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

Effect of Direct and Water-Mediated Interactions on the Identification of Hotspots in Biomolecular Complexes with Multiple Subsystems

Identification of important residues in biochemical complexes is often a crucial step for many problems in molecular biology and biochemistry. A method is proposed to identify hotspots in biomolecular complexes based on a new metric, derived from networks representing molecular subunits (residues, bridging solvent molecules, ligands etc.) connected by interactions. A singular value decomposition of the weighted adjacency matrix is used to construct a scalar rank for each subunit that reflects its importance in the residue interaction network. This metric is called the singular value centrality. In addition, a new formalism is proposed to account for water-mediated interactions in the ranking of residues. Interactions for a residue network can be provided by various computational methods. In this work interactions are obtained from full quantum-mechanical calculations of protein-protein complexes using the fragment molecular orbital method. The ranking results are shown to be in good agreement with earlier computational and experimental studies. The developed method can be used to gain a deeper insight into the role of subunits in complex biomolecular systems.

Elucidation of bezlotoxumab binding specificity to toxin B in Clostridioides difficile

C. difficile or Clostridioides difficile infection (CDI) is currently one of the major causes of epidemics worldwide. Toxin B from Clostridioides difficile toxin B (TcdB) infection is the main target protein inhibiting CDI recurrence. Clinical research suggested that bezlotoxumab's (Bez) efficiency is significantly reduced in neutralizing the B2 strain compared to the B1 strain. The monoclonal antibody (mAb) functions by binding to the epitope 1 and 2 regions in the combined repetitive oligopeptide (CROP) domain. Some binding residues are distinctively different between B1 and B2 strains. In this work, we aimed to elucidate and compare insights into the interaction of toxins B1 and B2 in complex with Bez by using all-atom molecular dynamics (MD) simulations and binding free energy calculations. The predicted ΔGbinding values suggested that the antibody (Ab) could bind to toxin B1 significantly better than B2, supported by higher salt bridge and hydrogen bonding (H-bonding) interactions, as well as the number of contact residues between the two focused proteins. The toxin B1 residues important for binding with Bez were E1878, T1901, E1902, F1905, N1941, V1946, N2031, T2032, E2033, V2076, V2077, and E2092. The lower susceptibility of Bez towards toxin B2 was primarily due to a change of residue E2033 from glutamate to alanine (A2033) and the loss of E1878 and E1902 contributions, as determined by the intermolecular interaction changes from the dynamic residue interaction network (dRIN) analysis. The obtained data strengthen our understanding of Bez/toxin B binding.

Fascin - F-actin interaction studied by molecular dynamics simulation and protein network analysis

Actin bundles are an important component of cellular cytoskeleton and participate in the movement of cells. The formation of actin bundles requires the participation of many actin binding proteins (ABPs). Fascin is a member of ABPs, which plays a key role in bundling filamentous actin (F-actin) to bundles. However, the detailed interactions between fascin and F-actin are unclear. In this study, we construct an atomic-level structure of fascin - F-actin complex based on a rather poor cryo-EM data with resolution of 20 nm. We first optimized the geometries of the complex by molecular dynamics (MD) simulation and analyzed the binding site and pose of fascin which bundles two F-actin chains. Next, binding free energy of fascin was calculated by MM/GBSA method. Finally, protein structure network analysis (PSNs) was performed to analyze the key residues for fascin binding. Our results show that residues of K22, E27, E29, K41, K43, R110, R149, K358, R408 and K471 on fascin are important for its bundling, which are in good agreement with the experimental data. On the other hand, the consistent results indicate that the atomic-level model of fascin - F-actin complex is reliable. In short, this model can be used to understand the detailed interactions between fascin and F-actin, and to develop novel potential drugs targeting fascin.Communicated by Ramaswamy H. Sarma.

Fenofibrate inhibits MOXD1 and PDZK1IP1 expression and improves lipid deposition and inflammation in mice with alcoholic fatty liver

AIMS

Alcoholic liver disease (ALD) can develop into cirrhosis and hepatocellular carcinoma but no specific drugs are available. Fenofibrate is therapeutically effective in ALD, however, the exact mechanism remains unknown. We explored the hub genes of ALD and the role of fenofibrate in ALD.

