin it rises to 0.0035. Thus, the hepcidin binding entails an enhanced allosteric character of the complex spike–ACE2.
Noticeably, again no active residue is within the RBD or in the RBM, addressing a purely allosteric nature of the communication between complex domains.

Hepcidin Binding Changes the Properties of Spike–ACE2 over Distance

Table 4 reports thermodynamic results of the application of the Proteins, Interfaces, Structures, and Assemblies (PISA) to the complex spike + ACE2 (with and without hepcidin), compared to free forms of the spike protein (open and closed). The stability of the closed form subunits is uniform, but the symmetry is broken upon activity (open conformation), since all subunits become less stable, with a quite high change in the C subunit, shifted to the open RBD conformation. The effect is mirrored as well in the interface interactions between subunits, which in general decrease upon activation, with a special regard to the B–C interface. In the complexes of the spike with ACE2, the stability of the subunits is strongly increased but not the interface binding between spike protomers (similar to values of the open form). The binding contribution to the general stability, however, strongly increases, passing from a fairly positive (unstable conformation) to a strongly negative (stable conformation) value. That means the ACE2 binding provides a large contribution to stabilize the complex molecular structure. In the presence of hepcidin, the ACE2–spike binding becomes a little bit more stable, whereas the whole complex (spike + ACE2 + hepcidin) gains more stability with respect to the spike + ACE2 complex (−130.7 kcal/mol in the spike + ACE2 vs −183.4 kcal/mol in the spike–ACE2 + hepcidin).
Table 4. Thermodynamic Parameters of Protein–Protein Interface (from PISA) (55)
 chainsΔGSOLV(Ai) kcal/molpairsΔGSOLV(AiAj) kcal/molΔGint kcal/molΔGDISS kcal/molTΔSDISS kcal/mol
S+AA–1080.1A-B–43.3–130.7–9.716.0
 B–1073.3B-C–39.6   
 C–1050.0A-C–42.8   
 D–453.3C-D–5.0   
S+A+HA–1014.7A-B–46.8–183.4–5.615.9
 B–1002.4B-C–46.8   
 C–1011.9A-C–43.2   
 D–524.7C-D–7.4   
S closedA–919.1A-B–52.934.5170.937.2
 B–919.5B-C–52.8   
 C–919.5A-C–52.8   
S openA–888.3A-B–49.628.5163.837.2
 B–864.2B-C–45.9   
 C–736.9A-C–50.8   
Tables 5 and 6 report in detail the results for the interface analysis adopting the PCN approach. Results confirm that hepcidin contributes to stabilize the spike–ACE2 binding, mainly in terms of an increased number of contacts.
Table 5. Interface Analysis through PCNs: The Complex Spike–ACE2 (without Hepcidin)
 
(Q/R)Ai
⟨kAiAjEM⟩
A-B
Ai910.0920.78170.062481.3437.560.15
Aj940.1150.643     
B-C
Ai880.0890.77972.302841.5844.290.16
Aj920.1120.643     
A-C
Ai910.1090.65468.012511.3036.550.15
Aj950.0960.781     
C-D
Ai110.3670.0249.38251.043.620.14
Aj130.0390.563     
Table 6. Interface Analysis through PCNs: The Complex Spike–ACE2 (with Hepcidin)
 QAi(Q/R)Ai
⟨kAiAjEM⟩
A-B
Ai1090.1010.85290.802941.2744.690.15
Aj1220.1460.655     
B-C
Ai860.0800.84664.202111.2531.450.15
Aj830.1020.643     
A-C
Ai1460.1340.853107.013381.2350.550.15
Aj1290.1200.848     
C-D
Ai120.2450.03910.50341.265.340.16
Aj150.0450.560     

SEPAS-Affinity Results

We are interested to find the possible binding sites of hepcidin25 on the spike of SARS-CoV2. As proteins have their own intrinsic motions, we need an efficient algorithm that considers protein motion in docking. SWARM docking performs flexible docking by including normal mode dynamics of receptor and ligand molecules. The blind docking option enables us to seek the spike protein for possible binding sites of hepcidin25 without prejudice. When we introduce trimeric spike protein (ABC) as receptor and hepcidin25 (H) as ligand molecule to SWARM, it returns us the best 10 complexes (Figure 10). In two structures of the top 10, hepcidin25 binds around/to AMR of the upward subunit of the trimeric spike with binding energy of −22 and −24 kcal/mol (Table 7).

Figure 10

Figure 10. Result of SWARM docking is presented. Trimeric spike protein acts as a receptor protein and hepcidin25 acts as a ligand molecule. Chain C of the spike trimer is presented as blue wire, and other chains of the spike are presented as gray dots.

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Table 7. Top 10 Solutions of SWARM Docking Are Presented for Trimeric and Monomeric Spike Protein as Receptor of Hepcidin25a
# solutionenergy, kcal/molposition
trimeric spike (chain ABC)
1–31.7 
2–29.44 
3–28.92RBM
4–27.3 
5–24.13 
6–23.95near AMR[27.86]
7–22.84 
8–21.94near AMR [24.70]
9–21.89 
10–21.85 
monomeric spike (chain C)
115.63 
2–13.1 
3–26.14AMR [19.98]
4–10.39 
521.68 
63.5 
74.78 
8–10.76 
90.59 
10–18.83RBM
a

The stalk region is laid on the body of spike in this section. The distance between Hepcidin25 and AMR is presented in brackets.

SWARM-docking results indicate that hepcidin25 also binds to the ACE2 binding site of spike protein (Table 7). To test how much the AMR accessibility affects hepcidin25 binding to AMR, we test the flexible docking between the first 700 residues of spike protein chain C and hepcidin25: results indicate that hepcidin25 binds with higher energy to the monomer form of spike. It also declares that the flexible structure of hepcidin25 and spike trimer are crucial for finding the binding sites of hepcidin25 on spike. The results of SWARM docking indicate that hepcidin25 also has a high affinity for the ACE2 binding-site region of monomeric or trimeric spike protein, suggesting that hepcidin25 may be a competitive inhibitor of ACE2 for binding to the SARS-CoV2 spike protein. The SWARM-docking results provide us a possible 3D structure of the complex between hepcidin25 and the AMR of spike protein. Our previous observations suggested that changes in the AMR structure affect the affinity of spike for ACE2 protein. These results encourage us to study the effect of hepcidin25 binding to AMR on the affinity of spike for ACE2.
We predict the affinity of trimeric spike for ACE2 when hepcidin25 is bonded to the AMR of spike. SEPAS allows us to predict the affinity of spike for ACE2 considering changes in the mechanical softness of the spike binding site for ACE2. To perform affinity predictions, we consider the ensembles generated for trimeric spike in association to ACE2 in the presence (ABCD-H) or in absence (ABCD) of hepcidin25 in AMR by ANM as a coarse-grain approach or by utilizing the structural ensembles generated by all-atom AT-MD simulations.
The presented results in Figure 11 suggest that the binding of hepcidin25 to the AMR of spike possibly increases the binding affinity of spike for ACE2 to some extent. Also we predict the binding affinities using the MM-GBSA approach as another established method. We predict the binding energy of ACE2 (D) to the trimeric spike (ABC) of SARS-CoV2 in two different cases; when hepcidin25 binds to AMR (ABCDH) or in the absence of hepcidin25 (ABCD).

Figure 11

Figure 11. SEPAS-predicted affinity of trimeric spike protein for ACE2 in the presence of hepcidin25 in the AMR (ABCD-H) or its absence (ABCD).

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The binding energy of ACE2 to trimeric spike is −52 kcal/mol (standard error of mean (SEM) = 0.24) when hepcidin25 bonded to AMR in comparison to its absence in AMR when the binding affinity of ACE2 for spike is −47 kcal/mol (SEM = 0.23). The predicted change in the affinity of ACE2 to trimeric spike upon binding of hepcidin25 to AMR suggests that the binding of hepcidin25 to AMR significantly (p < 0.05) increased the affinity of ACE2 to spike. The density plot of the MM-GBSA-based affinity is presented in Figure S3.
To study the possible effects of hepcidin25 binding to AMR on the dynamics of spike protein, we compute the angle between three residues (residues 494, 538, and 1042) of spike protein subunit C. These residues reside in the binding site of ACE2, around the AMR, and in an interface of the stalk region of chain C and other spike chains.
Using SWARM docking, we find a possible binding site of hepcidin25 on chain C (Table 7 and Figure 12). As we observe that hepcidin25 binds to the AMR of chain C in single-chain form (Figure 12A), we measure the changes of the noted angle in ANM-generated ensembles of chain C alone, chain C with hepcidin25 in its AMR (CH), chain C binds to ACE2 (CD), and when chain C is bonded to ACE2 and simultaneously hepcidin25 binds to AMR of chain C (CDH). The noted angle for chain C in different assemblies has its own spectrum; we assume the lowest value of the angle as the closed state because the binding site of ACE2 is close to the body of spike trimer in such a small angle. We compute the difference between the measured angle and the smallest angle of chain C in each assembly. Therefore, if the measured deviation is a small negative value, it means that the observed state of the ACE2 binding site of chain C is similar to the proposed closed state.

Figure 12

Figure 12. Effect of hepcidin25 on the dynamics of spike subunits. (A) Docked binding sites of hepcidin25 on chain C. (B) Measured angle is reported for Chain C, chain C with hepcidin25 in AMR (CH), chain C in association with ACE2 (CD), and hepcidin25 bonded to AMR of chain C in complex with ACE2 (CDH). (C) Results of hepcidin25 binding to chain C in association with other subunit chains and ACE2 (ABCD-HPC) or without hepcidin25 (ABCD).

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We present the measured deviation from the closed state in Figure 12B for chain C in the absence of other spike subunits and in Figure 12C for the complete spike. It is declared in Figure 12 that the binding of hepcidin25 in the AMR encourages chain C to spend more time in its closed state. We know that the closed state of spike has a lower tendency for ACE2 because of topological barriers ahead of spike subunits in the closed state for binding to cell-bonded ACE2. (46,47) While we predict that the binding of hepcidin25 to the AMR increases the affinity of spike chain C for ACE2 to some extent in the open state, we also observed that the binding of hepcidin25 to AMR converts the spike RBD from an upward state into the closed state, which naturally has a lower tendency for interaction with ACE2. These observations in addition to the possible action of hepcidin25 as a competitive inhibitor of ACE2 for interaction with spike suggest hepcidin25 as a possible therapeutic agent for COVID19.

The Dynamic Transition of Spike from the Closed to Open State Dictates AMR–Hepcidin Interactions

In the previous section, we study the interaction of hepcidin25 with spike protein. The considered spike was open and ready for binding to ACE2. Spike protein is in an inactive, trigger-ready, and closed state before binding to its cognate ligand. Upon interaction with host receptors, ACE2, the closed state of spike is changed to the open state. To study the binding of hepcidin25 to transient structures of spike from closed to open states, we utilized Targeted Molecular Dynamics simulations. To sample the spike structures, we perform the TMD simulations using spike in a trimeric state with all chains in the closed state, PDBID 6vxx, as the starting point and trimeric spike with one subunit in the open state (PDBID 6vsb) as the target structure. The resulting trajectories provide us possible 3D structures of spike along the transition from closed to open states (Figure 13A). We select six representative structures along the closed to open state pathway. The representative structure is introduced to the SWARM flexible docking algorithm. SWARM generates 10 first elastic normal modes for receptor and ligand structures to consider the natural motion of the structures that reside in the bottom of the energy curve. This approach not only implements the flexibility but also generates the possible structures around the seed structure for expanding the available binding sites. The docking results are presented in a clustering list that we consider the top 10 clusters.

Figure 13

Figure 13. Results of hepcidin25 interaction with spike in different states. (A) Output of the TMD simulation for sampling the spike structure from closed to open states. The most important residues of the AMR, P > 0.5, are declared by sphere. (B) SWARM-predicted affinity of hepcidin25 for the RBM of spike chains along the transition from closed to open states. The horizontal axis represents the distance of the state to the open conformation of spike by computing the RMSD between the considered frame and the target structure in TMD. (C) Same story but for affinity between hepcidin25 and the AMR in monomeric spike. The size of the circle (B, C) correlates with the size of the SWARM suggesting the top cluster corresponds to the considered representative introduced receptor.

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The results reported in Figure 13B indicate hepcidin25 binds to the receptor binding motif of spike. The diameter of circles in Figure 13 correlates with the population of the considered cluster defined by SWARM. We introduced trimeric spike to the SWARM algorithm. Hepcidin25 binds to the RBM of different chains in trimeric spike. If it binds to the chain that will stand up then we colored the corresponding circle red. The utilized samples of the trimeric spike structure from closed to open states show one meaningful complex with hepcidin25 in the AMR. The rectified spike chain in the trimeric form of spike protein presents a binding site around the AMR for hepcidin25 when it is near to the open conformation. In this state the distance between the center of mass of hepcidin25 and the AMR reaches 19.1 Å, but hepcidin25 is not successful for penetrating into the AMR; instead of binding into the AMR cavity it binds to the external wall of the AMR. Because the spike structure suffers structural changes in the RBM during the transition from closed to open states, the RBM shows a different affinity for hepcidin25 along the transition path (Figure 13B). It is indicated in Figure 13B that the RBM is becoming a better binding site for hepcidin25 upon transition of trimeric spike to the open state. We noted in a previous part that the considered spike was in the trimeric state, and by using TMD, we simulate the transition of one chain from the closed to open states in the context of the trimeric spike. Because of possible hindrances, hepcidin25 did not find the AMR in spike. When we introduce the representative transition structures of spike in monomer form to SWARM as a receptor, we detect that hepcidin25 binds to the AMR in most members of the top docking clusters (Figure 13C). It is indicated in Figure 13C that the affinity of hepcidin25 is decreased for AMR upon transition of spike to the open state.
We compute the interaction energy between residues of AMR and other parts of spike in trimeric form or the interaction of the AMR with itself (Figure 14) along the transition of spike from the closed to open state. The presented results in Figure 14 indicate that, upon transition of spike from the closed to open state, the AMR shows less contact with other regions of spike but becomes tighter because of its interactions with itself increased by the transition of spike to the open state (Figure 14). These results may rationalize why hepcidin25 has a lower chance for binding to AMR in the open state of trimeric spike. Our computations suggest that hepcidin25 preferentially binds to the RBM of spike in the trimeric open state while hepcidin25 tends to bind to AMR in closed monomeric spike protein. These observations comply with the above-stated conclusion that hepcidin25 upon binding to the AMR tends to convert spike from the open state to the closed and safer state.

Figure 14

Figure 14. Pair-interaction potential is computed for the AMR. The relative interaction potential is presented in the vertical axis. More negative potential means a higher amount of interactions. The horizontal axis represents the distance of the state to the open conformation of spike by computing the RMSD between the considered frame and the target structure in TMD.

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Conclusions

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By employing a multifaceted computational approach on complexes of SARS-Cov2 spike protein, we gave a proof-of-concept that the binding of hepcidin to the complex ACE2–spike can promote the allosteric character of AMR interactions by enhancing the stability of the intermolecular interactions. PCN results highlighted active residues in the complex of ACE2-spike-hepcidin and that the hepcidin presence enhanced the strength of interactions at ACE2–spike interface. These results came along with the SEPAS and ENM results, both pointing to an increased affinity of the binding. Eventually, energy flow results converged toward the same allosteric region in the modulation of the ACE2–spike complex, with and without hepcidin.
Thus, all the adopted computational methods converge to a very consistent picture of the SARS-Cov2 spike protein binding process to ACE2 receptor highlighting the allosteric character of the process and confirming the presence of an allosteric modulating region playing a crucial role in determining receptor binding affinity. These results can be interpreted as a case study of a possible integration of different computational techniques looking for their invariant features. In this case, the take-home message is the consistency of the hepcidin role in allosteric modulation.
Hepcidin, as often happens in pharmacology, while exerting a possible antagonist role in spike protein receptor binding (therapeutic effect), plays at the same time a crucial role in the iron balance of blood cells, and this implies any alteration of hepcidin concentration can result in a risk of blood clotting. In a similar vein, the structural (and sequence) similarities of spike protein segments with hepcidin offer a possible explanation to COVID19 pathogenesis as well as to the rare thrombotic cases associated with vaccination.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.2c00154.

