psnGPCRdb: The Structure-network Database of G Protein Coupled Receptors

G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market. Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure. psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well. psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design. psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.

Molecular dynamics simulations suggest possible activation and deactivation pathways in the hERG channel

The elusive activation/deactivation mechanism of hERG is investigated, a voltage-gated potassium channel involved in severe inherited and drug-induced cardiac channelopathies, including the Long QT Syndrome. Firstly, the available structural data are integrated by providing a homology model for the closed state of the channel. Secondly, molecular dynamics combined with a network analysis revealed two distinct pathways coupling the voltage sensor domain with the pore domain. Interestingly, some LQTS-related mutations known to impair the activation/deactivation mechanism are distributed along the identified pathways, which thus suggests a microscopic interpretation of their role. Split channels simulations clarify a surprising feature of this channel, which is still able to gate when a cut is introduced between the voltage sensor domain and the neighboring helix S5. In summary, the presented results suggest possible activation/deactivation mechanisms of non-domain-swapped potassium channels that may aid in biomedical applications.

Costa et al. present the electro-mechanical coupling between the voltage sensor and pore domain of the hERG channel using a combination of molecular dynamics simulations and theoretical network analyses.

PSNtools for standalone and web-based structure network analyses of conformational ensembles

Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function.

The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis.

Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold.

In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output.

PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.

Exploring K v 1.2 Channel Inactivation Through MD Simulations and Network Analysis

The KCNA2 gene encodes the K v 1.2 channel, a mammalian Shaker-like voltage-gated K+ channel, whose defections are linked to neuronal deficiency and childhood epilepsy. Despite the important role in the kinetic behavior of the channel, the inactivation remained hereby elusive. Here, we studied the K v 1.2 inactivation via a combined simulation/network theoretical approach that revealed two distinct pathways coupling the Voltage Sensor Domain and the Pore Domain to the Selectivity Filter. Additionally, we mutated some residues implicated in these paths and we explained microscopically their function in the inactivation mechanism by computing a contact map. Interestingly, some pathological residues shown to impair the inactivation lay on the paths. In summary, the presented results suggest two pathways as the possible molecular basis of the inactivation mechanism in the K v 1.2 channel. These pathways are consistent with earlier mutational studies and known mutations involved in neuronal channelopathies.

Structure network-based landscape of rhodopsin misfolding by mutations and algorithmic prediction of small chaperone action

Failure of a protein to achieve its functional structural state and normal cellular location contributes to the etiology and pathology of heritable human conformational diseases. The autosomal dominant form of retinitis pigmentosa (adRP) is an incurable blindness largely linked to mutations of the membrane protein rod opsin. While the mechanisms underlying the noxious effects of the mutated protein are not completely understood, a common feature is the functional protein conformational loss. Here, the wild type and 39 adRP rod opsin mutants were subjected to mechanical unfolding simulations coupled to the graph theory-based protein structure network analysis.

A robust computational model was inferred and in vitro validated in its ability to predict endoplasmic reticulum retention of adRP mutants, a feature linked to the mutation-caused misfolding. The structure-based approach could also infer the structural determinants of small chaperone action on misfolded protein mutants with therapeutic implications.

The approach is exportable to conformational diseases linked to missense mutations in any membrane protein.

Bcl-xL Dynamics under the Lens of Protein Structure Networks

Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research because the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between the promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. In particular, B-cell lymphoma extra-large (Bcl-x_L) is a target of interest due to the forefront role of its dysfunctions in cancer development. Bcl-x_L prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, like PUMA, and the noncanonical partners, such as p53, at different sites. An allosteric communication between the BH3-only protein binding pocket and the p53 binding site, mediating the release of p53 from Bcl-x_L upon PUMA binding, has been postulated and supported by nuclear magnetic resonance and other biophysical data. The molecular details of this mechanism, especially at the residue level, remain unclear. In this work, we investigated the distal communication between these two sites in Bcl-x_L in its free state and when bound to PUMA. We also evaluated how missense mutations of Bcl-x_L found in cancer samples might impair this communication and therefore the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations, analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.

Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations

The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure-(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.

webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules

A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.