MAIN METHODS

The hub genes of ALD were screened by bioinformatics method, and their functional enrichment, signalling pathways, target genes and their correlation with immune microenvironment and pathogenic genes were analysed. We also analysed the binding affinity of fenofibrate to proteins of hub genes using molecular docking techniques, and the effects on hub gene expression, lipid deposition, oxidative stress and inflammation in the liver of National Institute on Alcohol Abuse and Alcoholism (NIAAA) model mice. The regulatory effects of fenofibrate on MOXD1 and PDZK1P1 were investigated after gene silencing of peroxisome proliferator-activated receptor-α (Ppar-α).

KEY FINDINGS

Hub genes identified, including monooxygenase DBH-like 1 (MOXD1), PDZK1-interacting protein 1 (PDZK1IP1) and solute carrier 51 β (SLC51B), are highly predictive for ALD. Hepatic MOXD1 and PDZK1IP1 expression was elevated in patients with ALD and NIAAA model mice, with no significant difference in SLC51B expression between the groups. Fenofibrate binds tightly to MOXD1 and PDZK1IP1, inhibits their hepatic expression independently of PPAR-α signalling, and ameliorates lipid deposition, oxidative stress and inflammatory responses in NIAAA model mice.

SIGNIFICANCE

MOXD1 and PDZK1IP1 are key genes in ALD progression; fenofibrate improves liver damage in NIAAA model mice by downregulating their expression. Our findings provide insight for improving diagnostic and therapeutic strategies for ALD.

Unveiling differential mechanisms of chuanxiong cortex and pith in the treatment of coronary heart disease using SPME-GC×GC-MS and network pharmacology

Ligusticum chuanxiong Hort (LCH) is a well-known traditional Chinese medicinal herb for treating coronary heart disease (CHD). This study investigated the differential preventive mechanisms of Rhizome Cortex (RC) and Rhizome Pith (RP) of LCH. Solid-phase microextraction combined with comprehensive two-dimensional gas chromatography-tandem mass spectrometry analysis identified 32 differential components, and network pharmacology revealed 11 active ingredients and 191 gene targets in RC, along with 12 active ingredients and 318 gene targets in RP. Primary active ingredients in RC were carotol, epicubenol, fenipentol, and methylisoeugenol acetate, while 3-undecanone, (E)- 5-decen-1-ol acetate, linalyl acetate, and (E)- 2-Methoxy-4-(prop-1-enyl) phenol were dominant in RP. KEGG mapping analysis associated 27 pathways with RC targets and 116 pathways with RP targets. Molecular docking confirmed the efficient activation of corresponding targets by these active ingredients. This study provides valuable insights into the preventive and therapeutic effects of RC and RP in CHD.

Role of distal sites in enzyme engineering

The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.

Molecular and Structural Analysis of Specific Mutations from Saudi Isolates of SARS-CoV-2 RNA-Dependent RNA Polymerase and their Implications on Protein Structure and Drug-Protein Binding

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has stressed the global health system to a significant level, which has not only resulted in high morbidity and mortality but also poses a threat for future pandemics. This situation warrants efforts to develop novel therapeutics to manage SARS-CoV-2 in specific and other emerging viruses in general. This study focuses on SARS-CoV2 RNA-dependent RNA polymerase (RdRp) mutations collected from Saudi Arabia and their impact on protein structure and function. The Saudi SARS-CoV-2 RdRp sequences were compared with the reference Wuhan, China RdRp using a variety of computational and biophysics-based approaches. The results revealed that three mutations-A97V, P323I and Y606C-may affect protein stability, and hence the relationship of protein structure to function. The apo wild RdRp is more dynamically stable with compact secondary structure elements compared to the mutants. Further, the wild type showed stable conformational dynamics and interaction network to remdesivir. The net binding energy of wild-type RdRp with remdesivir is -50.76 kcal/mol, which is more stable than the mutants. The findings of the current study might deliver useful information regarding therapeutic development against the mutant RdRp, which may further furnish our understanding of SARS-CoV-2 biology.