  • Schematic illustration of simulations performed, plots of RMSD values and accessible surface area versus time, plots of density versus kilocalories per mole (PDF)

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Author Information

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  • Corresponding Authors
  • Authors
    • Hamid Hadi-Alijanvand - Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences, Zanjan 45137-66731, Iran
    • David M. Leitner - Department of Chemistry, University of Nevada, Reno 89557, Nevada, United States;  Orcidhttps://orcid.org/0000-0002-3105-818X
    • Gennady M. Verkhivker - Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange 92866, California, United States;  Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine 92618, California, United States;  Orcidhttps://orcid.org/0000-0002-4507-4471
    • Peixin Sun - Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
    • Humanath Poudel - Department of Chemistry, University of Nevada, Reno 89557, Nevada, United States
    • Alessandro Giuliani - Environmental and Health Department, Istituto Superiore di Sanità, Rome 00161, Italy;  Orcidhttps://orcid.org/0000-0002-4640-804X
  • Notes
    The authors declare no competing financial interest.

    Data and Software Availability. All utilized computational tools like programs and servers are freely available for academic users. The information and download links of the utilized tools are presented in this section. All of the performed conventional MD simulations are carried out by utilizing NAMD 2.13. It is freely available at https://www.ks.uiuc.edu/Research/namd/2.13/features.html. CHARMM force fields for proteins are downloaded from the freely accessible repository at https://charmm-gui.org/?doc=toppar. PCN are computed on Matlab, version 2021a; heat maps of network indicators are produced through PyMol v2.4.2; full details of the method can be found in a previously cited reference. (27) The presented molecular structures are generated using VMD 1.9.2 that is freely available at https://www.ks.uiuc.edu/Research/vmd/vmd-1.9.2/. To perform monomer-based affinity prediction, SEPAS software is utilized. The software is published and described at https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.9b00594. SEPAS is freely available at http://biophysics.ir/affinity/. To perform flexible docking we utilized SWARM. (44) The ANM-based ensembles are generated using Prody. It is freely available at http://prody.csb.pitt.edu/. Software for figures: Figure 1: generated by PyMol v2.4.2; Figure 2: generated in PowerPoint (MacOffice 2016); Figure 3: generated by PyMol v2.4.2. Figure 4: generated by PyMol v2.4.2. Figure 5: generated by PyMol v2.4.2. Figure 6: generated by VMD 1.9.2. Figure 7: generated by VMD 1.9.2. Figure 8: generated by excel and R. Figure 9: generated by VMD 1.9.2., R and excel. Figure 10: generated by VMD 1.9.2. and excel. Figure 11: generated by excel. Figure 12: panels (a) and (b) have been generated by Prody (http://prody.csb.pitt.edu/), panel (c) by VMD 1.9.2. Figure 13: panels (a–c) have been generated by VMD 1.9.2. and panel (d) by R.

Acknowledgments

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H.H.-A. acknowledges the Institute for Advanced Studies in Basic Sciences at Zanjan, Iran, for supporting this work. G.H. is thankful for the support by the National Natural Science Foundation of China (31872723).