Identification of potential allosteric binding sites in cathepsin K based on intramolecular communication

Network theory methods and molecular dynamics (MD) simulations are accepted tools to study allosteric regulation. Indeed, dynamic networks built upon correlation analysis of MD trajectories provide detailed information about communication paths between distant sites. In this context, we aimed to understand whether the efficiency of intramolecular communication could be used to predict the allosteric potential of a given site. To this end, we performed MD simulations and network theory analyses in cathepsin K (catK), whose allosteric sites are well defined. To obtain a quantitative measure of the efficiency of communication, we designed a new protocol that enables the comparison between properties related to ensembles of communication paths obtained from different sites. Further, we applied our strategy to evaluate the allosteric potential of different catK cavities not yet considered for drug design. Our predictions of the allosteric potential based on intramolecular communication correlate well with previous catK experimental and theoretical data. We also discuss the possibility of applying our approach to other proteins from the same family.

Significance of triple torsional correlations in proteins

The free energy landscape (FEL) of a given complex molecular system is fundamentally the joint probability density of its many comprising degrees of freedom (DOFs). Computation of a complete FEL at atomistic scale is unfortunately intractable for a typical biomolecular system. The challenge of entropy calculation comes from various correlations among different DOFs. The common strategy to treat such complexity is expansion of the full correlation into various orders of local correlations. In reality, expansion is usually cut off at the second order (i.e. pairwise interactions) for protein torsional correlations without reliable estimation of the resulting error. Here, we estimated the mutual information of different torsion sets and found that triple correlations were significant for both local/distant residue pairs and consecutive backbone torsional segments. As expected, the third order approximations were found to be consistently better than the second order approximations. These findings were true for all analyzed proteins with different folds, were independent of the two different force fields utilized to generate trajectory sets, and were therefore likely to be of general importance for proteins. Additionally, binning strategies are of universal importance for numerical computation of correlations, we here provided a detailed comparison between equal-width and equal-sample binning for different bin numbers and demonstrated the impact of binning strategies on variances and biases of calculated mutual information. Our observation suggested that caution should be taken when quantitative comparison of correlations were intended between different studies with different binning strategies.

Torsional mutual information for 10 typical residue pairs calculated with full joint distributions (MI), second order expansion (MI₂), third order expansions (MI₃), and their linear recombinations (MI_LR).

Eigenvector centrality for characterization of protein allosteric pathways

Determining the principal energy-transfer pathways responsible for allosteric communication in biomolecules remains challenging, partially due to the intrinsic complexity of the systems and the lack of effective characterization methods. In this work, we introduce the eigenvector centrality metric based on mutual information to elucidate allosteric mechanisms that regulate enzymatic activity. Moreover, we propose a strategy to characterize the range of correlations that underlie the allosteric processes. We use the V-type allosteric enzyme imidazole glycerol phosphate synthase (IGPS) to test the proposed methodology. The eigenvector centrality method identifies key amino acid residues of IGPS with high susceptibility to effector binding. The findings are validated by solution NMR measurements yielding important biological insights, including direct experimental evidence for interdomain motion, the central role played by helix h[Formula: see text], and the short-range nature of correlations responsible for the allosteric mechanism. Beyond insights on IGPS allosteric pathways and the nature of residues that could be targeted by therapeutic drugs or site-directed mutagenesis, the reported findings demonstrate the eigenvector centrality analysis as a general cost-effective methodology to gain fundamental understanding of allosteric mechanisms at the molecular level.

Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Normal mode analysis (NMA) can facilitate quick and systematic investigation of protein dynamics using data from the Protein Data Bank (PDB). We developed an elastic network model-based NMA program using dihedral angles as independent variables. Compared to the NMA programs that use Cartesian coordinates as independent variables, key attributes of the proposed program are as follows: (1) chain connectivity related to the folding pattern of a polypeptide chain is naturally embedded in the model; (2) the full-atom system is acceptable, and owing to a considerably smaller number of independent variables, the PDB data can be used without further manipulation; (3) the number of variables can be easily reduced by some of the rotatable dihedral angles; (4) the PDB data for any molecule besides proteins can be considered without coarse-graining; and (5) individual motions of constituent subunits and ligand molecules can be easily decomposed into external and internal motions to examine their mutual and intrinsic motions. Its performance is illustrated with an example of a DNA-binding allosteric protein, a catabolite activator protein. In particular, the focus is on the conformational change upon cAMP and DNA binding, and on the communication between their binding sites remotely located from each other. In this illustration, NMA creates a vivid picture of the protein dynamics at various levels of the structures, i.e., atoms, residues, secondary structures, domains, subunits, and the complete system, including DNA and cAMP. Comparative studies of the specific protein in different states, e.g., apo- and holo-conformations, and free and complexed configurations, provide useful information for studying structurally and functionally important aspects of the protein.