Exploring the Mechanism of Hawthorn Leaves Against Coronary Heart Disease Using Network Pharmacology and Molecular Docking

Hawthorn leaves, which is a traditional Chinese medicine (TCM), has been used for treating coronary heart disease (CHD) for a long time in China. But the limited understanding of the main active components and molecular mechanisms of this traditional medicine has restricted its application and further research. The active compounds of hawthorn leaves were obtained from TCMSP database and SymMap database. The targets of it were predicted based on TCMSP, PubChem, Swiss Target Prediction, and SymMap database. The putative targets of CHD were gathered from multi-sources databases including the Online Mendelian Inheritance in Man (OMIM) database, the DrugBank database, the GeneCards database and the DisGeNet database. Network topology analysis, GO and KEGG pathway enrichment analyses were performed to select the key targets and pathways. Molecular docking was performed to demonstrate the binding capacity of the key compounds to the predicted targets. Furthermore, RAW264.7 cells stimulated by lipopolysaccharides (LPS) were treated with three effective compounds of hawthorn leaves to assess reliability of prediction. Quercetin, isorhamnetin and kaempferol were main active compounds in hawthorn leaves. Forty four candidate therapeutic targets were identified to be involved in protection of hawthorn leaves against CHD. Additionally, the effective compounds of it had good binding affinities to PTGS2, EGFR, and MMP2. Enrichment analyses suggested that immune inflammation related biological processes and pathways were possibly the potential mechanism. Besides, we found that three predicted effective compounds of hawthorn leaves decreased protein expression of PTGS2, MMP2, MMP9, IL6, IL1B, TNFα and inhibited activation of macrophage. In summary, the present study demonstrates that quercetin, kaempferol and isorhamnetin are proved to be the main effective compounds of hawthorn leaves in treatment of CHD, possibly by suppressing expression of PTGS2, MMP2, MMP9, inflammatory cytokines and macrophages viability. This study provides a new understanding of the active components and mechanisms of hawthorn leaves treating CHD from the perspective of network pharmacology.

SenseNet, a tool for analysis of protein structure networks obtained from molecular dynamics simulations

Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet (“Structure ENSEmble NETworks”) framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but by contrast benefits from its independence from NMR structures. Our results are complementary to experimental data and the consensus of previous predictions, demonstrating the potential of our new analysis tool SenseNet. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, allowing for ease of future application and contributing to a system of compatible tools bridging the fields of system and structural biology.

Computational methods and theory for ion channel research

Ion channels are fundamental biological devices that act as gates in order to ensure selective ion transport across cellular membranes; their operation constitutes the molecular mechanism through which basic biological functions, such as nerve signal transmission and muscle contraction, are carried out. Here, we review recent results in the field of computational research on ion channels, covering theoretical advances, state-of-the-art simulation approaches, and frontline modeling techniques. We also report on few selected applications of continuum and atomistic methods to characterize the mechanisms of permeation, selectivity, and gating in biological and model channels.

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.

Extracting Dynamical Correlations and Identifying Key Residues for Allosteric Communication in Proteins by correlationplus

Extracting dynamical pairwise correlations and identifying key residues from large molecular dynamics trajectories or normal-mode analysis of coarse-grained models are important for explaining various processes like ligand binding, mutational effects, and long-distance interactions. Efficient and flexible tools to perform this task can provide new insights about residues involved in allosteric regulation and protein function. In addition, combining and comparing dynamical coupling information with sequence coevolution data can help to understand better protein function. To this aim, we developed a Python package called correlationplus to calculate, visualize, and analyze pairwise correlations. In this way, the package aids to identify key residues and interactions in proteins. The source code of correlationplus is available under LGPL version 3 at https://github.com/tekpinar/correlationplus. The current version of the package (0.2.0) can be installed with common installation methods like conda or pip in addition to source code installation. Moreover, docker images are also available for usage of the code without installation.