References

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    The Discovery of a Putative Allosteric Site in the SARS-CoV-2 Spike Protein Using an Integrated Structural/Dynamic Approach
    Di Paola, Luisa; Hadi-Alijanvand, Hamid; Song, Xingyu; Hu, Guang; Giuliani, Alessandro
    Journal of Proteome Research (2020), 19 (11), 4576-4586CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    SARS-CoV-2 has caused the largest pandemic of the twenty-first century (COVID-19), threatening the life and economy of all countries in the world. The identification of novel therapies and vaccines that can mitigate or control this global health threat is among the most important challenges facing biomedical sciences. To construct a long-term strategy to fight both SARS-CoV-2 and other possible future threats from coronaviruses, it is crit. to understand the mol. mechanisms underlying the virus action. The viral entry and assocd. infectivity stems from the formation of the SARS-CoV-2 spike protein complex with angiotensin-converting enzyme 2 (ACE2). The detection of putative allosteric sites on the viral spike protein mol. can be used to elucidate the mol. pathways that can be targeted with allosteric drugs to weaken the spike-ACE2 interaction and, thus, reduce viral infectivity. In this study, we present the results of the application of different computational methods aimed at detecting allosteric sites on the SARS-CoV-2 spike protein. The adopted tools consisted of the protein contact networks (PCNs), SEPAS (Affinity by Flexibility), and perturbation response scanning (PRS) based on elastic network modes. All of these methods were applied to the ACE2 complex with both the SARS-CoV2 and SARS-CoV spike proteins. All of the adopted analyses converged toward a specific region (allosteric modulation region [AMR]), present in both complexes and predicted to act as an allosteric site modulating the binding of the spike protein with ACE2. Preliminary results on hepcidin (a mol. with strong structural and sequence with AMR) indicated an inhibitory effect on the binding affinity of the spike protein toward the ACE2 protein.
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  14. 14
    Agrawal, P.; Singh, H.; Srivastava, H. K.; Singh, S.; Kishore, G.; Raghava, G. P. S. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinf 2019, 19, 426,  DOI: 10.1186/s12859-018-2449-y
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    14
    Benchmarking of different molecular docking methods for protein-peptide docking
    Agrawal Piyush; Raghava Gajendra P S; Agrawal Piyush; Singh Harinder; Srivastava Hemant Kumar; Singh Sandeep; Kishore Gaurav; Raghava Gajendra P S
    BMC bioinformatics (2019), 19 (Suppl 13), 426 ISSN:.
    BACKGROUND: Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. RESULT: Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 ÅA. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 ÅA in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 ÅA and 2.88 ÅA for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 ÅA and 2.09 ÅA for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. CONCLUSION: The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench ( http://webs.iiitd.edu.in/raghava/ppdbench/ ).
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  15. 15
    Verkhivker, G. M.; Di Paola, L. Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches. J. Phys. Chem. B 2021, 125, 850– 873,  DOI: 10.1021/acs.jpcb.0c10637
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    15
    Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches
    Verkhivker, Gennady M.; Di Paola, Luisa
    Journal of Physical Chemistry B (2021), 125 (3), 850-873CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
    The rapidly growing body of structural and biochem. studies of the SARS-CoV-2 spike glycoprotein has revealed a variety of distinct functional states with radically different arrangements of the receptor-binding domain, highlighting a remarkable function-driven conformational plasticity and adaptability of the spike proteins. In this study, we examd. mol. mechanisms underlying conformational and dynamic changes in the SARS-CoV-2 spike mutant trimers through the lens of dynamic anal. of allosteric interaction networks and atomistic modeling of signal transmission. Using an integrated approach that combined coarse-grained mol. simulations, protein stability anal., and perturbation-based modeling of residue interaction networks, we examd. how mutations in the regulatory regions of the SARS-CoV-2 spike protein can differentially affect dynamics and allosteric signaling in distinct functional states. The results of this study revealed key functional regions and regulatory centers that govern collective dynamics, allosteric interactions, and control signal transmission in the SARS-CoV-2 spike proteins. We found that the exptl. confirmed regulatory hotspots that dictate dynamic switching between conformational states of the SARS-CoV-2 spike protein correspond to the key hinge sites and global mediating centers of the allosteric interaction networks. The results of this study provide a novel insight into allosteric regulatory mechanisms of SARS-CoV-2 spike proteins showing that mutations at the key regulatory positions can differentially modulate distribution of states and det. topog. of signal communication pathways operating through state-specific cascades of control switch points. This anal. provides a plausible strategy for allosteric probing of the conformational equil. and therapeutic intervention by targeting specific hotspots of allosteric interactions and communications in the SARS-CoV-2 spike proteins.
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  16. 16
    Blacklock, K.; Verkhivker, G. M. Allosteric regulation of the Hsp90 dynamics and stability by client recruiter cochaperones: protein structure network modeling. PLoS One 2014, 9, e86547  DOI: 10.1371/journal.pone.0086547
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    16
    Allosteric regulation of the Hsp90 dynamics and stability by client recruiter cochaperones: protein structure network modeling
    Blacklock, Kristin; Verkhivker, Gennady M.
    PLoS One (2014), 9 (1), e86547/1-e86547/21, 21 pp.CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
    The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect mol. mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the expts., we have detd. that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network anal. of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equil. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network anal. has recapitulated a broad range of structural and mutagenesis expts., particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be detd. by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins.
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    Di Paola, L.; De Ruvo, M.; Paci, P.; Santoni, D.; Giuliani, A. Protein contact networks: an emerging paradigm in chemistry. Chem. Rev. 2013, 113, 1598– 1613,  DOI: 10.1021/cr3002356
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    Protein Contact Networks: An Emerging Paradigm in Chemistry
    Di Paola, L.; De Ruvo, M.; Paci, P.; Santoni, D.; Giuliani, A.
    Chemical Reviews (Washington, DC, United States) (2013), 113 (3), 1598-1613CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
    A review. Nowadays, many different fields of investigation ranging from systems biol. to elec. engineering, sociol., and statistical mechanics converge into the shared operational paradigm of complex network anal. A massive advancement in the elucidation of general behavior of network systems made possible the generation of brand new graph theor. descriptors, at both single node and entire graph level, that could be useful in many fields of chem. In this review we will deal with the protein 3D structures in terms of contact networks between amino acid residues. This case allows for a straightforward formalization in topol. terms: the role of nodes (residues) and edges (contacts) is devoid of any ambiguity and the introduction of van der Waals radii of amino acids allows us to assign a motivated threshold for assigning contacts and building the network.
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    Leitner, D. M.; Pandey, H. D.; Reid, K. M. Energy Transport across Interfaces in Biomolecular Systems. J. Phys. Chem. B 2019, 123, 9507– 9524,  DOI: 10.1021/acs.jpcb.9b07086
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    18
    Energy Transport across Interfaces in Biomolecular Systems
    Leitner, David M.; Pandey, Hari Datt; Reid, Korey M.
    Journal of Physical Chemistry B (2019), 123 (45), 9507-9524CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
    A review. Energy transport during chem. reactions or following photoexcitation in systems of biol. mols. is mediated by numerous interfaces that sep. chem. groups and mols. Describing and predicting energy transport has been complicated by the inhomogeneous environment through which it occurs, and general rules are still lacking. The authors discuss recent work on identification of networks for vibrational energy transport in biomols. and their environment, with focus on the nature of energy transfer across interfaces. Energy transport is influenced both by structure of the biomol. system as well as equil. fluctuations of nonbonded contacts between chem. groups, biomols. and water along the network. The authors also discuss recent theor. and computational work on the related topic of thermal transport through mol. interfaces, with focus on systems important in biol., as well as relevant exptl. studies.
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    Johnson, D. B. Efficient Algorithms for Shortest Paths in Sparse Networks. J. ACM 1977, 24, 1– 13,  DOI: 10.1145/321992.321993
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    Tasdighian, S.; Di Paola, L.; De Ruvo, M.; Paci, P.; Santoni, D.; Palumbo, P.; Mei, G.; Di Venere, A.; Giuliani, A. Modules identification in protein structures: the topological and geometrical solutions. J. Chem. Inf. Model. 2014, 54, 159– 68,  DOI: 10.1021/ci400218v
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    Modules Identification in Protein Structures: The Topological and Geometrical Solutions
    Tasdighian, Setareh; Di Paola, Luisa; De Ruvo, Micol; Paci, Paola; Santoni, Daniele; Palumbo, Pasquale; Mei, Giampiero; Di Venere, Almerinda; Giuliani, Alessandro
    Journal of Chemical Information and Modeling (2014), 54 (1), 159-168CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    The identification of modules in protein structures has major relevance in structural biol., with consequences in protein stability and functional classification, adding new perspectives in drug design. In this work, we present the comparison between a topol. (spectral clustering) and a geometrical (k-means) approach to module identification, in the frame of a multiscale anal. of the protein architecture principles. The global consistency of an adjacency matrix based technique (spectral clustering) and a method based on full rank geometrical information (k-means) give a proof-of-concept of the relevance of protein contact networks in structure detn. The peculiar "small-world" character of protein contact graphs is established as well, pointing to av. shortest path as a mesoscopic crucial variable to maximize the efficiency of within-mol. signal transmission. The specific nature of protein architecture indicates topol. approach as the most proper one to highlight protein functional domains, and two new representations linking sequence and topol. role of amino acids are demonstrated to be of use for protein structural anal. Here we present a case study regarding azurin, a small copper protein implied in the Pseudomonas aeruginosa respiratory chain. Its pocket mol. shape and its electron transfer function have challenged the method, highlighting its potentiality to catch jointly the structure and function features of protein structures through their decompn. into modules.
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    Cumbo, F.; Paci, P.; Santoni, D.; Di Paola, L.; Giuliani, A. GIANT: a cytoscape plugin for modular networks. PLoS One 2014, 9, e105001  DOI: 10.1371/journal.pone.0105001
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    GIANT: a Cytoscape plugin for modular networks
    Cumbo, Fabio; Paci, Paola; Santoni, Daniele; Di Paola, Luisa; Giuliani, Alessandro
    PLoS One (2014), 9 (10), e105001/1-e105001/7, 7 pp.CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
    Network anal. provides deep insight into real complex systems. Revealing the link between topol. and functional role of network elements can be crucial to understand the mechanisms underlying the system. Here we propose a Cytoscape plugin (GIANT) to perform network clustering and characterize nodes at the light of a modified Guimera-Amaral cartog. This approach results into a vivid picture of the a topol./functional relationship at both local and global level. The plugin has been already approved and uploaded on the Cytoscape APP store.
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    Guimerà, R.; Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 2005, 433, 895– 900,  DOI: 10.1038/nature03288
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    Functional cartography of complex metabolic networks
    Guimera, Roger; Amaral, Luis A. Nunes
    Nature (London, United Kingdom) (2005), 433 (7028), 895-900CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    High-throughput techniques are leading to an explosive growth in the size of biol. databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodol. that enables us to ext. and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a 'cartog. representation' of complex networks. Metabolic networks are among the most challenging biol. networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their resp. modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.
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    Cimini, S.; Di Paola, L.; Giuliani, A.; Ridolfi, A.; De Gara, L. GH32 family activity: a topological approach through protein contact networks. Plant Mol. Biol. 2016, 92, 401– 410,  DOI: 10.1007/s11103-016-0515-2
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    GH32 family activity: a topological approach through protein contact networks
    Cimini, Sara; Di Paola, Luisa; Giuliani, Alessandro; Ridolfi, Alessandra; De Gara, Laura
    Plant Molecular Biology (2016), 92 (4-5), 401-410CODEN: PMBIDB; ISSN:0167-4412. (Springer)
    Key message: The application of Protein Contact Networks methodol. allowed to highlight a novel response of border region between the two domains to substrate binding. Abstr.: Glycoside hydrolases (GH) are enzymes that mainly hydrolyze the glycosidic bond between two carbohydrates or a carbohydrate and a non-carbohydrate moiety. These enzymes are involved in many fundamental and diverse biol. processes in plants. We have focused on the GH32 family, including enzymes very similar in both sequence and structure, each having however clear specificities of substrate preferences and kinetic properties. Structural and topol. differences among proteins of the GH32 family have been here identified by means of an emerging approach (Protein Contact network, PCN) based on the formalization of 3D structures as contact networks among amino-acid residues. The PCN approach proved successful in both reconstructing the already known functional domains and in identifying the structural counterpart of the properties of GH32 enzymes, which remain uncertain, like their allosteric character. The main outcome of the study was the discovery of the activation upon binding of the border (cleft) region between the two domains. This reveals the allosteric nature of the enzymic activity for all the analyzed forms in the GH32 family, a character yet to be highlighted in biochem. studies. Furthermore, we have been able to recognize a topol. signature (graph energy) of the different affinity of the enzymes towards small and large substrates.
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    Di Paola, L.; Mei, G.; Di Venere, A.; Giuliani, A. Exploring the stability of dimers through protein structure topology. Curr. Protein Pept. Sci. 2015, 17, 30– 6,  DOI: 10.2174/1389203716666150923104054
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    Di Paola, L.; Platania, C. B. M.; Oliva, G.; Setola, R.; Pascucci, F.; Giuliani, A. Characterization of protein–protein interfaces through a protein contact network approach. Front. bioeng. biotechnol. 2015, 3, 170,  DOI: 10.3389/fbioe.2015.00170
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    Characterization of Protein-Protein Interfaces through a Protein Contact Network Approach
    Di Paola Luisa; Oliva Gabriele; Setola Roberto; Platania Chiara Bianca Maria; Pascucci Federica; Giuliani Alessandro
    Frontiers in bioengineering and biotechnology (2015), 3 (), 170 ISSN:2296-4185.
    Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein-protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node's ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein-protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.
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    Krissinel, E.; Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 2007, 372, 774– 797,  DOI: 10.1016/j.jmb.2007.05.022
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    Inference of Macromolecular Assemblies from Crystalline State
    Krissinel, Evgeny; Henrick, Kim
    Journal of Molecular Biology (2007), 372 (3), 774-797CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
    The authors discuss basic phys.-chem. principles underlying the formation of stable macromol. complexes, which in many cases are likely to be the biol. units performing a certain physiol. function. The authors also consider available theor. approaches to the calcn. of macromol. affinity and entropy of complexation. The latter is shown to play an important role and make a major effect on complex size and symmetry. The authors develop a new method, based on chem. thermodn., for automatic detection of macromol. assemblies in the Protein Data Bank (PDB) entries that are the results of x-ray diffraction expts. As found, biol. units may be recovered at 80-90% success rate, which makes x-ray crystallog. an important source of exptl. data on macromol. complexes and protein-protein interactions. The method is implemented as a public WWW service (http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html).
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    Di Paola, L.; Mei, G.; Di Venere, A.; Giuliani, A. Allostery; Springer, 2021; pp 7– 20.
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    Atilgan, A. R.; Durell, S. R.; Jernigan, R. L.; Demirel, M. C.; Keskin, O.; Bahar, I. Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 2001, 80, 505– 15,  DOI: 10.1016/S0006-3495(01)76033-X
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    Anisotropy of fluctuation dynamics of proteins with an elastic network model
    Atilgan, A. R.; Durell, S. R.; Jernigan, R. L.; Demirel, M. C.; Keskin, O.; Bahar, I.
    Biophysical Journal (2001), 80 (1), 505-515CODEN: BIOJAU; ISSN:0006-3495. (Biophysical Society)
    Fluctuations about the native conformation of proteins have proven to be suitably reproduced with a simple elastic network model, which has shown excellent agreement with a no. of different properties for a wide variety of proteins. This scalar model simply investigates the magnitudes of motion of individual residues in the structure. To use the elastic model approach further for developing the details of protein mechanisms, it becomes essential to expand this model to include the added details of the directions of individual residue fluctuations. In this paper, a new tool is presented for this purpose and applied to the retinol-binding protein, which indicates enhanced flexibility in the region of entry to the ligand binding site and for the portion of the protein binding to its carrier protein.
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    Marcos, E.; Crehuet, R.; Bahar, I. Changes in dynamics upon oligomerization regulate substrate binding and allostery in amino acid kinase family members. PLoS Comput. Biol. 2011, 7, e1002201  DOI: 10.1371/journal.pcbi.1002201
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    Changes in dynamics upon oligomerization regulate substrate binding and allostery in amino acid kinase family members
    Marcos, Enrique; Crehuet, Ramon; Bahar, Ivet
    PLoS Computational Biology (2011), 7 (9), e1002201CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
    Oligomerization is a functional requirement for many proteins. The interfacial interactions and the overall packing geometry of the individual monomers are viewed as important determinants of the thermodn. stability and allosteric regulation of oligomers. The present study focuses on the role of the interfacial interactions and overall contact topol. in the dynamic features acquired in the oligomeric state. To this aim, the collective dynamics of enzymes belonging to the amino acid kinase family both in dimeric and hexameric forms are examd. by means of an elastic network model, and the softest collective motions (i.e., lowest frequency or global modes of motions) favored by the overall architecture are analyzed. Notably, the lowest-frequency modes accessible to the individual subunits in the absence of multimerization are conserved to a large extent in the oligomer, suggesting that the oligomer takes advantage of the intrinsic dynamics of the individual monomers. At the same time, oligomerization stiffens the interfacial regions of the monomers and confers new cooperative modes that exploit the rigid-body translational and rotational degrees of freedom of the intact monomers. The present study sheds light on the mechanism of cooperative inhibition of hexameric N-acetyl--glutamate kinase by arginine and on the allosteric regulation of UMP kinases. It also highlights the significance of the particular quaternary design in selectively detg. the oligomer dynamics congruent with required ligand-binding and allosteric activities.
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    Bakan, A.; Meireles, L. M.; Bahar, I. ProDy: protein dynamics inferred from theory and experiments. Bioinformatics 2011, 27, 1575– 7,  DOI: 10.1093/bioinformatics/btr168
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    ProDy: Protein Dynamics Inferred from Theory and Experiments
    Bakan, Ahmet; Meireles, Lidio M.; Bahar, Ivet
    Bioinformatics (2011), 27 (11), 1575-1577CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Summary: We developed a Python package, ProDy, for structure-based anal. of protein dynamics. ProDy allows for quant. characterization of structural variations in heterogeneous datasets of structures exptl. resolved for a given biomol. system, and for comparison of these variations with the theor. predicted equil. dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative anal. of exptl. and theor. data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods. Availability: ProDy is open source and freely available under GNU General Public License from http://www.csb.pitt.edu/ProDy/. Contact: [email protected]; [email protected].
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    Eyal, E.; Yang, L. W.; Bahar, I. Anisotropic network model: Systematic evaluation and a new web interface. Bioinformatics 2006, 22, 2619– 2627,  DOI: 10.1093/bioinformatics/btl448
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    Anisotropic network model: systematic evaluation and a new web interface
    Eyal, Eran; Yang, Lee-Wei; Bahar, Ivet
    Bioinformatics (2006), 22 (21), 2619-2627CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    The Anisotropic Network Model (ANM) is a simple yet powerful model for normal mode anal. of proteins. Despite its broad use for exploring biomol. collective motions, ANM has not been systematically evaluated to date. A lack of a convenient interface has been an addnl. obstacle for easy usage. ANM has been evaluated on a large set of proteins to establish the optimal model parameters that achieve the highest correlation with exptl. data and its limits of accuracy and applicability. Residue fluctuations in globular proteins are shown to be more accurately predicted than those in nonglobular proteins, and core residues are more accurately described than solvent-exposed ones. Significant improvement in agreement with expts. is obsd. with increase in the resoln. of the examd. structure. A new server for ANM calcns. is presented, which offers flexible options for controlling model parameters and output formats, interactive animation of collective modes and advanced graphical features.
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    Atilgan, C.; Gerek, Z. N.; Ozkan, S. B.; Atilgan, A. R. Manipulation of conformational change in proteins by single-residue perturbations. Biophys. J. 2010, 99, 933– 43,  DOI: 10.1016/j.bpj.2010.05.020