Uncovering GPCR and G Protein Function by Protein Structure Network Analysis

Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used for investigating structural communication in biomolecular systems. Information on the system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM-NMA). This chapter reports on selected applications of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs) and G proteins. Strategies to highlight changes in structural communication caused by mutations, ligand and protein binding are described. Conserved amino acids, sites of misfolding mutations, or ligands acting as functional switches tend to behave as hubs in the native structure networks. Densely linked regions in the protein structure graphs could be identified as playing central roles in protein stability and function. Changes in the communication pathway fingerprints depending on the bound ligand or following amino acid mutation could be highlighted as well. A bridge between misfolding and misrouting could be established in rhodopsin mutants linked to inherited blindness. The analysis of native network perturbations by misfolding mutations served to infer key structural elements of protein responsiveness to small chaperones with implications for drug discovery.

Nonlinear backbone torsional pair correlations in proteins

Protein allostery requires dynamical structural correlations. Physical origin of which, however, remain elusive despite intensive studies during last two and half decades. Based on analysis of molecular dynamics (MD) simulation trajectories for ten proteins with different sizes and folds, we found that nonlinear backbone torsional pair (BTP) correlations, which are mainly spatially long-ranged and are dominantly executed by loop residues, exist extensively in most analyzed proteins. Examination of torsional motion for correlated BTPs suggested that such nonlinear correlations are mainly associated aharmonic torsional state transitions and in some cases strongly anisotropic local torsional motion of participating torsions, and occur on widely different and relatively longer time scales. In contrast, correlations between backbone torsions in stable α helices and β strands are mainly linear and spatially short-ranged, and are more likely to associate with harmonic local torsional motion. Further analysis revealed that the direct cause of nonlinear contributions are heterogeneous linear correlations. These findings implicate a general search strategy for novel allosteric modulation sites of protein activities.

Structure network analysis to gain insights into GPCR function

G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM–NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM–NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

Conformational Changes and Free Energies in a Proline Isomerase

Proteins are dynamic molecules and their ability to adopt alternative conformations is central to their biological function. The structural and biophysical properties of transiently and sparsely populated states are, however, difficult to study and an atomic-level description of those states is challenging. We have used enhanced-sampling all-atom, explicit-solvent molecular simulations, guided by structural information from X-ray crystallography and NMR, to describe quantitatively the transition between the major and a minor state of Cyclophilin A, thus providing new insight into how dynamics can affect enzyme function. We calculate the conformational free energy between the two states, and comparison with experiments demonstrates a surprisingly high accuracy for both the wild type protein and a mutant that traps the protein in its alternative conformation. Our results demonstrate how the combination of state-of-the-art force fields and enhanced sampling methods can provide a detailed and quantitative description of the conformational changes in proteins such as those observed in Cyclophilin A.

Conformational transition pathway in the inhibitor binding process of human monoacylglycerol lipase

Human monoacylglycerol lipase (MGL) catalyzes the hydrolysis of 2-arachidonoylglycerol to arachidonic and glycerol, which plays a pivotal role in the normal biological processes of brain. Co-crystal structure of the MGL in complex with its inhibitor, compound 1, shows that the helix α4 undergoes large-scale conformational changes in response to the compound 1 binding compared to the apo MGL. However, the detailed conformational transition pathway of the helix α4 in the inhibitor binding process of MGL has remained unclear. Here, conventional molecular dynamics (MD) and nudged elastic band (NEB) simulations were performed to explore the conformational transition pathway of the helix α4. Conventional MD simulations unveiled that the compound 1 induced the closed conformation of the active site of MGL, reduced the conformational flexibility of the helix α4, and elicited the large-scale conformational rearrangement of the helix α4, leading to the complete folding of the helix α4. Moreover, NEB simulations revealed that the conformational transition pathway of helix α4 underwent an almost 180° counter-clockwise rotation of the helix α4. Our computational results advance the structural and mechanistic understanding of the inhibitory mechanism.