Structural probing of HapR to identify potent phytochemicals to control Vibrio cholera through integrated computational approaches

Cholera is a severe small intestine bacterial disease caused by consumption of water and food contaminated with Vibrio cholera. The disease causes watery diarrhea leading to severe dehydration and even death if left untreated. In the past few decades, V. cholerae has emerged as multidrug-resistant enteric pathogen due to its rapid ability to adapt in detrimental environmental conditions. This research study aimed to design inhibitors of a master virulence gene expression regulator, HapR. HapR is critical in regulating the expression of several set of V. cholera virulence genes, quorum-sensing circuits and biofilm formation. A blind docking strategy was employed to infer the natural binding tendency of diverse phytochemicals extracted from medicinal plants by exposing the whole HapR structure to the screening library. Scoring function criteria was applied to prioritize molecules with strong binding affinity (binding energy < -11 kcal/mol) and as such two compounds: Strychnogucine A and Galluflavanone were filtered. Both the compounds were found favourably binding to the conserved dimerization interface of HapR. One rare binding conformation of Strychnogucine A was noticed docked at the elongated cavity formed by α1, α4 and α6 (binding energy of -12.5 kcal/mol). The binding stability of both top leads at dimer interface and elongated cavity was further estimated using long run of molecular dynamics simulations, followed by MMGB/PBSA binding free energy calculations to define the dominance of different binding energies. In a nutshell, this study presents computational evidence on antibacterial potential of phytochemicals capable of directly targeting bacterial virulence and highlight their great capacity to be utilized in the future experimental studies to stop the evolution of antibiotic resistance evolution.

Conformational Characterization of the Co-Activator Binding Site Revealed the Mechanism to Achieve the Bioactive State of FXR

FXR bioactive states are responsible for the regulation of metabolic pathways, which are modulated by agonists and co-activators. The synergy between agonist binding and ‘co-activator’ recruitment is highly conformationally driven. The characterization of conformational dynamics is essential for mechanistic and therapeutic understanding. To shed light on the conformational ensembles, dynamics, and structural determinants that govern the activation process of FXR, molecular dynamic (MD) simulation is employed. Atomic insights into the ligand binding domain (LBD) of FXR revealed significant differences in inter/intra molecular bonding patterns, leading to structural anomalies in different systems of FXR. The sole presence of an agonist or ‘co-activator’ fails to achieve the essential bioactive conformation of FXR. However, the presence of both establishes the bioactive conformation of FXR as they modulate the internal wiring of key residues that coordinate allosteric structural transitions and their activity. We provide a precise description of critical residue positioning during conformational changes that elucidate the synergy between its binding partners to achieve an FXR activation state. Our study offers insights into the associated modulation occurring in FXR at bound and unbound forms. Thereafter, we also identified hot-spots that are critical to arrest the activation mechanism of FXR that would be helpful for the rational design of its agonists.

pyProGA-A PyMOL plugin for protein residue network analysis

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.

Is Disrupted Nucleotide-Substrate Cooperativity a Common Trait for Cushing's Syndrome Driving Mutations of Protein Kinase A?

Somatic mutations in the PRKACA gene encoding the catalytic α subunit of protein kinase A (PKA-C) are responsible for cortisol-producing adrenocortical adenomas. These benign neoplasms contribute to the development of Cushing's syndrome. The majority of these mutations occur at the interface between the two lobes of PKA-C and interfere with the enzyme's ability to recognize substrates and regulatory (R) subunits, leading to aberrant phosphorylation patterns and activation. Rarely, patients with similar phenotypes carry an allosteric mutation, E31V, located at the C-terminal end of the αA-helix and adjacent to the αC-helix, but structurally distinct from the PKA-C/R subunit interface mutations. Using a combination of solution NMR, thermodynamics, kinetic assays, and molecular dynamics simulations, we show that the E31V allosteric mutation disrupts central communication nodes between the N- and C- lobes of the enzyme as well as nucleotide-substrate binding cooperativity, a hallmark for kinases' substrate fidelity and regulation. For both orthosteric (L205R and W196R) and allosteric (E31V) Cushing's syndrome mutants, the loss of binding cooperativity is proportional to the density of the intramolecular allosteric network. This structure-activity relationship suggests a possible common mechanism for Cushing's syndrome driving mutations in which decreased nucleotide/substrate binding cooperativity is linked to loss in substrate fidelity and dysfunctional regulation.