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    Manipulation of Conformational Change in Proteins by Single-Residue Perturbations
    Atilgan, C.; Gerek, Z. N.; Ozkan, S. B.; Atilgan, A. R.
    Biophysical Journal (2010), 99 (3), 933-943CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
    Using the perturbation-response scanning (PRS) technique, we study a set of 25 proteins that display a variety of conformational motions upon ligand binding (e.g., shear, hinge, allosteric). In most cases, PRS dets. single residues that may be manipulated to achieve the resulting conformational change. PRS reveals that for some proteins, binding-induced conformational change may be achieved through the perturbation of residues scattered throughout the protein, whereas in others, perturbation of specific residues confined to a highly specific region is necessary. Overlaps between the exptl. and PRS-calcd. at. displacement vectors are usually more descriptive of the conformational change than those obtained from a modal anal. of elastic network models. Furthermore, the largest overlaps obtained by the latter approach do not always appear in the most collective modes; there are cases where more than one mode yields comparable overlap sizes. We show that success of the modal anal. depends on an absence of redundant paths in the protein. PRS thus demonstrates that several relevant modes can be induced simultaneously by perturbing a single select residue on the protein. We also illustrate the biol. relevance of applying PRS to the GroEL, adenylate kinase, myosin, and kinesin structures in detail by showing that the residues whose perturbation leads to precise conformational changes usually correspond to those exptl. detd. to be functionally important.
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    Sali, A.; Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 1993, 234, 779– 815,  DOI: 10.1006/jmbi.1993.1626
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    Comparative protein modeling by satisfaction of spatial restraints
    Sali, Andrej; Blundell, Tom L.
    Journal of Molecular Biology (1993), 234 (3), 779-815CODEN: JMOBAK; ISSN:0022-2836.
    The authors describe a comparative protein modeling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability d. functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equiv. residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain Cα-Cα distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the mol. pdf such that the model violates the input restraints as little as possible. The mol. pdf is derived as a combination of pdfs restraining individual spatial features of the whole mol. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modeling of trypsin from two other serine proteinases.
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    Hadi-Alijanvand, H. Soft regions of protein surface are potent for stable dimer formation. J. Biomol. Struct. Dyn. 2020, 38, 3587– 3598,  DOI: 10.1080/07391102.2019.1662328
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    Soft regions of protein surface are potent for stable dimer formation
    Hadi-Alijanvand, Hamid
    Journal of Biomolecular Structure and Dynamics (2020), 38 (12), 3587-3598CODEN: JBSDD6; ISSN:0739-1102. (Taylor & Francis Ltd.)
    By having knowledge about the characteristics of protein interaction interfaces, we will be able to manipulate protein complexes for therapies. Dimer state is considered as the primary alphabet of the most proteins' quaternary structure. The properties of binding interface between subunits and of noninterface region define the specificity and stability of the intended protein complex. Considering some topol. properties and amino acids' affinity for binding in interfaces of protein dimers, we construct the interface-specific recurrence plots. The data obtained from recurrence quant. anal., and accessibility-related metrics help us to classify the protein dimers into four distinct classes. Some mech. properties of binding interfaces are computed for each predefined class of the dimers. The computed mech. characteristics of binding patch region are compared with those of nonbinding region of proteins. Our observations indicate that the mech. properties of protein binding sites have a decisive impact on detg. the dimer stability. We introduce a new concept in analyzing protein structure by considering mech. properties of protein structure. We conclude that the interface region between subunits of stable dimers is usually mech. softer than the interface of unstable protein dimers.
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    Hadi-Alijanvand, H.; Rouhani, M. Partner-specific prediction of protein-dimer stability from unbound structure of monomer. J. Chem. Inf. Model. 2018, 58, 733– 745,  DOI: 10.1021/acs.jcim.7b00606
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    Partner-Specific Prediction of Protein-Dimer Stability from Unbound Structure of Monomer
    Hadi-Alijanvand, Hamid; Rouhani, Maryam
    Journal of Chemical Information and Modeling (2018), 58 (3), 733-745CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    Protein complexes play deterministic roles in live entities in sensing, compiling, controlling, and responding to external and internal stimuli. Thermodn. stability is an important property of protein complexes; having knowledge about complex stability helps us to understand the basics of protein assembly-related diseases and the mechanism of protein assembly clearly. Enormous protein-protein interactions, detected by high-throughput methods, necessitate finding fast methods for predicting the stability of protein assemblies in a quant. and qual. manner. The existing methods of predicting complex stability need knowledge about the three-dimensional (3D) structure of the intended protein complex. Here, we introduce a new method for predicting dissocn. free energy of subunits by analyzing the structural and topol. properties of a protein binding patch on a single subunit of the desired protein complex. The method needs the 3D structure of just one subunit and the information about the position of the intended binding site on the surface of that subunit to predict dimer stability in a classwise manner. The patterns of structural and topol. properties of a protein binding patch are decoded by recurrence quantification anal. Nonparametric discrimination is then utilized to predict the stability class of the intended dimer with accuracy greater than 85%.
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    Hadi-Alijanvand, H. Complex Stability Is Encoded in Binding Patch Softness: A Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers. J. Proteome Res. 2020, 19, 409– 423,  DOI: 10.1021/acs.jproteome.9b00594
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    Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers
    Hadi-Alijanvand, Hamid
    Journal of Proteome Research (2020), 19 (1), 409-423CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    Knowledge about the structure and stability of protein-protein interactions is vital to decipher the behavior of protein systems. Prediction of protein complexes' stability is an interesting topic in the field of structural biol. There are some promising published computational approaches that predict the affinity between subunits of protein dimers using 3D structures of both subunits. In the current study, we classify protein complexes with exptl. measured affinities into distinct classes with different mean affinities. By predicting the mech. stiffness of the protein binding patch (PBP) region on a single subunit, we successfully predict the assigned affinity class of the PBP in the classification step. Now to predict the exptl. measured affinity between protein monomers in soln., we just need the 3D structure of the suggested PBP on one subunit of the proposed dimer. We designed the SEPAS software and have made the software freely available for academic non-com. research purposes at "http://biophysics.ir/affinity". SEPAS predicts the stability of the intended dimer in a classwise manner by utilizing the computed mech. stiffness of the introduced binding site on one subunit with the min. accuracy of 0.72.
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    Onufriev, A.; Bashford, D.; Case, D. A. Modification of the generalized born model suitable for macromolecules. J. Phys. Chem. B 2000, 104, 3712– 3720,  DOI: 10.1021/jp994072s
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    Modification of the Generalized Born Model Suitable for Macromolecules
    Onufriev, Alexey; Bashford, Donald; Case, David A.
    Journal of Physical Chemistry B (2000), 104 (15), 3712-3720CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)
    The analytic generalized Born approxn. is an efficient electrostatic model that describes mols. in soln. Here it is modified to permit a more accurate description of large macromols., while its established performance on small compds. is nearly unaffected. The modified model is also adapted to describe mols. with an interior dielec. const. not equal to unity. The model was tested by computations of pK shifts for a no. of titratable residues in lysozyme, myoglobin, and bacteriorhodopsin. In general, except for some deeply buried residues of bacteriorhodopsin, the results show reasonable agreement with both exptl. data and calcns. based on numerical soln. of the Poisson-Boltzmann equation. A very close agreement between the two models is obtained in prediction of the pK shifts assocd. with conformational change. The calcns. based on this version of the generalized Born approxn. are much faster than finite difference solns. of the Poisson-Boltzmann equation, which makes the present method useful for a variety of other applications where computational time is a crit. factor. The model may also be integrated into mol. dynamics programs to replace explicit solvent simulations which are particularly time-consuming for large mols.
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    Acun, B.; Hardy, D. J.; Kale, L. V.; Li, K.; Phillips, J. C.; Stone, J. E. Scalable Molecular Dynamics with NAMD on the Summit System. IBM J. Res. Dev. 2018, 62, 1– 9,  DOI: 10.1147/JRD.2018.2888986
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    Scalable Molecular Dynamics with NAMD on the Summit System
    Acun B; Hardy D J; Stone J E; Kale L V; Li K; Phillips J C
    IBM journal of research and development (2018), 62 (6), 1-9 ISSN:.
    NAMD (NAnoscale Molecular Dynamics) is a parallel molecular dynamics application that has been used to make breakthroughs in understanding the structure and dynamics of large biomolecular complexes, such as viruses like HIV and various types of influenza. State-of-the-art biomolecular simulations often require integration of billions of timesteps, computing all interatomic forces for each femtosecond timestep. Molecular dynamics simulation of large biomolecular systems and long-timescale biological phenomena requires tremendous computing power. NAMD harnesses the power of thousands of heterogeneous processors to meet this demand. In this paper, we present algorithm improvements and performance optimizations that enable NAMD to achieve high performance on the IBM Newell platform (with POWER9 processors and NVIDIA Volta V100 GPUs) which underpins the Oak Ridge National Laboratory's Summit and Lawrence Livermore National Laboratory's Sierra supercomputers. The Top-500 supercomputers June 2018 list shows Summit at the number one spot with 187 Petaflop/s peak performance and Sierra third with 119 Petaflop/s. Optimizations for NAMD on Summit include: data layout changes for GPU acceleration and CPU vectorization, improving GPU offload efficiency, increasing performance with PAMI support in Charm++, improving efficiency of FFT calculations, improving load balancing, enabling better CPU vectorization and cache performance, and providing an alternative thermostat through stochastic velocity rescaling. We also present performance scaling results on early Newell systems.
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    Mackerell, A. D., Jr; Feig, M.; Brooks, C. L. 3rd Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J. Comput. Chem. 2004, 25, 1400– 15,  DOI: 10.1002/jcc.20065
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    Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations
    MacKerell, Alexander D., Jr.; Feig, Michael; Brooks, Charles L., III
    Journal of Computational Chemistry (2004), 25 (11), 1400-1415CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
    Computational studies of proteins based on empirical force fields represent a powerful tool to obtain structure-function relationships at an at. level, and are central in current efforts to solve the protein folding problem. The results from studies applying these tools are, however, dependent on the quality of the force fields used. In particular, accurate treatment of the peptide backbone is crucial to achieve representative conformational distributions in simulation studies. To improve the treatment of the peptide backbone, quantum mech. (QM) and mol. mech. (MM) calcns. were undertaken on the alanine, glycine, and proline dipeptides, and the results from these calcns. were combined with mol. dynamics (MD) simulations of proteins in crystal and aq. environments. QM potential energy maps of the alanine and glycine dipeptides at the LMP2/cc-pVxZ/MP2/6-31G* levels, where x = D, T, and Q, were detd., and are compared to available QM studies on these mols. The LMP2/cc pVQZ//MP2/6-31G* energy surfaces for all three dipeptides were then used to improve the MM treatment of the dipeptides. These improvements included addnl. parameter optimization via Monte Carlo simulated annealing and extension of the potential energy function to contain peptide backbone .vphi., ψ dihedral crossterms or a .vphi., ψ grid-based energy correction term. Simultaneously, MD simulations of up to seven proteins in their cryst. environments were used to validate the force field enhancements. Comparison with QM and crystallog. data showed that an addnl. optimization of the .vphi., ψ dihedral parameters along with the grid-based energy correction were required to yield significant improvements over the CHARMM22 force field. However, systematic deviations in the treatment of .vphi. and ψ in the helical and sheet regions were evident. Accordingly, empirical adjustments were made to the grid-based energy correction for alanine and glycine to account for these systematic differences. These adjustments lead to greater deviations from QM data for the two dipeptides but also yielded improved agreement with exptl. crystallog. data. These improvements enhance the quality of the CHARMM force field in treating proteins. This extension of the potential energy function is anticipated to facilitate improved treatment of biol. macromols. via MM approaches in general.
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    Foloppe, N.; MacKerell, A. D., Jr All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data. J. Comput. Chem. 2000, 21, 86– 104,  DOI: 10.1002/(SICI)1096-987X(20000130)21:2<86::AID-JCC2>3.0.CO;2-G
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    All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data
    Foloppe, Nicolas; Mackerell, Alexander D.
    Journal of Computational Chemistry (2000), 21 (2), 86-104CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
    Empirical force-field calcns. on biol. mols. represent an effective method to obtain at. detail information on the relationship of their structure to their function. Results from those calcns. depend on the quality of the force field. In this manuscript, optimization of the CHARMM27 all-atom empirical force field for nucleic acids is presented together with the resulting parameters. The optimization procedure is based on the reprodn. of small mol. target data from both exptl. and quantum mech. studies and condensed phase structural properties of DNA and RNA. Via an iterative approach, the parameters were primarily optimized to reproduce macromol. target data while maximizing agreement with small mol. target data. This approach is expected to ensure that the different contributions from the individual moieties in the nucleic acids are properly balanced to yield condensed phase properties of DNA and RNA, which are consistent with expt. The quality of the presented force field in reproducing both crystal and soln. properties are detailed in the present and an accompanying manuscript (MacKerell and Banavali, J Comput Chem, this issue). The resultant parameters represent the latest step in the continued development of the CHARMM all-atom biomol. force field for proteins, lipids, and nucleic acids.
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    Zhang, C.; Ma, J. Enhanced sampling and applications in protein folding in explicit solvent. J. Chem. Phys. 2010, 132, 244101,  DOI: 10.1063/1.3435332
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    Enhanced sampling and applications in protein folding in explicit solvent
    Zhang, Cheng; Ma, Jianpeng
    Journal of Chemical Physics (2010), 132 (24), 244101/1-244101/16CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
    We report a single-copy tempering method for simulating large complex systems. In a generalized ensemble, the method uses runtime est. of the thermal av. energy computed from a novel integral identity to guide a continuous temp.-space random walk. We first validated the method in a two-dimensional Ising model and a Lennard-Jones liq. system. It was then applied to folding of three small proteins, trpzip2, trp-cage, and villin headpiece in explicit solvent. Within 0.5 ∼ 1 μs, all three systems were reversibly folded into at. accuracy: the alpha carbon root mean square deviations of the best folded conformations from the native states were 0.2, 0.4, and 0.4 Å, for trpzip2, trp-cage, and villin headpiece, resp. (c) 2010 American Institute of Physics.
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    Genheden, S.; Kuhn, O.; Mikulskis, P.; Hoffmann, D.; Ryde, U. The normal-mode entropy in the MM/GBSA method: effect of system truncation, buffer region, and dielectric constant. J. Chem. Inf. Model. 2012, 52, 2079– 2088,  DOI: 10.1021/ci3001919
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    The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant
    Genheden, Samuel; Kuhn, Oliver; Mikulskis, Paulius; Hoffmann, Daniel; Ryde, Ulf
    Journal of Chemical Information and Modeling (2012), 52 (8), 2079-2088CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    We have performed a systematic study of the entropy term in the MM/GBSA (mol. mechanics combined with generalized Born and surface-area solvation) approach to calc. ligand-binding affinities. The entropies are calcd. by a normal-mode anal. of harmonic frequencies from minimized snapshots of mol. dynamics simulations. For computational reasons, these calcns. have normally been performed on truncated systems. We have studied the binding of eight inhibitors of blood clotting factor Xa, nine ligands of ferritin, and two ligands of HIV-1 protease and show that removing protein residues with distances larger than 8-16 Å to the ligand, including a 4 Å shell of fixed protein residues and water mols., change the abs. entropies by 1-5 kJ/mol on av. However, the change is systematic, so relative entropies for different ligands change by only 0.7-1.6 kJ/mol on av. Consequently, entropies from truncated systems give relative binding affinities that are identical to those obtained for the whole protein within statistical uncertainty (1-2 kJ/mol). We have also tested to use a distance-dependent dielec. const. in the minimization and frequency calcn. (ε = 4r), but it typically gives slightly different entropies and poorer binding affinities. Therefore, we recommend entropies calcd. with the smallest truncation radius (8 Å) and ε =1. Such an approach also gives an improved precision for the calcd. binding free energies.
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    Vergara-Jaque, A.; Comer, J.; Monsalve, L.; Gonzalez-Nilo, F. D.; Sandoval, C. Computationally efficient methodology for atomic-level characterization of dendrimer–drug complexes: a comparison of amine-and acetyl-terminated PAMAM. J. Phys. Chem. B 2013, 117, 6801– 6813,  DOI: 10.1021/jp4000363
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    Computationally Efficient Methodology for Atomic-Level Characterization of Dendrimer-Drug Complexes: A Comparison of Amine- and Acetyl-Terminated PAMAM
    Vergara-Jaque, Ariela; Comer, Jeffrey; Monsalve, Luis; Gonzalez-Nilo, Fernando D.; Sandoval, Claudia
    Journal of Physical Chemistry B (2013), 117 (22), 6801-6813CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
    PAMAM dendrimers have been widely studied as a novel means for controlled drug delivery; however, computational study of dendrimer-drug complexation is made difficult by the conformational flexibility of dendrimers and the nonspecific nature of the dendrimer-drug interactions. Conventional protocols for studying drug binding have been designed primarily for protein substrates, and, therefore, there is a need to establish new protocols to deal with the unique aspects of dendrimers. In this work, we generate cavities in generation-5 polyamidoamine (PAMAM) dendrimers at selected distances from the center of mass of the dendrimer for the insertion of the model drug: dexamethasone 21-phosphate or Dp21. The complexes are then allowed to equilibrate with distance between centers of mass of the drug and dendrimers confined to selected ranges; the free energy of complexation is estd. by the MM-GBSA (MM, mol. mechanics; GB, generalized Born; SA, surface area) method. For both amine- and modified acetyl-terminated PAMAM at both low and neutral pH, the most favorable free energy of complexation is assocd. with Dp21 at distance of 15-20 Å from the center of mass of the dendrimer and that smaller or larger distances yield considerably weaker affinity. In agreement with exptl. results, we find acetyl-terminated PAMAM at neutral pH to form the least stable complex with Dp21. The greatest affinity is seen in the case of acetyl-terminated PAMAM at low pH, which appears to be due a complex balance of different contributions, which cannot be attributed to electrostatics, van der Waals interactions, hydrogen bonds, or charge-charge interactions alone.
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    Torchala, M.; Moal, I. H.; Chaleil, R. A. G.; Fernandez-Recio, J.; Bates, P. A. SwarmDock: a server for flexible protein-protein docking. Bioinformatics 2013, 29, 807– 9,  DOI: 10.1093/bioinformatics/btt038
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    SwarmDock: a server for flexible protein-protein docking
    Torchala, Mieczyslaw; Moal, Iain H.; Chaleil, Raphael A. G.; Fernandez-Recio, Juan; Bates, Paul A.
    Bioinformatics (2013), 29 (6), 807-809CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Summary: Protein-protein interactions are central to almost all biol. functions, and the at. details of such interactions can yield insights into the mechanisms that underlie these functions. We present a web server that wraps and extends the SwarmDock flexible protein-protein docking algorithm. After uploading PDB files of the binding partners, the server generates low energy conformations and returns a ranked list of clustered docking poses and their corresponding structures. The user can perform full global docking, or focus on particular residues that are implicated in binding. The server is validated in the CAPRI blind docking expt., against the most current docking benchmark, and against the ClusPro docking server, the highest performing server currently available.
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    A Markov-chain model description of binding funnels to enhance the ranking of docked solutions
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    Within the crowded, seemingly chaotic environment of the cell, proteins are still able to find their binding partners. This is achieved via an ensemble of trajectories, which funnel them towards their functional binding sites, the binding funnel. Here, we characterize funnel-like energy structures on the global energy landscape using time-homogeneous finite state Markov chain models. These models are based on the idea that transitions can occur between structurally similar docking solns., with transition probabilities detd. by their difference in binding energy. Funnel-like energy structures are those contg. solns. with very high equil. populations. Although these are found surrounding both near-native and false pos. binding sites, we show that the removal of nonfunnel-like energy structures, by filtering away solns. with low max. equil. population, can significantly improve the ranking of docked poses.
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Cited By