(Dis)similarity Index To Compare Correlated Motions in Molecular Simulations

Molecular dynamics (MD) simulations are widely used to complement or guide experimental studies in the characterization of protein dynamics, thanks to improvements in force-field accuracy, along with in the software and hardware to sample the conformational landscape of proteins. Among the different applications of MD simulations, the study of correlated motions is largely employed for different purposes. Several metrics have been developed to describe correlated motions in the MD ensemble, such as methods based on Pearson Correlation or Mutual Information. Cross-correlation analysis of MD trajectories is indeed appealing not only to identify residues characterized by coupled fluctuations in protein structures but also since it can be used to extrapolate motions along directions in which major conformational changes should occur, for example on longer time scales than the ones that are actually simulated. Nevertheless, most of the MD studies employ average correlation maps and mostly in a qualitative way, even when different systems or different replicates of the same system are compared. The broad application of correlation metrics in the analysis of MD simulations, especially for comparative purposes, requires a step forward toward more quantitative and accurate comparisons. We thus here employed a simple but effective index, which is based on a normalized Frobenius norm of the differences between protein correlation maps, to compare correlated motions. We applied this index for a quantitative comparison of correlated motions from MD simulations of seven proteins of different size and fold. We also employed the index to assess the robustness of correlation description when multi-replicate MD simulations of a same system are used, and we compared our index to metrics for comparison of structural ensembles such as Root Mean Square Inner Product and the Bhattacharyya Coefficient.

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity

In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

Communication routes in ARID domains between distal residues in helix 5 and the DNA-binding loops

ARID is a DNA-binding domain involved in several transcriptional regulatory processes, including cell-cycle regulation and embryonic development. ARID domains are also targets of the Human Cancer Protein Interaction Network. Little is known about the molecular mechanisms related to conformational changes in the family of ARID domains. Thus, we have examined their structural dynamics to enrich the knowledge on this important family of regulatory proteins. In particular, we used an approach that integrates atomistic simulations and methods inspired by graph theory. To relate these properties to protein function we studied both the free and DNA-bound forms. The interaction with DNA not only stabilizes the conformations of the DNA-binding loops, but also strengthens pre-existing paths in the native ARID ensemble for long-range communication to those loops. Residues in helix 5 are identified as critical mediators for intramolecular communication to the DNA-binding regions. In particular, we identified a distal tyrosine that plays a key role in long-range communication to the DNA-binding loops and that is experimentally known to impair DNA-binding. Mutations at this tyrosine and in other residues of helix 5 are also demonstrated, by our approach, to affect the paths of communication to the DNA-binding loops and alter their native dynamics. Overall, our results are in agreement with a scenario in which ARID domains exist as an ensemble of substates, which are shifted by external perturbation, such as the interaction with DNA. Conformational changes at the DNA-binding loops are transmitted long-range by intramolecular paths, which have their heart in helix 5.

Structural basis of valmerins as dual inhibitors of GSK3β/CDK5

Development of multi-target drugs is becoming increasingly attractive in the repertoire of protein kinase inhibitors discovery. In this study, we carried out molecular docking, molecular dynamics simulations, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations, principal component analysis (PCA), and dynamical cross-correlation matrices (DCCM) to dissect the molecular mechanism for the valmerin-19 acting as a dual inhibitor for glycogen synthase kinase 3β (GSK3β) and cyclin-dependent kinase 5 (CDK5). Detailed MM-PBSA calculations revealed that the binding free energies of the valmerin-19 to GSK3β/CDK5 were calculated to be -12.60 ± 2.28 kcal mol(-1) and -11.85 ± 2.54 kcal mol(-1), respectively, indicating that valmerin-19 has the potential to act as a dual inhibitor of GSK3β/CDK5. The analyses of PCA and DCCM results unraveled that binding of the valmerin-19 reduced the conformational dynamics of GSK3β/CDK5 and the valmerin-19 bound to GSK3β/CDK5 might occur mostly through a conformational selection mechanism. This study may be helpful for the future design of novel and potent dual GSK3β/CDK5 inhibitors.