Dynamic residue interaction network analysis of the oseltamivir binding site of N1 neuraminidase and its H274Y mutation site conferring drug resistance in influenza A virus

Background

Oseltamivir (OTV)-resistant influenza virus exhibits His-to-Tyr mutation at residue 274 (H274Y) in N1 neuraminidase (NA). However, the molecular mechanisms by which the H274Y mutation in NA reduces its binding affinity to OTV have not been fully elucidated.

Methods

In this study, we used dynamic residue interaction network (dRIN) analysis based on molecular dynamics simulation to investigate the correlation between the OTV binding site of NA and its H274Y mutation site.

Results

dRIN analysis revealed that the OTV binding site and H274Y mutation site of NA interact via the three interface residues connecting them. H274Y mutation significantly enhanced the interaction between residue 274 and the three interface residues in NA, thereby significantly decreasing the interaction between OTV and its surrounding loop 150 residues. Thus, we concluded that such changes in residue interactions could reduce the binding affinity of OTV to NA, resulting in drug resistant influenza viruses. Using dRIN analysis, we succeeded in understanding the characteristic changes in residue interactions due to H274Y mutation, which can elucidate the molecular mechanism of reduction in OTV binding affinity to influenza NA. Finally, the dRIN analysis used in this study can be widely applied to various systems such as individual proteins, protein-ligand complexes, and protein-protein complexes, to characterize the dynamic aspects of the interactions.

Activation mechanism of Drosophila cryptochrome through an allosteric switch

Cryptochromes are signaling proteins activated by photoexcitation of the flavin adenine dinucleotide (FAD) cofactor. Although extensive research has been performed, the mechanism for this allosteric process is still unknown. We constructed three computational models, corresponding to different redox states of the FAD cofactor in Drosophila cryptochrome (dCRY). Analyses of the dynamics trajectories reveal that the activation process occurs in the semiquinone state FAD^-●, resulting from excited-state electron transfer. The Arg³⁸¹-Asp⁴¹⁰ salt bridge acts as an allosteric switch, regulated by the change in the redox state of FAD. In turn, Asp⁴¹⁰ forms new hydrogen bonds, connecting allosteric networks of the amino-terminal and carboxyl-terminal domains initially separated in the resting state. The expansion to a global dynamic network leads to enhanced protein fluctuations, an increase in the radius of gyration, and the expulsion of the carboxyl-terminal tail. These structural features are in accord with mutations and spectroscopic experiments.

Weakening of interaction networks with aging in tip-link protein induces hearing loss

Age-related hearing loss (ARHL) is a common condition in humans marking the gradual decrease in hearing with age. Perturbations in the tip-link protein cadherin-23 that absorbs the mechanical tension from sound and maintains the integrity of hearing is associated with ARHL. Here, in search of molecular origins for ARHL, we dissect the conformational behavior of cadherin-23 along with the mutant S47P that progresses the hearing loss drastically. Using an array of experimental and computational approaches, we highlight a lower thermodynamic stability, significant weakening in the hydrogen-bond network and inter-residue correlations among β-strands, due to the S47P mutation. The loss in correlated motions translates to not only a remarkable two orders of magnitude slower folding in the mutant but also to a proportionately complex unfolding mechanism. We thus propose that loss in correlated motions within cadherin-23 with aging may trigger ARHL, a molecular feature that likely holds true for other disease-mutations in β-strand-rich proteins.

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.

PyVibMS: a PyMOL plugin for visualizing vibrations in molecules and solids

Visualizing vibrational motions calculated with different ab initio packages requires dedicated post-processing tools. Here, we present a PyMOL plugin called PyVibMS for visualizing the vibrational motions for both molecular and solid systems calculated by mainstream quantum chemical computer programs including Gaussian, Q-Chem, VASP, and CRYSTAL. Benefiting from the continuing development of the PyMOL platform, PyVibMS provides powerful functionalities and user-friendly interface. PyVibMS was written in Python and its open-source nature makes it flexible and sustainable. As an example, the motions of the Konkoli-Cremer local vibrational modes are shown in this work for the first time. PyVibMS is freely available at https://github.com/smutao/PyVibMS . Graphical abstract In this work, a PyMOL plugin named PyVibMS is developed to visualize molecular and lattice vibrations.