This article is cited by 5 publications.

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  • Abstract

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    Figure 1

    Figure 1. Complex of the spike protein of SARS-CoV2 with the human receptor ACE2 (in yellow). (A) Complex spike–ACE2; (B) complex spike–ACE2 docked with the hepcidin (blue surface).

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    Figure 2

    Figure 2. Interface between two chains. In chain A, the length of the peptide segment participating in the interface accounts for seven residues (solid and empty blue bullets), and three of them are directly involved in four links (solid blue bullets). Analogously, chain B accounts for 12 residues in the interface, three of which are in direct contact with residues in chain A.

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    Figure 3

    Figure 3. General workflow of the multifaceted computational approach to the analysis of the allosteric behavior of the spike–ACE2 complex in the perspective of inhibition by hepcidin.

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    Figure 4

    Figure 4. Network clustering of closed conformation of the SARS-CoV2 spike protein: (A) the two clusters in the closed conformation are reported in green and red; (B) the active region (P > 0) in the two clusters partition.

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    Figure 5

    Figure 5. Network clustering of open (1-up) conformation of the SARS-CoV2 spike protein: (A) the two clusters in the closed conformation are reported in green and red; (B) the active region (P > 0) in the two clusters partition.

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    Figure 6

    Figure 6. Intrinsic dynamics of S proteins in closed, open, and bound states. (A) Overlap of 10 ANM modes between the closed and open states. (B) Overlap of 10 ANM modes between the open and bound states. (C) The square fluctuations of S proteins in three states based on the first ANM modes. The bounded RBD is most stable in the closed state (blue) but has the largest flexibility in the open state (green). The bounded RBD in the complex state has the lowest stability (red).

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    Figure 7

    Figure 7. Allosteric properties of S proteins in closed, open, and bound states. (a–c) Distributions of the hinge sites (green beads) based on the first three GNM modes. (d) Comparison of effectiveness for three S proteins. (e) Effectiveness profiles for three S proteins, while their predicted AMR are labeled with black stars.

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    Figure 8

    Figure 8. Spike–ACE2 complex, with chain A, B, and C shown in green, cyan and magenta, respectively, and the ACE2 ectodomain in yellow. The five residues identified as having the largest influence on energy transport in the complex are indicated in red. They all lie in the AMR, previously identified as containing the residues with the largest participation number in the complex, labeled in dark blue.

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    Figure 9

    Figure 9. Network clustering of the equilibrated form of the spike–ACE2 complex. (a) Cluster partition; (b) participation coefficient P map; (c) complex chains.

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    Figure 10

    Figure 10. Result of SWARM docking is presented. Trimeric spike protein acts as a receptor protein and hepcidin25 acts as a ligand molecule. Chain C of the spike trimer is presented as blue wire, and other chains of the spike are presented as gray dots.

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    Figure 11

    Figure 11. SEPAS-predicted affinity of trimeric spike protein for ACE2 in the presence of hepcidin25 in the AMR (ABCD-H) or its absence (ABCD).

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    Figure 12

    Figure 12. Effect of hepcidin25 on the dynamics of spike subunits. (A) Docked binding sites of hepcidin25 on chain C. (B) Measured angle is reported for Chain C, chain C with hepcidin25 in AMR (CH), chain C in association with ACE2 (CD), and hepcidin25 bonded to AMR of chain C in complex with ACE2 (CDH). (C) Results of hepcidin25 binding to chain C in association with other subunit chains and ACE2 (ABCD-HPC) or without hepcidin25 (ABCD).

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    Figure 13

    Figure 13. Results of hepcidin25 interaction with spike in different states. (A) Output of the TMD simulation for sampling the spike structure from closed to open states. The most important residues of the AMR, P > 0.5, are declared by sphere. (B) SWARM-predicted affinity of hepcidin25 for the RBM of spike chains along the transition from closed to open states. The horizontal axis represents the distance of the state to the open conformation of spike by computing the RMSD between the considered frame and the target structure in TMD. (C) Same story but for affinity between hepcidin25 and the AMR in monomeric spike. The size of the circle (B, C) correlates with the size of the SWARM suggesting the top cluster corresponds to the considered representative introduced receptor.

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    Figure 14

    Figure 14. Pair-interaction potential is computed for the AMR. The relative interaction potential is presented in the vertical axis. More negative potential means a higher amount of interactions. The horizontal axis represents the distance of the state to the open conformation of spike by computing the RMSD between the considered frame and the target structure in TMD.

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  • References

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    This article references 55 other publications.