Analysis on long-range residue-residue communication using molecular dynamics

We investigated the possibility of inter-residue communication of side chains in barstar, an 89 residue protein, using mutual information theory. The normalized mutual information (NMI) of the dihedral angles of the side chains was obtained from all-atom molecular dynamics simulations. The accumulated NMI from an explicit solvent equilibrated trajectory (600 ns) with free backbone exhibits a parabola-shaped distribution over the inter-residue distances (0-36 Å): smaller at the end regimes but larger in the middle regime. This analysis, plus several other measures, does not find unusual long-range communication for free backbone in explicit solvent simulations.

PyInteraph: a framework for the analysis of interaction networks in structural ensembles of proteins

In the last years, a growing interest has been gathering around the ability of Molecular Dynamics (MD) to provide insight into the paths of long-range structural communication in biomolecules. The knowledge of the mechanisms related to structural communication helps in the rationalization in atomistic details of the effects induced by mutations, ligand binding, and the intrinsic dynamics of proteins. We here present PyInteraph, a tool for the analysis of structural ensembles inspired by graph theory. PyInteraph is a software suite designed to analyze MD and structural ensembles with attention to binary interactions between residues, such as hydrogen bonds, salt bridges, and hydrophobic interactions. PyInteraph also allows the different classes of intra- and intermolecular interactions to be represented, combined or alone, in the form of interaction graphs, along with performing network analysis on the resulting interaction graphs. The program also integrates the network description with a knowledge-based force field to estimate the interaction energies between side chains in the protein. It can be used alone or together with the recently developed xPyder PyMOL plugin through an xPyder-compatible format. The software capabilities and associated protocols are here illustrated by biologically relevant cases of study. The program is available free of charge as Open Source software via the GPL v3 license at http://linux.btbs.unimib.it/pyinteraph/.

A Mixed Protein Structure Network and Elastic Network Model Approach to Predict the Structural Communication in Biomolecular Systems: The PDZ2 Domain from Tyrosine Phosphatase 1E As a Case Study

Graph theory is being increasingly used to study the structural communication in biomolecular systems. This requires incorporating information on the system's dynamics, which is time-consuming and not suitable for high-throughput investigations. We propose a mixed Protein Structure Network (PSN) and Elastic Network Model (ENM)-based strategy, i.e., PSN-ENM, for fast investigation of allosterism in biological systems. PSN analysis and ENM-Normal Mode Analysis (ENM-NMA) are implemented in the structural analysis software Wordom, freely available at http://wordom.sourceforge.net/ . The method performs a systematic search of the shortest communication pathways that traverse a protein structure. A number of strategies to compare the structure networks of a protein in different functional states and to get a global picture of communication pathways are presented as well. The approach was validated on the PDZ2 domain from tyrosine phosphatase 1E (PTP1E) in its free (APO) and peptide-bound states. PDZ domains are, indeed, the systems whose structural communication and allosteric features are best characterized both in vitro and in silico. The agreement between predictions by the PSN-ENM method and in vitro evidence is remarkable and comparable to or higher than that reached by more time-consuming computational approaches tested on the same biological system. Finally, the PSN-ENM method was able to reproduce the salient communication features of unbound and bound PTP1E inferred from molecular dynamics simulations. High speed makes this method suitable for high throughput investigation of the communication pathways in large sets of biomolecular systems in different functional states.

Network analysis to uncover the structural communication in GPCRs

Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system dynamics can be provided by atomistic molecular dynamics simulations or coarse-grained Elastic Network Models paired with Normal Mode Analysis (ENM-NMA). This chapter describes the application of PSN analysis to uncover the structural communication in G protein-coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding mutations, dimerization, and activation are described. Focus is put on the ENM-NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signaling active (meta II (MII)) states, highlighting clear changes in the PSN and the centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

xPyder: a PyMOL plugin to analyze coupled residues and their networks in protein structures

A versatile method to directly identify and analyze short- or long-range coupled or communicating residues in a protein conformational ensemble is of extreme relevance to achieve a complete understanding of protein dynamics and structural communication routes. Here, we present xPyder, an interface between one of the most employed molecular graphics systems, PyMOL, and the analysis of dynamical cross-correlation matrices (DCCM). The approach can also be extended, in principle, to matrices including other indexes of communication propensity or intensity between protein residues, as well as the persistence of intra- or intermolecular interactions, such as those underlying protein dynamics. The xPyder plugin for PyMOL 1.4 and 1.5 is offered as Open Source software via the GPL v2 license, and it can be found, along with the installation package, the user guide, and examples, at http://linux.btbs.unimib.it/xpyder/.