NAPS update: network analysis of molecular dynamics data and protein-nucleic acid complexes

Network theory is now a method of choice to gain insights in understanding protein structure, folding and function. In combination with molecular dynamics (MD) simulations, it is an invaluable tool with widespread applications such as analyzing subtle conformational changes and flexibility regions in proteins, dynamic correlation analysis across distant regions for allosteric communications, in drug design to reveal alternative binding pockets for drugs, etc. Updated version of NAPS now facilitates network analysis of the complete repertoire of these biomolecules, i.e., proteins, protein–protein/nucleic acid complexes, MD trajectories, and RNA. Various options provided for analysis of MD trajectories include individual network construction and analysis of intermediate time-steps, comparative analysis of these networks, construction and analysis of average network of the ensemble of trajectories and dynamic cross-correlations. For protein–nucleic acid complexes, networks of the whole complex as well as that of the interface can be constructed and analyzed. For analysis of proteins, protein–protein complexes and MD trajectories, network construction based on inter-residue interaction energies with realistic edge-weights obtained from standard force fields is provided to capture the atomistic details. Updated version of NAPS also provides improved visualization features, interactive plots and bulk execution. URL: http://bioinf.iiit.ac.in/NAPS/

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledge-based protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95- PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.Communicated by Ramaswamy H. Sarma.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

RIP-MD: a tool to study residue interaction networks in protein molecular dynamics

Protein structure is not static; residues undergo conformational rearrangements and, in doing so, create, stabilize or break non-covalent interactions. Molecular dynamics (MD) is a technique used to simulate these movements with atomic resolution. However, given the data-intensive nature of the technique, gathering relevant information from MD simulations is a complex and time consuming process requiring several computational tools to perform these analyses. Among different approaches, the study of residue interaction networks (RINs) has proven to facilitate the study of protein structures. In a RIN, nodes represent amino-acid residues and the connections between them depict non-covalent interactions. Here, we describe residue interaction networks in protein molecular dynamics (RIP-MD), a visual molecular dynamics (VMD) plugin to facilitate the study of RINs using trajectories obtained from MD simulations of proteins. Our software generates RINs from MD trajectory files. The non-covalent interactions defined by RIP-MD include H-bonds, salt bridges, VdWs, cation-π, π–π, Arginine–Arginine, and Coulomb interactions. In addition, RIP-MD also computes interactions based on distances between C_αs and disulfide bridges. The results of the analysis are shown in an user friendly interface. Moreover, the user can take advantage of the VMD visualization capacities, whereby through some effortless steps, it is possible to select and visualize interactions described for a single, several or all residues in a MD trajectory. Network and descriptive table files are also generated, allowing their further study in other specialized platforms. Our method was written in python in a parallelized fashion. This characteristic allows the analysis of large systems impossible to handle otherwise. RIP-MD is available at http://www.dlab.cl/ripmd.

HUWE1 E3 ligase promotes PINK1/PARKIN-independent mitophagy by regulating AMBRA1 activation via IKKα

The selective removal of undesired or damaged mitochondria by autophagy, known as mitophagy, is crucial for cellular homoeostasis, and prevents tumour diffusion, neurodegeneration and ageing. The pro-autophagic molecule AMBRA1 (autophagy/beclin-1 regulator-1) has been defined as a novel regulator of mitophagy in both PINK1/PARKIN-dependent and -independent systems. Here, we identified the E3 ubiquitin ligase HUWE1 as a key inducing factor in AMBRA1-mediated mitophagy, a process that takes place independently of the main mitophagy receptors. Furthermore, we show that mitophagy function of AMBRA1 is post-translationally controlled, upon HUWE1 activity, by a positive phosphorylation on its serine 1014. This modification is mediated by the IKKα kinase and induces structural changes in AMBRA1, thus promoting its interaction with LC3/GABARAP (mATG8) proteins and its mitophagic activity. Altogether, these results demonstrate that AMBRA1 regulates mitophagy through a novel pathway, in which HUWE1 and IKKα are key factors, shedding new lights on the regulation of mitochondrial quality control and homoeostasis in mammalian cells.