    1. 1
      Irani, A. H.; Steyn-Ross, D. A.; Steyn-Ross, M. L.; Voss, L.; Sleigh, J. The molecular dynamics of possible inhibitors for SARS-CoV-2. J. Biomol. Struct. Dyn. 2021, 1– 10,  DOI: 10.1080/07391102.2021.1942215
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      Journal of biomolecular structure & dynamics (2021), (), 1-10 ISSN:.
      The novel coronavirus SARS-CoV-2, responsible for the present COVID-19 global pandemic, is known to bind to the angiotensin converting enzyme-2 (ACE2) receptor in human cells. A possible treatment of COVID-19 could involve blocking ACE2 and/or disabling the spike protein on the virus. Here, molecular dynamics simulations were performed to test the binding affinities of nine candidate compounds. Of these, three drugs showed significant therapeutic potential that warrant further investigation: SN35563, a ketamine ester analogue, was found to bind strongly to the ACE2 receptor but weakly within the spike receptor-binding domain (RBD); in contrast, arbidol and hydroxychloroquine bound preferentially with the spike RBD rather than ACE2. A fourth drug, remdesivir, bound approximately equally to both the ACE2 and viral spike RBD, thus potentially increasing risk of viral infection by bringing the spike protein into closer proximity to the ACE2 receptor. We suggest more experimental investigations to test that SN35563-in combination with arbidol or hydroxychloroquine-might act synergistically to block viral cell entry by providing therapeutic blockade of the host ACE2 simultaneous with reduction of viral spike receptor-binding; and that this combination therapy would allow the use of smaller doses of each drug.Communicated by Ramaswamy H. Sarma.
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      A review. Due to the rapidly spreading of novel coronavirus disease (COVID-19) worldwide, there is an urgent need to develop efficient vaccines and specific antiviral treatments. Pathways of the viral entry into cells are interesting subjects for targeted therapy of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The present study aims to provide a systematic evaluation of the most recent in vitro and in vivo investigations targeting SARS-CoV-2 cell entry. A systematic search was carried out in major medical sources, including MEDLINE (through PubMed), Web of Science, Scopus, and EMBASE. Combinations of the following search terms were used: SARS-CoV-2, in vitro, in vivo, preclin., targeted therapy, and cell entry. A modified version of the Consolidated Stds. of Reporting Trials and Systematic Review Center for Lab. Animal Experimentation assessment tools were applied for evaluating the risk of bias of in vitro and in vivo studies, resp. A narrative synthesis was performed as a qual. method for the data synthesis of each outcome measure. A total of 2,649 articles were identified through searching PubMed, Web of Science, Scopus, EMBASE, Google Scholar, and Biorxiv. Finally, 22 studies (one in vivo study and 21 in vitro studies) were included. The spike (S) glycoprotein of the SARS-CoV-2 was the main target of investigation in 19 studies. SARS-CoV-2 can enter into the host cells through endocytosis or independently. SARS-CoV-2 S protein utilizes angiotensin-converting enzyme 2 or CD147 as its cell-surface receptor to attach host cells. It consists of S1 and S2 subunits. The S1 subunit mediates viral attachment to the host cells, while the S2 subunit facilitates virus-host membrane fusion. The cleavage of the S1-S2 protein, which is required for the conformational changes of the S2 subunit and processing of viral fusion, is regulated by the host proteases, including cathepsin L (during endocytosis) and type II membrane serine protease (independently). Targeted therapy strategies against SARS-CoV-2 cell entry mechanisms fall into four main categories: strategies targeting virus receptors on the host, strategies neutralizing SARS-CoV-2 spike protein, strategies targeting virus fusion to host cells, and strategies targeting endosomal and non-endosomal dependent pathways of virus entry. Inhibition of the viral entry by targeting host or virus-related components remains the most potent strategy to prevent and treat COVID-19. Further high-quality investigations are needed to assess the efficacy of the proposed targets and develop specific antivirals against SARS-CoV-2.
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      Biswas, S.; Dey, S.; Chatterjee, S.; Nandy, A. Combatting future variants of SARS-CoV-2 using an in-silico peptide vaccine approach by targeting the spike protein. Med. Hypotheses 2022, 161, 110810,  DOI: 10.1016/j.mehy.2022.110810
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      Medical Hypotheses (2022), 161 (), 110810CODEN: MEHYDY; ISSN:0306-9877. (Elsevier Ltd.)
      The far-reaching effects of the SARS-CoV-2 pandemic have crippled the progress of the world today. With the introduction of newer and newer mutated variants of the virus, it has become necessary to have a vaccine that remains useful against all the mutated strains of SARS-CoV-2. In this regard, peptide vaccines turn out to be a cheap alternative to the traditionally designed vaccines owing to their much quicker and computationally easier, and more robust design procedures. Here, in this article, we hypothesize that there are three possible peptide vaccine regions that can be targeted to prevent the surge of SARS-CoV-2. The candidates that were selected, were surface-exposed and were not sequestered by any neighboring amino acids. They were also found to be capable of generating both B-cell and T-cell immune responses. Most importantly, none of them contains any spike protein mutation of the currently prevailing variants of SARS-CoV-2. From these findings, we have therefore concluded that these three regions can be used in wet labs for peptide vaccine design against the upcoming strains of SARS-CoV-2.
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    4. 4
      Wang, Y.; Liu, M.; Gao, J. Enhanced receptor binding of SARS-CoV-2 through networks of hydrogen-bonding and hydrophobic interactions. Proc. Natl. Acad. Sci. U.S.A. 2020, 117, 13967– 13974,  DOI: 10.1073/pnas.2008209117
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      Enhanced receptor binding of SARS-CoV-2 through networks of hydrogen-bonding and hydrophobic interactions
      Wang, Yingjie; Liu, Meiyi; Gao, Jiali
      Proceedings of the National Academy of Sciences of the United States of America (2020), 117 (25), 13967-13974CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Mol. dynamics and free energy simulations have been carried out to elucidate the structural origin of differential protein-protein interactions between the common receptor protein angiotensin converting enzyme 2 (ACE2) and the receptor binding domains of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) and the SARS coronavirus in the 2002-2003 (SARS-CoV) outbreak. Anal. of the dynamic trajectories reveals that the binding interface consists of a primarily hydrophobic region and a delicate hydrogen-bonding network in the 2019 novel coronavirus. A key mutation from a hydrophobic residue in the SARS-CoV sequence to Lys417 in SARS-CoV-2 creates a salt bridge across the central hydrophobic contact region, which along with polar residue mutations results in greater electrostatic complementarity than that of the SARS-CoV complex. Furthermore, both electrostatic effects and enhanced hydrophobic packing due to removal of four out of five proline residues in a short 12-residue loop lead to conformation shift toward a more tilted binding groove in the complex in comparison with the SARS-CoV complex. On the other hand, hydrophobic contacts in the complex of the SARS-CoV-neutralizing antibody 80R are disrupted in the SARS-CoV-2 homol. complex model, which is attributed to failure of recognition of SARS-CoV-2 by 80R.
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    5. 5
      Triveri, A.; Serapian, S. A.; Marchetti, F.; Doria, F.; Pavoni, S.; Cinquini, F.; Moroni, E.; Rasola, A.; Frigerio, F.; Colombo, G. SARS-CoV-2 spike protein mutations and escape from antibodies: a computational model of epitope loss in variants of concern. J. Chem. Inf. Model. 2021, 61, 4687– 4700,  DOI: 10.1021/acs.jcim.1c00857
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      SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: A Computational Model of Epitope Loss in Variants of Concern
      Triveri, Alice; Serapian, Stefano A.; Marchetti, Filippo; Doria, Filippo; Pavoni, Silvia; Cinquini, Fabrizio; Moroni, Elisabetta; Rasola, Andrea; Frigerio, Francesco; Colombo, Giorgio
      Journal of Chemical Information and Modeling (2021), 61 (9), 4687-4700CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      The SARS-CoV-2 spike (S) protein is exposed on the viral surface and is the 1st point of contact between the virus and the host. For these reasons it represents the prime target for COVID-19 vaccines. In recent months, variants of this protein have started to emerge. Their ability to reduce or evade recognition by S-targeting antibodies poses a threat to immunol. treatments and raises concerns for their consequences on vaccine efficacy. To develop a model able to predict the potential impact of S-protein mutations on antibody binding sites, we performed unbiased multimicrosecond mol. dynamics of several glycosylated S-protein variants and applied a straightforward structure-dynamics-energy based strategy to predict potential changes in immunogenic regions on each variant. We recover known epitopes on the ref. D614G sequence. By comparing our results, obtained on isolated S-proteins in soln., to recently published data on antibody binding and reactivity in new S variants, we directly show that modifications in the S-protein consistently translate into the loss of potentially immunoreactive regions. Our findings can thus be qual. reconnected to the exptl. characterized decreased ability of some of the Abs elicited against the dominant S-sequence to recognize variants. While based on the study of SARS-CoV-2 spike variants, our computational epitope-prediction strategy is portable and could be applied to study immunoreactivity in mutants of proteins of interest whose structures have been characterized, helping the development/selection of vaccines, and antibodies able to control emerging variants.
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    6. 6
      Nai, A.; Lorè, N. I.; Pagani, A.; De Lorenzo, R.; Di Modica, S.; Saliu, F.; Cirillo, D. M.; Rovere-Querini, P.; Manfredi, A. A.; Silvestri, L. Hepcidin levels predict Covid-19 severity and mortality in a cohort of hospitalized Italian patients. Am. J. Hematol. 2021, 96, E32– E35,  DOI: 10.1002/ajh.26027
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      Hepcidin levels predict Covid-19 severity and mortality in a cohort of hospitalized Italian patients
      Nai, Antonella; Lore, Nicola Ivan; Pagani, Alessia; De Lorenzo, Rebecca; Di Modica, Simona; Saliu, Fabio; Cirillo, Daniela Maria; Rovere-Querini, Patrizia; Manfredi, Angelo A.; Silvestri, Laura
      American Journal of Hematology (2021), 96 (1), E32-E35CODEN: AJHEDD; ISSN:0361-8609. (Wiley-Liss, Inc.)
      Overall our data suggest that inCovid-19 hepcidin can be considered a marker of morbidity and outcome, of special value for severely compromised patients in ICU. Further studies are necessary to verify whether targeting the hepcidin axis may influence the disease outcome.
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      Ganz, T. Hepcidin─a peptide hormone at the interface of innate immunity and iron metabolism. Curr. Top. Microbiol. Immunol. 2006, 306, 183– 198,  DOI: 10.1007/3-540-29916-5_7
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      Hepcidin - a peptide hormone at the interface of innate immunity and iron metabolism
      Ganz, T.
      Current Topics in Microbiology and Immunology (2006), 306 (Antimicrobial Peptides and Human Disease), 183-198CODEN: CTMIA3; ISSN:0070-217X. (Springer GmbH)
      A review. Hepcidin is a cationic amphipathic peptide made in the liver, released into plasma and excreted in urine. Hepcidin is the homeostatic regulator of intestinal iron absorption, iron recycling by macrophages, and iron mobilization from hepatic stores, but it is also markedly induced during infections and inflammation. Under the influence of hepcidin, macrophages, hepatocytes, and enterocytes retain iron that would otherwise be released into plasma. Hepcidin acts by inhibiting the efflux of iron through ferroportin, the sole known iron exporter that is expressed in the small intestine, and in hepatocytes and macrophages. As befits an iron-regulatory hormone, hepcidin synthesis is increased by iron loading, and decreased by anemia and hypoxia. Hepcidin is also rapidly induced by cytokines, including IL-6. The resulting decrease in plasma iron levels eventually limits iron availability to erythropoiesis and contributes to the anemia assocd. with infection and inflammation. The decrease in extracellular iron concns. due to hepcidin probably limits iron availability to invading microorganisms, thus contributing to host defense.
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    8. 8
      Yagci, S.; Serin, E.; Acicbe, O.; Zeren, M. I.; Odabasi, M. S. The relationship between serum erythropoietin, hepcidin, and haptoglobin levels with disease severity and other biochemical values in patients with COVID-19. Int. J. Lab. Hematol. 2021, 43, 142– 151,  DOI: 10.1111/ijlh.13479
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      The relationship between serum erythropoietin, hepcidin, and haptoglobin levels with disease severity and other biochemical values in patients with COVID-19
      Yagci Sema; Serin Erdinc; Odabasi Merve Sena; Acicbe Ozlem; Zeren Mustafa Ismet
      International journal of laboratory hematology (2021), 43 Suppl 1 (), 142-151 ISSN:.
      INTRODUCTION: Studies have shown that iron metabolism is affected by coronavirus disease 19 (COVID-19), which has spread worldwide and has become a global health problem. Our study aimed to evaluate the relationship between COVID-19 and serum erythropoietin (EPO), hepcidin, and haptoglobin (Hpt) levels with disease severity, and other biochemical values. METHODS: Fifty nine COVID-19 patients hospitalized in the intensive care unit (ICU) and wards in our hospital between March and June 2020 and 19 healthy volunteers were included in the study. Participants were divided into mild, severe, and critical disease severity groups. Group mean values were analyzed with SPSS according to disease severity, mortality, and intubation status. RESULTS: Hemoglobin (Hb) levels were significantly lower in the critical patient group (P < .0001) and deceased group (P < .0001). The red blood cell distribution width-coefficient of variation (RDW-CV) and ferritin values were significantly higher in the intubated (P = .001, P = .005) and deceased (P = .014, P = .003) groups. Ferritin values were positively correlated with disease severity (P < .0001). Serum iron levels were lower in the patient group compared with the reference range. (P < .0001). It was found that the transferrin saturation (TfSat) was lower in the patient group compared with the control group (P < .0001). It was found that the mean EPO of the deceased was lower than the control group and the survived patient group (P = .035). Hepcidin levels were found to be significantly lower in the patient group (P < .0001). Hpt values were found to be significantly lower in the intubated group (P = .004) and the deceased group (P = .042). CONCLUSION: In our study, while serum iron and hepcidin levels decreased in patients diagnosed with COVID-19, we found that EPO and Hpt levels were significantly lower in critical and deceased patient groups. Our study is the first study examining EPO and Hpt levels in patients diagnosed with COVID-19.
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      Park, C. H.; Valore, E. V.; Waring, A. J.; Ganz, T. Hepcidin, a Urinary Antimicrobial Peptide Synthesized in the Liver. J. Biol. Chem. 2001, 276,  DOI: 10.1074/jbc.M008922200
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      Ehsani, S. COVID-19 and iron dysregulation: distant sequence similarity between hepcidin and the novel coronavirus spike glycoprotein. Biol. Direct 2020, 15, 1– 13,  DOI: 10.1186/s13062-020-00275-2
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      Ni, D.; Lu, S.; Zhang, J. Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery. Med. Res. Rev. 2019, 39, 2314– 2342,  DOI: 10.1002/med.21585
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      Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery
      Ni Duan; Lu Shaoyong; Zhang Jian; Lu Shaoyong; Zhang Jian; Zhang Jian
      Medicinal research reviews (2019), 39 (6), 2314-2342 ISSN:.
      Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.
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      Genome-wide Structure and Function Modeling of SARS-CoV-2. https://zhanglab.ccmb.med.umich.edu/COVID-19/, 2020.
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      Di Paola, L.; Hadi-Alijanvand, H.; Song, X.; Hu, G.; Giuliani, A. The Discovery of a Putative Allosteric Site in the SARS-CoV-2 Spike Protein Using an Integrated Structural/Dynamic Approach. J. Proteome Res. 2020, 19, 4576– 4586,  DOI: 10.1021/acs.jproteome.0c00273
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      13
      The Discovery of a Putative Allosteric Site in the SARS-CoV-2 Spike Protein Using an Integrated Structural/Dynamic Approach
      Di Paola, Luisa; Hadi-Alijanvand, Hamid; Song, Xingyu; Hu, Guang; Giuliani, Alessandro
      Journal of Proteome Research (2020), 19 (11), 4576-4586CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      SARS-CoV-2 has caused the largest pandemic of the twenty-first century (COVID-19), threatening the life and economy of all countries in the world. The identification of novel therapies and vaccines that can mitigate or control this global health threat is among the most important challenges facing biomedical sciences. To construct a long-term strategy to fight both SARS-CoV-2 and other possible future threats from coronaviruses, it is crit. to understand the mol. mechanisms underlying the virus action. The viral entry and assocd. infectivity stems from the formation of the SARS-CoV-2 spike protein complex with angiotensin-converting enzyme 2 (ACE2). The detection of putative allosteric sites on the viral spike protein mol. can be used to elucidate the mol. pathways that can be targeted with allosteric drugs to weaken the spike-ACE2 interaction and, thus, reduce viral infectivity. In this study, we present the results of the application of different computational methods aimed at detecting allosteric sites on the SARS-CoV-2 spike protein. The adopted tools consisted of the protein contact networks (PCNs), SEPAS (Affinity by Flexibility), and perturbation response scanning (PRS) based on elastic network modes. All of these methods were applied to the ACE2 complex with both the SARS-CoV2 and SARS-CoV spike proteins. All of the adopted analyses converged toward a specific region (allosteric modulation region [AMR]), present in both complexes and predicted to act as an allosteric site modulating the binding of the spike protein with ACE2. Preliminary results on hepcidin (a mol. with strong structural and sequence with AMR) indicated an inhibitory effect on the binding affinity of the spike protein toward the ACE2 protein.
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    14. 14
      Agrawal, P.; Singh, H.; Srivastava, H. K.; Singh, S.; Kishore, G.; Raghava, G. P. S. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinf 2019, 19, 426,  DOI: 10.1186/s12859-018-2449-y
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      14
      Benchmarking of different molecular docking methods for protein-peptide docking
      Agrawal Piyush; Raghava Gajendra P S; Agrawal Piyush; Singh Harinder; Srivastava Hemant Kumar; Singh Sandeep; Kishore Gaurav; Raghava Gajendra P S
      BMC bioinformatics (2019), 19 (Suppl 13), 426 ISSN:.
      BACKGROUND: Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. RESULT: Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 ÅA. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 ÅA in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 ÅA and 2.88 ÅA for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 ÅA and 2.09 ÅA for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. CONCLUSION: The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench ( http://webs.iiitd.edu.in/raghava/ppdbench/ ).
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    15. 15
      Verkhivker, G. M.; Di Paola, L. Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches. J. Phys. Chem. B 2021, 125, 850– 873,  DOI: 10.1021/acs.jpcb.0c10637
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      15
      Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches
      Verkhivker, Gennady M.; Di Paola, Luisa
      Journal of Physical Chemistry B (2021), 125 (3), 850-873CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
      The rapidly growing body of structural and biochem. studies of the SARS-CoV-2 spike glycoprotein has revealed a variety of distinct functional states with radically different arrangements of the receptor-binding domain, highlighting a remarkable function-driven conformational plasticity and adaptability of the spike proteins. In this study, we examd. mol. mechanisms underlying conformational and dynamic changes in the SARS-CoV-2 spike mutant trimers through the lens of dynamic anal. of allosteric interaction networks and atomistic modeling of signal transmission. Using an integrated approach that combined coarse-grained mol. simulations, protein stability anal., and perturbation-based modeling of residue interaction networks, we examd. how mutations in the regulatory regions of the SARS-CoV-2 spike protein can differentially affect dynamics and allosteric signaling in distinct functional states. The results of this study revealed key functional regions and regulatory centers that govern collective dynamics, allosteric interactions, and control signal transmission in the SARS-CoV-2 spike proteins. We found that the exptl. confirmed regulatory hotspots that dictate dynamic switching between conformational states of the SARS-CoV-2 spike protein correspond to the key hinge sites and global mediating centers of the allosteric interaction networks. The results of this study provide a novel insight into allosteric regulatory mechanisms of SARS-CoV-2 spike proteins showing that mutations at the key regulatory positions can differentially modulate distribution of states and det. topog. of signal communication pathways operating through state-specific cascades of control switch points. This anal. provides a plausible strategy for allosteric probing of the conformational equil. and therapeutic intervention by targeting specific hotspots of allosteric interactions and communications in the SARS-CoV-2 spike proteins.
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      Blacklock, K.; Verkhivker, G. M. Allosteric regulation of the Hsp90 dynamics and stability by client recruiter cochaperones: protein structure network modeling. PLoS One 2014, 9, e86547  DOI: 10.1371/journal.pone.0086547
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      Allosteric regulation of the Hsp90 dynamics and stability by client recruiter cochaperones: protein structure network modeling
      Blacklock, Kristin; Verkhivker, Gennady M.
      PLoS One (2014), 9 (1), e86547/1-e86547/21, 21 pp.CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect mol. mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the expts., we have detd. that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network anal. of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equil. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network anal. has recapitulated a broad range of structural and mutagenesis expts., particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be detd. by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins.
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      Di Paola, L.; De Ruvo, M.; Paci, P.; Santoni, D.; Giuliani, A. Protein contact networks: an emerging paradigm in chemistry. Chem. Rev. 2013, 113, 1598– 1613,  DOI: 10.1021/cr3002356
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      Protein Contact Networks: An Emerging Paradigm in Chemistry
      Di Paola, L.; De Ruvo, M.; Paci, P.; Santoni, D.; Giuliani, A.
      Chemical Reviews (Washington, DC, United States) (2013), 113 (3), 1598-1613CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
      A review. Nowadays, many different fields of investigation ranging from systems biol. to elec. engineering, sociol., and statistical mechanics converge into the shared operational paradigm of complex network anal. A massive advancement in the elucidation of general behavior of network systems made possible the generation of brand new graph theor. descriptors, at both single node and entire graph level, that could be useful in many fields of chem. In this review we will deal with the protein 3D structures in terms of contact networks between amino acid residues. This case allows for a straightforward formalization in topol. terms: the role of nodes (residues) and edges (contacts) is devoid of any ambiguity and the introduction of van der Waals radii of amino acids allows us to assign a motivated threshold for assigning contacts and building the network.
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      Leitner, D. M.; Pandey, H. D.; Reid, K. M. Energy Transport across Interfaces in Biomolecular Systems. J. Phys. Chem. B 2019, 123, 9507– 9524,  DOI: 10.1021/acs.jpcb.9b07086
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      18
      Energy Transport across Interfaces in Biomolecular Systems
      Leitner, David M.; Pandey, Hari Datt; Reid, Korey M.
      Journal of Physical Chemistry B (2019), 123 (45), 9507-9524CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
      A review. Energy transport during chem. reactions or following photoexcitation in systems of biol. mols. is mediated by numerous interfaces that sep. chem. groups and mols. Describing and predicting energy transport has been complicated by the inhomogeneous environment through which it occurs, and general rules are still lacking. The authors discuss recent work on identification of networks for vibrational energy transport in biomols. and their environment, with focus on the nature of energy transfer across interfaces. Energy transport is influenced both by structure of the biomol. system as well as equil. fluctuations of nonbonded contacts between chem. groups, biomols. and water along the network. The authors also discuss recent theor. and computational work on the related topic of thermal transport through mol. interfaces, with focus on systems important in biol., as well as relevant exptl. studies.
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      Johnson, D. B. Efficient Algorithms for Shortest Paths in Sparse Networks. J. ACM 1977, 24, 1– 13,  DOI: 10.1145/321992.321993
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      Tasdighian, S.; Di Paola, L.; De Ruvo, M.; Paci, P.; Santoni, D.; Palumbo, P.; Mei, G.; Di Venere, A.; Giuliani, A. Modules identification in protein structures: the topological and geometrical solutions. J. Chem. Inf. Model. 2014, 54, 159– 68,  DOI: 10.1021/ci400218v
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      Modules Identification in Protein Structures: The Topological and Geometrical Solutions
      Tasdighian, Setareh; Di Paola, Luisa; De Ruvo, Micol; Paci, Paola; Santoni, Daniele; Palumbo, Pasquale; Mei, Giampiero; Di Venere, Almerinda; Giuliani, Alessandro
      Journal of Chemical Information and Modeling (2014), 54 (1), 159-168CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      The identification of modules in protein structures has major relevance in structural biol., with consequences in protein stability and functional classification, adding new perspectives in drug design. In this work, we present the comparison between a topol. (spectral clustering) and a geometrical (k-means) approach to module identification, in the frame of a multiscale anal. of the protein architecture principles. The global consistency of an adjacency matrix based technique (spectral clustering) and a method based on full rank geometrical information (k-means) give a proof-of-concept of the relevance of protein contact networks in structure detn. The peculiar "small-world" character of protein contact graphs is established as well, pointing to av. shortest path as a mesoscopic crucial variable to maximize the efficiency of within-mol. signal transmission. The specific nature of protein architecture indicates topol. approach as the most proper one to highlight protein functional domains, and two new representations linking sequence and topol. role of amino acids are demonstrated to be of use for protein structural anal. Here we present a case study regarding azurin, a small copper protein implied in the Pseudomonas aeruginosa respiratory chain. Its pocket mol. shape and its electron transfer function have challenged the method, highlighting its potentiality to catch jointly the structure and function features of protein structures through their decompn. into modules.
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    21. 21
      Cumbo, F.; Paci, P.; Santoni, D.; Di Paola, L.; Giuliani, A. GIANT: a cytoscape plugin for modular networks. PLoS One 2014, 9, e105001  DOI: 10.1371/journal.pone.0105001
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      GIANT: a Cytoscape plugin for modular networks
      Cumbo, Fabio; Paci, Paola; Santoni, Daniele; Di Paola, Luisa; Giuliani, Alessandro
      PLoS One (2014), 9 (10), e105001/1-e105001/7, 7 pp.CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      Network anal. provides deep insight into real complex systems. Revealing the link between topol. and functional role of network elements can be crucial to understand the mechanisms underlying the system. Here we propose a Cytoscape plugin (GIANT) to perform network clustering and characterize nodes at the light of a modified Guimera-Amaral cartog. This approach results into a vivid picture of the a topol./functional relationship at both local and global level. The plugin has been already approved and uploaded on the Cytoscape APP store.
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    22. 22
      Guimerà, R.; Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 2005, 433, 895– 900,  DOI: 10.1038/nature03288
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      Functional cartography of complex metabolic networks
      Guimera, Roger; Amaral, Luis A. Nunes
      Nature (London, United Kingdom) (2005), 433 (7028), 895-900CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      High-throughput techniques are leading to an explosive growth in the size of biol. databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodol. that enables us to ext. and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a 'cartog. representation' of complex networks. Metabolic networks are among the most challenging biol. networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their resp. modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.
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      Cimini, S.; Di Paola, L.; Giuliani, A.; Ridolfi, A.; De Gara, L. GH32 family activity: a topological approach through protein contact networks. Plant Mol. Biol. 2016, 92, 401– 410,  DOI: 10.1007/s11103-016-0515-2
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      GH32 family activity: a topological approach through protein contact networks
      Cimini, Sara; Di Paola, Luisa; Giuliani, Alessandro; Ridolfi, Alessandra; De Gara, Laura
      Plant Molecular Biology (2016), 92 (4-5), 401-410CODEN: PMBIDB; ISSN:0167-4412. (Springer)
      Key message: The application of Protein Contact Networks methodol. allowed to highlight a novel response of border region between the two domains to substrate binding. Abstr.: Glycoside hydrolases (GH) are enzymes that mainly hydrolyze the glycosidic bond between two carbohydrates or a carbohydrate and a non-carbohydrate moiety. These enzymes are involved in many fundamental and diverse biol. processes in plants. We have focused on the GH32 family, including enzymes very similar in both sequence and structure, each having however clear specificities of substrate preferences and kinetic properties. Structural and topol. differences among proteins of the GH32 family have been here identified by means of an emerging approach (Protein Contact network, PCN) based on the formalization of 3D structures as contact networks among amino-acid residues. The PCN approach proved successful in both reconstructing the already known functional domains and in identifying the structural counterpart of the properties of GH32 enzymes, which remain uncertain, like their allosteric character. The main outcome of the study was the discovery of the activation upon binding of the border (cleft) region between the two domains. This reveals the allosteric nature of the enzymic activity for all the analyzed forms in the GH32 family, a character yet to be highlighted in biochem. studies. Furthermore, we have been able to recognize a topol. signature (graph energy) of the different affinity of the enzymes towards small and large substrates.
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      Di Paola, L.; Mei, G.; Di Venere, A.; Giuliani, A. Exploring the stability of dimers through protein structure topology. Curr. Protein Pept. Sci. 2015, 17, 30– 6,  DOI: 10.2174/1389203716666150923104054
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      Di Paola, L.; Platania, C. B. M.; Oliva, G.; Setola, R.; Pascucci, F.; Giuliani, A. Characterization of protein–protein interfaces through a protein contact network approach. Front. bioeng. biotechnol. 2015, 3, 170,  DOI: 10.3389/fbioe.2015.00170
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      Characterization of Protein-Protein Interfaces through a Protein Contact Network Approach
      Di Paola Luisa; Oliva Gabriele; Setola Roberto; Platania Chiara Bianca Maria; Pascucci Federica; Giuliani Alessandro
      Frontiers in bioengineering and biotechnology (2015), 3 (), 170 ISSN:2296-4185.
      Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein-protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node's ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein-protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.
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      Krissinel, E.; Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 2007, 372, 774– 797,  DOI: 10.1016/j.jmb.2007.05.022
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      Inference of Macromolecular Assemblies from Crystalline State
      Krissinel, Evgeny; Henrick, Kim
      Journal of Molecular Biology (2007), 372 (3), 774-797CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
      The authors discuss basic phys.-chem. principles underlying the formation of stable macromol. complexes, which in many cases are likely to be the biol. units performing a certain physiol. function. The authors also consider available theor. approaches to the calcn. of macromol. affinity and entropy of complexation. The latter is shown to play an important role and make a major effect on complex size and symmetry. The authors develop a new method, based on chem. thermodn., for automatic detection of macromol. assemblies in the Protein Data Bank (PDB) entries that are the results of x-ray diffraction expts. As found, biol. units may be recovered at 80-90% success rate, which makes x-ray crystallog. an important source of exptl. data on macromol. complexes and protein-protein interactions. The method is implemented as a public WWW service (http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html).
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      Di Paola, L.; Mei, G.; Di Venere, A.; Giuliani, A. Allostery; Springer, 2021; pp 7– 20.
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      Atilgan, A. R.; Durell, S. R.; Jernigan, R. L.; Demirel, M. C.; Keskin, O.; Bahar, I. Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 2001, 80, 505– 15,  DOI: 10.1016/S0006-3495(01)76033-X
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      Anisotropy of fluctuation dynamics of proteins with an elastic network model
      Atilgan, A. R.; Durell, S. R.; Jernigan, R. L.; Demirel, M. C.; Keskin, O.; Bahar, I.
      Biophysical Journal (2001), 80 (1), 505-515CODEN: BIOJAU; ISSN:0006-3495. (Biophysical Society)
      Fluctuations about the native conformation of proteins have proven to be suitably reproduced with a simple elastic network model, which has shown excellent agreement with a no. of different properties for a wide variety of proteins. This scalar model simply investigates the magnitudes of motion of individual residues in the structure. To use the elastic model approach further for developing the details of protein mechanisms, it becomes essential to expand this model to include the added details of the directions of individual residue fluctuations. In this paper, a new tool is presented for this purpose and applied to the retinol-binding protein, which indicates enhanced flexibility in the region of entry to the ligand binding site and for the portion of the protein binding to its carrier protein.
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      Marcos, E.; Crehuet, R.; Bahar, I. Changes in dynamics upon oligomerization regulate substrate binding and allostery in amino acid kinase family members. PLoS Comput. Biol. 2011, 7, e1002201  DOI: 10.1371/journal.pcbi.1002201
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      Changes in dynamics upon oligomerization regulate substrate binding and allostery in amino acid kinase family members
      Marcos, Enrique; Crehuet, Ramon; Bahar, Ivet
      PLoS Computational Biology (2011), 7 (9), e1002201CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
      Oligomerization is a functional requirement for many proteins. The interfacial interactions and the overall packing geometry of the individual monomers are viewed as important determinants of the thermodn. stability and allosteric regulation of oligomers. The present study focuses on the role of the interfacial interactions and overall contact topol. in the dynamic features acquired in the oligomeric state. To this aim, the collective dynamics of enzymes belonging to the amino acid kinase family both in dimeric and hexameric forms are examd. by means of an elastic network model, and the softest collective motions (i.e., lowest frequency or global modes of motions) favored by the overall architecture are analyzed. Notably, the lowest-frequency modes accessible to the individual subunits in the absence of multimerization are conserved to a large extent in the oligomer, suggesting that the oligomer takes advantage of the intrinsic dynamics of the individual monomers. At the same time, oligomerization stiffens the interfacial regions of the monomers and confers new cooperative modes that exploit the rigid-body translational and rotational degrees of freedom of the intact monomers. The present study sheds light on the mechanism of cooperative inhibition of hexameric N-acetyl--glutamate kinase by arginine and on the allosteric regulation of UMP kinases. It also highlights the significance of the particular quaternary design in selectively detg. the oligomer dynamics congruent with required ligand-binding and allosteric activities.
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    30. 30
      Bakan, A.; Meireles, L. M.; Bahar, I. ProDy: protein dynamics inferred from theory and experiments. Bioinformatics 2011, 27, 1575– 7,  DOI: 10.1093/bioinformatics/btr168
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      ProDy: Protein Dynamics Inferred from Theory and Experiments
      Bakan, Ahmet; Meireles, Lidio M.; Bahar, Ivet
      Bioinformatics (2011), 27 (11), 1575-1577CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Summary: We developed a Python package, ProDy, for structure-based anal. of protein dynamics. ProDy allows for quant. characterization of structural variations in heterogeneous datasets of structures exptl. resolved for a given biomol. system, and for comparison of these variations with the theor. predicted equil. dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative anal. of exptl. and theor. data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods. Availability: ProDy is open source and freely available under GNU General Public License from http://www.csb.pitt.edu/ProDy/. Contact: [email protected]; [email protected].
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      Eyal, E.; Yang, L. W.; Bahar, I. Anisotropic network model: Systematic evaluation and a new web interface. Bioinformatics 2006, 22, 2619– 2627,  DOI: 10.1093/bioinformatics/btl448
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      Anisotropic network model: systematic evaluation and a new web interface
      Eyal, Eran; Yang, Lee-Wei; Bahar, Ivet
      Bioinformatics (2006), 22 (21), 2619-2627CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      The Anisotropic Network Model (ANM) is a simple yet powerful model for normal mode anal. of proteins. Despite its broad use for exploring biomol. collective motions, ANM has not been systematically evaluated to date. A lack of a convenient interface has been an addnl. obstacle for easy usage. ANM has been evaluated on a large set of proteins to establish the optimal model parameters that achieve the highest correlation with exptl. data and its limits of accuracy and applicability. Residue fluctuations in globular proteins are shown to be more accurately predicted than those in nonglobular proteins, and core residues are more accurately described than solvent-exposed ones. Significant improvement in agreement with expts. is obsd. with increase in the resoln. of the examd. structure. A new server for ANM calcns. is presented, which offers flexible options for controlling model parameters and output formats, interactive animation of collective modes and advanced graphical features.
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      Atilgan, C.; Gerek, Z. N.; Ozkan, S. B.; Atilgan, A. R. Manipulation of conformational change in proteins by single-residue perturbations. Biophys. J. 2010, 99, 933– 43,  DOI: 10.1016/j.bpj.2010.05.020
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      Manipulation of Conformational Change in Proteins by Single-Residue Perturbations
      Atilgan, C.; Gerek, Z. N.; Ozkan, S. B.; Atilgan, A. R.
      Biophysical Journal (2010), 99 (3), 933-943CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
      Using the perturbation-response scanning (PRS) technique, we study a set of 25 proteins that display a variety of conformational motions upon ligand binding (e.g., shear, hinge, allosteric). In most cases, PRS dets. single residues that may be manipulated to achieve the resulting conformational change. PRS reveals that for some proteins, binding-induced conformational change may be achieved through the perturbation of residues scattered throughout the protein, whereas in others, perturbation of specific residues confined to a highly specific region is necessary. Overlaps between the exptl. and PRS-calcd. at. displacement vectors are usually more descriptive of the conformational change than those obtained from a modal anal. of elastic network models. Furthermore, the largest overlaps obtained by the latter approach do not always appear in the most collective modes; there are cases where more than one mode yields comparable overlap sizes. We show that success of the modal anal. depends on an absence of redundant paths in the protein. PRS thus demonstrates that several relevant modes can be induced simultaneously by perturbing a single select residue on the protein. We also illustrate the biol. relevance of applying PRS to the GroEL, adenylate kinase, myosin, and kinesin structures in detail by showing that the residues whose perturbation leads to precise conformational changes usually correspond to those exptl. detd. to be functionally important.
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    33. 33
      Sali, A.; Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 1993, 234, 779– 815,  DOI: 10.1006/jmbi.1993.1626
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      Comparative protein modeling by satisfaction of spatial restraints
      Sali, Andrej; Blundell, Tom L.
      Journal of Molecular Biology (1993), 234 (3), 779-815CODEN: JMOBAK; ISSN:0022-2836.
      The authors describe a comparative protein modeling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability d. functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equiv. residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain Cα-Cα distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the mol. pdf such that the model violates the input restraints as little as possible. The mol. pdf is derived as a combination of pdfs restraining individual spatial features of the whole mol. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modeling of trypsin from two other serine proteinases.
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      Hadi-Alijanvand, H. Soft regions of protein surface are potent for stable dimer formation. J. Biomol. Struct. Dyn. 2020, 38, 3587– 3598,  DOI: 10.1080/07391102.2019.1662328
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      Soft regions of protein surface are potent for stable dimer formation
      Hadi-Alijanvand, Hamid
      Journal of Biomolecular Structure and Dynamics (2020), 38 (12), 3587-3598CODEN: JBSDD6; ISSN:0739-1102. (Taylor & Francis Ltd.)
      By having knowledge about the characteristics of protein interaction interfaces, we will be able to manipulate protein complexes for therapies. Dimer state is considered as the primary alphabet of the most proteins' quaternary structure. The properties of binding interface between subunits and of noninterface region define the specificity and stability of the intended protein complex. Considering some topol. properties and amino acids' affinity for binding in interfaces of protein dimers, we construct the interface-specific recurrence plots. The data obtained from recurrence quant. anal., and accessibility-related metrics help us to classify the protein dimers into four distinct classes. Some mech. properties of binding interfaces are computed for each predefined class of the dimers. The computed mech. characteristics of binding patch region are compared with those of nonbinding region of proteins. Our observations indicate that the mech. properties of protein binding sites have a decisive impact on detg. the dimer stability. We introduce a new concept in analyzing protein structure by considering mech. properties of protein structure. We conclude that the interface region between subunits of stable dimers is usually mech. softer than the interface of unstable protein dimers.
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    35. 35
      Hadi-Alijanvand, H.; Rouhani, M. Partner-specific prediction of protein-dimer stability from unbound structure of monomer. J. Chem. Inf. Model. 2018, 58, 733– 745,  DOI: 10.1021/acs.jcim.7b00606
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      Partner-Specific Prediction of Protein-Dimer Stability from Unbound Structure of Monomer
      Hadi-Alijanvand, Hamid; Rouhani, Maryam
      Journal of Chemical Information and Modeling (2018), 58 (3), 733-745CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      Protein complexes play deterministic roles in live entities in sensing, compiling, controlling, and responding to external and internal stimuli. Thermodn. stability is an important property of protein complexes; having knowledge about complex stability helps us to understand the basics of protein assembly-related diseases and the mechanism of protein assembly clearly. Enormous protein-protein interactions, detected by high-throughput methods, necessitate finding fast methods for predicting the stability of protein assemblies in a quant. and qual. manner. The existing methods of predicting complex stability need knowledge about the three-dimensional (3D) structure of the intended protein complex. Here, we introduce a new method for predicting dissocn. free energy of subunits by analyzing the structural and topol. properties of a protein binding patch on a single subunit of the desired protein complex. The method needs the 3D structure of just one subunit and the information about the position of the intended binding site on the surface of that subunit to predict dimer stability in a classwise manner. The patterns of structural and topol. properties of a protein binding patch are decoded by recurrence quantification anal. Nonparametric discrimination is then utilized to predict the stability class of the intended dimer with accuracy greater than 85%.
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      Hadi-Alijanvand, H. Complex Stability Is Encoded in Binding Patch Softness: A Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers. J. Proteome Res. 2020, 19, 409– 423,  DOI: 10.1021/acs.jproteome.9b00594
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      36
      Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers
      Hadi-Alijanvand, Hamid
      Journal of Proteome Research (2020), 19 (1), 409-423CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      Knowledge about the structure and stability of protein-protein interactions is vital to decipher the behavior of protein systems. Prediction of protein complexes' stability is an interesting topic in the field of structural biol. There are some promising published computational approaches that predict the affinity between subunits of protein dimers using 3D structures of both subunits. In the current study, we classify protein complexes with exptl. measured affinities into distinct classes with different mean affinities. By predicting the mech. stiffness of the protein binding patch (PBP) region on a single subunit, we successfully predict the assigned affinity class of the PBP in the classification step. Now to predict the exptl. measured affinity between protein monomers in soln., we just need the 3D structure of the suggested PBP on one subunit of the proposed dimer. We designed the SEPAS software and have made the software freely available for academic non-com. research purposes at "http://biophysics.ir/affinity". SEPAS predicts the stability of the intended dimer in a classwise manner by utilizing the computed mech. stiffness of the introduced binding site on one subunit with the min. accuracy of 0.72.
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      Onufriev, A.; Bashford, D.; Case, D. A. Modification of the generalized born model suitable for macromolecules. J. Phys. Chem. B 2000, 104, 3712– 3720,  DOI: 10.1021/jp994072s
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      Modification of the Generalized Born Model Suitable for Macromolecules
      Onufriev, Alexey; Bashford, Donald; Case, David A.
      Journal of Physical Chemistry B (2000), 104 (15), 3712-3720CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)
      The analytic generalized Born approxn. is an efficient electrostatic model that describes mols. in soln. Here it is modified to permit a more accurate description of large macromols., while its established performance on small compds. is nearly unaffected. The modified model is also adapted to describe mols. with an interior dielec. const. not equal to unity. The model was tested by computations of pK shifts for a no. of titratable residues in lysozyme, myoglobin, and bacteriorhodopsin. In general, except for some deeply buried residues of bacteriorhodopsin, the results show reasonable agreement with both exptl. data and calcns. based on numerical soln. of the Poisson-Boltzmann equation. A very close agreement between the two models is obtained in prediction of the pK shifts assocd. with conformational change. The calcns. based on this version of the generalized Born approxn. are much faster than finite difference solns. of the Poisson-Boltzmann equation, which makes the present method useful for a variety of other applications where computational time is a crit. factor. The model may also be integrated into mol. dynamics programs to replace explicit solvent simulations which are particularly time-consuming for large mols.
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      Acun, B.; Hardy, D. J.; Kale, L. V.; Li, K.; Phillips, J. C.; Stone, J. E. Scalable Molecular Dynamics with NAMD on the Summit System. IBM J. Res. Dev. 2018, 62, 1– 9,  DOI: 10.1147/JRD.2018.2888986
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      Scalable Molecular Dynamics with NAMD on the Summit System
      Acun B; Hardy D J; Stone J E; Kale L V; Li K; Phillips J C
      IBM journal of research and development (2018), 62 (6), 1-9 ISSN:.
      NAMD (NAnoscale Molecular Dynamics) is a parallel molecular dynamics application that has been used to make breakthroughs in understanding the structure and dynamics of large biomolecular complexes, such as viruses like HIV and various types of influenza. State-of-the-art biomolecular simulations often require integration of billions of timesteps, computing all interatomic forces for each femtosecond timestep. Molecular dynamics simulation of large biomolecular systems and long-timescale biological phenomena requires tremendous computing power. NAMD harnesses the power of thousands of heterogeneous processors to meet this demand. In this paper, we present algorithm improvements and performance optimizations that enable NAMD to achieve high performance on the IBM Newell platform (with POWER9 processors and NVIDIA Volta V100 GPUs) which underpins the Oak Ridge National Laboratory's Summit and Lawrence Livermore National Laboratory's Sierra supercomputers. The Top-500 supercomputers June 2018 list shows Summit at the number one spot with 187 Petaflop/s peak performance and Sierra third with 119 Petaflop/s. Optimizations for NAMD on Summit include: data layout changes for GPU acceleration and CPU vectorization, improving GPU offload efficiency, increasing performance with PAMI support in Charm++, improving efficiency of FFT calculations, improving load balancing, enabling better CPU vectorization and cache performance, and providing an alternative thermostat through stochastic velocity rescaling. We also present performance scaling results on early Newell systems.
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      Mackerell, A. D., Jr; Feig, M.; Brooks, C. L. 3rd Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J. Comput. Chem. 2004, 25, 1400– 15,  DOI: 10.1002/jcc.20065
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      Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations
      MacKerell, Alexander D., Jr.; Feig, Michael; Brooks, Charles L., III
      Journal of Computational Chemistry (2004), 25 (11), 1400-1415CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
      Computational studies of proteins based on empirical force fields represent a powerful tool to obtain structure-function relationships at an at. level, and are central in current efforts to solve the protein folding problem. The results from studies applying these tools are, however, dependent on the quality of the force fields used. In particular, accurate treatment of the peptide backbone is crucial to achieve representative conformational distributions in simulation studies. To improve the treatment of the peptide backbone, quantum mech. (QM) and mol. mech. (MM) calcns. were undertaken on the alanine, glycine, and proline dipeptides, and the results from these calcns. were combined with mol. dynamics (MD) simulations of proteins in crystal and aq. environments. QM potential energy maps of the alanine and glycine dipeptides at the LMP2/cc-pVxZ/MP2/6-31G* levels, where x = D, T, and Q, were detd., and are compared to available QM studies on these mols. The LMP2/cc pVQZ//MP2/6-31G* energy surfaces for all three dipeptides were then used to improve the MM treatment of the dipeptides. These improvements included addnl. parameter optimization via Monte Carlo simulated annealing and extension of the potential energy function to contain peptide backbone .vphi., ψ dihedral crossterms or a .vphi., ψ grid-based energy correction term. Simultaneously, MD simulations of up to seven proteins in their cryst. environments were used to validate the force field enhancements. Comparison with QM and crystallog. data showed that an addnl. optimization of the .vphi., ψ dihedral parameters along with the grid-based energy correction were required to yield significant improvements over the CHARMM22 force field. However, systematic deviations in the treatment of .vphi. and ψ in the helical and sheet regions were evident. Accordingly, empirical adjustments were made to the grid-based energy correction for alanine and glycine to account for these systematic differences. These adjustments lead to greater deviations from QM data for the two dipeptides but also yielded improved agreement with exptl. crystallog. data. These improvements enhance the quality of the CHARMM force field in treating proteins. This extension of the potential energy function is anticipated to facilitate improved treatment of biol. macromols. via MM approaches in general.
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      Foloppe, N.; MacKerell, A. D., Jr All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data. J. Comput. Chem. 2000, 21, 86– 104,  DOI: 10.1002/(SICI)1096-987X(20000130)21:2<86::AID-JCC2>3.0.CO;2-G
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      All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data
      Foloppe, Nicolas; Mackerell, Alexander D.
      Journal of Computational Chemistry (2000), 21 (2), 86-104CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
      Empirical force-field calcns. on biol. mols. represent an effective method to obtain at. detail information on the relationship of their structure to their function. Results from those calcns. depend on the quality of the force field. In this manuscript, optimization of the CHARMM27 all-atom empirical force field for nucleic acids is presented together with the resulting parameters. The optimization procedure is based on the reprodn. of small mol. target data from both exptl. and quantum mech. studies and condensed phase structural properties of DNA and RNA. Via an iterative approach, the parameters were primarily optimized to reproduce macromol. target data while maximizing agreement with small mol. target data. This approach is expected to ensure that the different contributions from the individual moieties in the nucleic acids are properly balanced to yield condensed phase properties of DNA and RNA, which are consistent with expt. The quality of the presented force field in reproducing both crystal and soln. properties are detailed in the present and an accompanying manuscript (MacKerell and Banavali, J Comput Chem, this issue). The resultant parameters represent the latest step in the continued development of the CHARMM all-atom biomol. force field for proteins, lipids, and nucleic acids.
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      Zhang, C.; Ma, J. Enhanced sampling and applications in protein folding in explicit solvent. J. Chem. Phys. 2010, 132, 244101,  DOI: 10.1063/1.3435332
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      Enhanced sampling and applications in protein folding in explicit solvent
      Zhang, Cheng; Ma, Jianpeng
      Journal of Chemical Physics (2010), 132 (24), 244101/1-244101/16CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      We report a single-copy tempering method for simulating large complex systems. In a generalized ensemble, the method uses runtime est. of the thermal av. energy computed from a novel integral identity to guide a continuous temp.-space random walk. We first validated the method in a two-dimensional Ising model and a Lennard-Jones liq. system. It was then applied to folding of three small proteins, trpzip2, trp-cage, and villin headpiece in explicit solvent. Within 0.5 ∼ 1 μs, all three systems were reversibly folded into at. accuracy: the alpha carbon root mean square deviations of the best folded conformations from the native states were 0.2, 0.4, and 0.4 Å, for trpzip2, trp-cage, and villin headpiece, resp. (c) 2010 American Institute of Physics.
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      Genheden, S.; Kuhn, O.; Mikulskis, P.; Hoffmann, D.; Ryde, U. The normal-mode entropy in the MM/GBSA method: effect of system truncation, buffer region, and dielectric constant. J. Chem. Inf. Model. 2012, 52, 2079– 2088,  DOI: 10.1021/ci3001919
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      The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant
      Genheden, Samuel; Kuhn, Oliver; Mikulskis, Paulius; Hoffmann, Daniel; Ryde, Ulf
      Journal of Chemical Information and Modeling (2012), 52 (8), 2079-2088CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      We have performed a systematic study of the entropy term in the MM/GBSA (mol. mechanics combined with generalized Born and surface-area solvation) approach to calc. ligand-binding affinities. The entropies are calcd. by a normal-mode anal. of harmonic frequencies from minimized snapshots of mol. dynamics simulations. For computational reasons, these calcns. have normally been performed on truncated systems. We have studied the binding of eight inhibitors of blood clotting factor Xa, nine ligands of ferritin, and two ligands of HIV-1 protease and show that removing protein residues with distances larger than 8-16 Å to the ligand, including a 4 Å shell of fixed protein residues and water mols., change the abs. entropies by 1-5 kJ/mol on av. However, the change is systematic, so relative entropies for different ligands change by only 0.7-1.6 kJ/mol on av. Consequently, entropies from truncated systems give relative binding affinities that are identical to those obtained for the whole protein within statistical uncertainty (1-2 kJ/mol). We have also tested to use a distance-dependent dielec. const. in the minimization and frequency calcn. (ε = 4r), but it typically gives slightly different entropies and poorer binding affinities. Therefore, we recommend entropies calcd. with the smallest truncation radius (8 Å) and ε =1. Such an approach also gives an improved precision for the calcd. binding free energies.
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    43. 43
      Vergara-Jaque, A.; Comer, J.; Monsalve, L.; Gonzalez-Nilo, F. D.; Sandoval, C. Computationally efficient methodology for atomic-level characterization of dendrimer–drug complexes: a comparison of amine-and acetyl-terminated PAMAM. J. Phys. Chem. B 2013, 117, 6801– 6813,  DOI: 10.1021/jp4000363
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      Computationally Efficient Methodology for Atomic-Level Characterization of Dendrimer-Drug Complexes: A Comparison of Amine- and Acetyl-Terminated PAMAM
      Vergara-Jaque, Ariela; Comer, Jeffrey; Monsalve, Luis; Gonzalez-Nilo, Fernando D.; Sandoval, Claudia
      Journal of Physical Chemistry B (2013), 117 (22), 6801-6813CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
      PAMAM dendrimers have been widely studied as a novel means for controlled drug delivery; however, computational study of dendrimer-drug complexation is made difficult by the conformational flexibility of dendrimers and the nonspecific nature of the dendrimer-drug interactions. Conventional protocols for studying drug binding have been designed primarily for protein substrates, and, therefore, there is a need to establish new protocols to deal with the unique aspects of dendrimers. In this work, we generate cavities in generation-5 polyamidoamine (PAMAM) dendrimers at selected distances from the center of mass of the dendrimer for the insertion of the model drug: dexamethasone 21-phosphate or Dp21. The complexes are then allowed to equilibrate with distance between centers of mass of the drug and dendrimers confined to selected ranges; the free energy of complexation is estd. by the MM-GBSA (MM, mol. mechanics; GB, generalized Born; SA, surface area) method. For both amine- and modified acetyl-terminated PAMAM at both low and neutral pH, the most favorable free energy of complexation is assocd. with Dp21 at distance of 15-20 Å from the center of mass of the dendrimer and that smaller or larger distances yield considerably weaker affinity. In agreement with exptl. results, we find acetyl-terminated PAMAM at neutral pH to form the least stable complex with Dp21. The greatest affinity is seen in the case of acetyl-terminated PAMAM at low pH, which appears to be due a complex balance of different contributions, which cannot be attributed to electrostatics, van der Waals interactions, hydrogen bonds, or charge-charge interactions alone.
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      Torchala, M.; Moal, I. H.; Chaleil, R. A. G.; Fernandez-Recio, J.; Bates, P. A. SwarmDock: a server for flexible protein-protein docking. Bioinformatics 2013, 29, 807– 9,  DOI: 10.1093/bioinformatics/btt038
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      SwarmDock: a server for flexible protein-protein docking
      Torchala, Mieczyslaw; Moal, Iain H.; Chaleil, Raphael A. G.; Fernandez-Recio, Juan; Bates, Paul A.
      Bioinformatics (2013), 29 (6), 807-809CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Summary: Protein-protein interactions are central to almost all biol. functions, and the at. details of such interactions can yield insights into the mechanisms that underlie these functions. We present a web server that wraps and extends the SwarmDock flexible protein-protein docking algorithm. After uploading PDB files of the binding partners, the server generates low energy conformations and returns a ranked list of clustered docking poses and their corresponding structures. The user can perform full global docking, or focus on particular residues that are implicated in binding. The server is validated in the CAPRI blind docking expt., against the most current docking benchmark, and against the ClusPro docking server, the highest performing server currently available.
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      Torchala, M.; Moal, I. H.; Chaleil, R. A.; Agius, R.; Bates, P. A. A Markov-chain model description of binding funnels to enhance the ranking of docked solutions. Proteins: Struct., Funct., Bioinf. 2013, 81, 2143– 2149,  DOI: 10.1002/prot.24369
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      A Markov-chain model description of binding funnels to enhance the ranking of docked solutions
      Torchala, Mieczyslaw; Moal, Iain H.; Chaleil, Raphael A. G.; Agius, Rudi; Bates, Paul A.
      Proteins: Structure, Function, and Bioinformatics (2013), 81 (12), 2143-2149CODEN: PSFBAF ISSN:. (Wiley-Blackwell)
      Within the crowded, seemingly chaotic environment of the cell, proteins are still able to find their binding partners. This is achieved via an ensemble of trajectories, which funnel them towards their functional binding sites, the binding funnel. Here, we characterize funnel-like energy structures on the global energy landscape using time-homogeneous finite state Markov chain models. These models are based on the idea that transitions can occur between structurally similar docking solns., with transition probabilities detd. by their difference in binding energy. Funnel-like energy structures are those contg. solns. with very high equil. populations. Although these are found surrounding both near-native and false pos. binding sites, we show that the removal of nonfunnel-like energy structures, by filtering away solns. with low max. equil. population, can significantly improve the ranking of docked poses.
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      Wrapp, D.; Wang, N.; Corbett, K. S.; Goldsmith, J. A.; Hsieh, C.-L.; Abiona, O.; Graham, B. S.; McLellan, J. S. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 2020, 367 (6483), 1260– 1263,  DOI: 10.1126/science.abb2507
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      Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
      Wrapp, Daniel; Wang, Nianshuang; Corbett, Kizzmekia S.; Goldsmith, Jory A.; Hsieh, Ching-Lin; Abiona, Olubukola; Graham, Barney S.; McLellan, Jason S.
      Science (Washington, DC, United States) (2020), 367 (6483), 1260-1263CODEN: SCIEAS; ISSN:1095-9203. (American Association for the Advancement of Science)
      The outbreak of a novel coronavirus (2019-nCoV) represents a pandemic threat that has been declared a public health emergency of international concern. The CoV spike (S) glycoprotein is a key target for vaccines, therapeutic antibodies, and diagnostics. To facilitate medical countermeasure development, we detd. a 3.5-angstrom-resoln. cryo-electron microscopy structure of the 2019-nCoV S trimer in the prefusion conformation. The predominant state of the trimer has one of the three receptor-binding domains (RBDs) rotated up in a receptor-accessible conformation. We also provide biophys. and structural evidence that the 2019-nCoV S protein binds angiotensin-converting enzyme 2 (ACE2) with higher affinity than does severe acute respiratory syndrome (SARS)-CoV S. Addnl., we tested several published SARS-CoV RBD-specific monoclonal antibodies and found that they do not have appreciable binding to 2019-nCoV S, suggesting that antibody cross-reactivity may be limited between the two RBDs. The structure of 2019-nCoV S should enable the rapid development and evaluation of medical countermeasures to address the ongoing public health crisis.
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      Mansbach, R. A.; Chakraborty, S.; Nguyen, K.; Montefiori, D. C.; Korber, B.; Gnanakaran, S. The SARS-CoV-2 Spike variant D614G favors an open conformational state. Sci. Adv. 2021, 7 (16), eabf3671  DOI: 10.1126/sciadv.abf3671
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      Gnanasekaran, R.; Agbo, J. K.; Leitner, D. M. Communication maps computed for homodimeric hemoglobin: computational study of water-mediated energy transport in proteins. J. Chem. Phys. 2011, 135, 065103,  DOI: 10.1063/1.3623423
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      Communication maps computed for homodimeric hemoglobin: Computational study of water-mediated energy transport in proteins
      Gnanasekaran, Ramachandran; Agbo, Johnson K.; Leitner, David M.
      Journal of Chemical Physics (2011), 135 (6), 065103/1-065103/10CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      Frequency-resolved communication maps provide a coarse-grained picture of energy transport in nanoscale systems. We calc. communication maps for homodimeric Hb from Scapharca inaequivalvis and sample them to elucidate energy transfer pathways between the binding sites and other parts of the protein with focus on the role of the cluster of water mols. at the interface between the globules. We complement anal. of communication maps with mol. simulations of energy flow. Both approaches reveal that excess energy in one heme flows mainly to regions of the interface where early hydrogen bond rearrangements occur in the allosteric transition. In particular, energy is carried disproportionately by the water mols., consistent with the larger thermal cond. of water compared to proteins. (c) 2011 American Institute of Physics.
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      Leitner, D. M. Water-mediated energy dynamics in a homodimeric hemoglobin. J. Phys. Chem. B 2016, 120, 4019– 4027,  DOI: 10.1021/acs.jpcb.6b02137
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      Water-Mediated Energy Dynamics in a Homodimeric Hemoglobin
      Leitner, David M.
      Journal of Physical Chemistry B (2016), 120 (17), 4019-4027CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
      We examine energy dynamics in the unliganded and liganded states of the homodimeric Hb from Scapharca inaequivalvis (HbI), which exhibits cooperativity mediated by the cluster of water mols. at the interface upon ligand binding and dissocn. We construct and analyze a dynamic network in which nodes representing the residues, hemes, and water cluster are connected by edges that represent energy transport times, as well as a nonbonded network (NBN) indicating regions that respond rapidly to local strain within the protein via nonbonded interactions. One of the two largest NBNs includes the Lys30-Asp89 salt bridge crit. for stabilizing the dimer. The other includes the hemes and surrounding residues, as well as, in the unliganded state, the cluster of water mols. between the globules. Energy transport in the protein appears to be controlled by the Lys30-Asp89 salt bridge crit. for stabilizing the dimer, as well as the interface water cluster in the unliganded state. Possible connections between energy transport dynamics in response to local strain identified here and allosteric transitions in HbI are discussed.
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      Lee, Y.; Choi, S.; Hyeon, C. Mapping the intramolecular signal transduction of G-protein coupled receptors. Proteins 2014, 82, 727– 43,  DOI: 10.1002/prot.24451
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      Mapping the intramolecular signal transduction of G-protein coupled receptors
      Lee, Yoonji; Choi, Sun; Hyeon, Changbong
      Proteins: Structure, Function, and Bioinformatics (2014), 82 (5), 727-743CODEN: PSFBAF ISSN:. (Wiley-Blackwell)
      G-protein coupled receptors (GPCRs), a major gatekeeper of extracellular signals on plasma membrane, are unarguably one of the most important therapeutic targets. Given the recent discoveries of allosteric modulations, an allosteric wiring diagram of intramol. signal transductions would be of great use to glean the mechanism of receptor regulation. Here, by evaluating betweenness centrality (CB) of each residue, we calc. maps of information flow in GPCRs and identify key residues for signal transductions and their pathways. Compared with preexisting approaches, the allosteric hotspots that our CB-based anal. detects for human A2A adenosine receptor (A2AAR) and bovine rhodopsin are better correlated with biochem. data. In particular, our anal. outperforms other methods in locating the rotameric microswitches, which are generally deemed crit. for mediating orthosteric signaling in class A GPCRs. For A2AAR, the inter-residue cross-correlation map, calcd. using equil. structural ensemble from mol. dynamics simulations, reveals that strong signals of long-range transmembrane communications exist only in the agonist-bound state. A seemingly subtle variation in structure, found in different GPCR subtypes or imparted by agonist bindings or a point mutation at an allosteric site, can lead to a drastic difference in the map of signaling pathways and protein activity. The signaling map of GPCRs provides valuable insights into allosteric modulations as well as reliable identifications of orthosteric signaling pathways. Proteins 2013. © 2013 Wiley Periodicals, Inc.
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      Leitner, D. M.; Hyeon, C.; Reid, K. M. Water-mediated biomolecular dynamics and allostery. J. Chem. Phys. 2020, 152, 240901,  DOI: 10.1063/5.0011392
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      Water-mediated biomolecular dynamics and allostery
      Leitner, David M.; Hyeon, Changbong; Reid, Korey M.
      Journal of Chemical Physics (2020), 152 (24), 240901CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      Dynamic coupling with water contributes to regulating the functional dynamics of a biomol. Protein-water dynamics, with emphasis on water that is partially confined, and the role of protein-confined water dynamics in allosteric regulation are discussed. These properties are illustrated with two systems, a homodimeric Hb from Scapharca inaequivalvis (HbI) and an A2A adenosine receptor (A2AAR). For HbI, water-protein interactions, long known to contribute to the thermodn. of cooperativity, influence the dynamics of the protein not only around the protein-water interface but also into the core of each globule, where dynamic and entropic changes upon ligand binding are coupled to protein-water contact dynamics. Similarly, hydration waters trapped deep inside the core region of A2AAR enable the formation of an allosteric network made of water-mediated inter-residue contacts. Extending from the ligand binding pocket to the G-protein binding site, this allosteric network plays key roles in regulating the activity of the receptor. (c) 2020 American Institute of Physics.
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      Di Paola, L.; Giuliani, A. Protein contact network topology: a natural language for allostery. Curr. Opin. Struct. Biol. 2015, 31, 43– 8,  DOI: 10.1016/j.sbi.2015.03.001
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      Protein contact network topology: a natural language for allostery
      Di Paola, Luisa; Giuliani, Alessandro
      Current Opinion in Structural Biology (2015), 31 (), 43-48CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
      A review. Protein mols. work as a whole, so that any local perturbation may resonate on the entire structure; allostery deals with this general property of protein mols. It is worth noting that a perturbation does not necessarily involve a conformational change but, more generally, it travels across the structure as an 'energy signal'. The at. interactions within the network provide the structural support for this 'signaling highway'. Network descriptors, capturing network signaling efficiency, explain allostery in terms of signal transmission. Here, the authors survey the key applications of graph theory to protein allostery. The complex network approach introduces a new perspective in biochem.; as for applications, the development of new drugs relying on allosteric effects (allo-network drugs) represents a promising avenue of contact network formalism.
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