Mitophagy is crucial for mitochondrial quality control and maintenance of cellular homeostasis. Here the authors identify an E3 ubiquitin ligase, HUWE1, that collaborates with LC3-interacting protein AMBRA1 to induce mitochondrial clearance.

Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO® ChromaTOF® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO® ChromaTOF® software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold.

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.

Allosteric modulation of cardiac myosin dynamics by omecamtiv mecarbil

New promising avenues for the pharmacological treatment of skeletal and heart muscle diseases rely on direct sarcomeric modulators, which are molecules that can directly bind to sarcomeric proteins and either inhibit or enhance their activity. A recent breakthrough has been the discovery of the myosin activator omecamtiv mecarbil (OM), which has been shown to increase the power output of the cardiac muscle and is currently in clinical trials for the treatment of heart failure. While the overall effect of OM on the mechano-chemical cycle of myosin is to increase the fraction of myosin molecules in the sarcomere that are strongly bound to actin, the molecular basis of its action is still not completely clear. We present here a Molecular Dynamics study of the motor domain of human cardiac myosin bound to OM, where the effects of the drug on the dynamical properties of the protein are investigated for the first time with atomistic resolution. We found that OM has a double effect on myosin dynamics, inducing a) an increased coupling of the motions of the converter and lever arm subdomains to the rest of the protein and b) a rewiring of the network of dynamic correlations, which produces preferential communication pathways between the OM binding site and distant functional regions. The location of the residues responsible for these effects suggests possible strategies for the future development of improved drugs and the targeting of specific cardiomyopathy-related mutations.

An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass

Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.

DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions

Binding-induced conformational changes of a protein at regions distant from the binding site may play crucial roles in protein function and regulation. The p53 tumour suppressor is an example of such an allosterically regulated protein. Little is known, however, about how DNA binding can affect distal sites for transcription factors. Furthermore, the molecular details of how a local perturbation is transmitted through a protein structure are generally elusive and occur on timescales hard to explore by simulations. Thus, we employed state-of-the-art enhanced sampling atomistic simulations to unveil DNA-induced effects on p53 structure and dynamics that modulate the recruitment of cofactors and the impact of phosphorylation at Ser215. We show that DNA interaction promotes a conformational change in a region 3 nm away from the DNA binding site. Specifically, binding to DNA increases the population of an occluded minor state at this distal site by more than 4-fold, whereas phosphorylation traps the protein in its major state. In the minor conformation, the interface of p53 that binds biological partners related to p53 transcription-independent functions is not accessible. Significantly, our study reveals a mechanism of DNA-mediated protection of p53 from interactions with partners involved in the p53 transcription-independent signalling. This also suggests that conformational dynamics is tightly related to p53 signalling.

Biophysical and computational methods to analyze amino acid interaction networks in proteins

Globular proteins are held together by interacting networks of amino acid residues. A number of different structural and computational methods have been developed to interrogate these amino acid networks. In this review, we describe some of these methods, including analyses of X-ray crystallographic data and structures, computer simulations, NMR data, and covariation among protein sequences, and indicate the critical insights that such methods provide into protein function. This information can be leveraged towards the design of new allosteric drugs, and the engineering of new protein function and protein regulation strategies.

NAPS: Network Analysis of Protein Structures

Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue-residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein-protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/.

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

The dynamics of the β-propeller domain in Kelch protein KLHL40 changes upon nemaline myopathy-associated mutation

The nemaline myopathy-associated E528K mutation in the KLHL40 alters the communication between the Kelch propeller blades.

(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.

CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers

It can be informative to view biological data, e.g., protein-protein interactions within a large complex, in a network representation coupled with three-dimensional structural visualizations of individual molecular entities. CyToStruct, introduced here, provides a transparent interface between the Cytoscape platform for network analysis and molecular viewers, including PyMOL, UCSF Chimera, VMD, and Jmol. CyToStruct launches and passes scripts to molecular viewers from the network's edges and nodes. We provide demonstrations to analyze interactions among subunits in large protein/RNA/DNA complexes, and similarities among proteins. CyToStruct enriches the network tools of Cytoscape by adding a layer of structural analysis, offering all capabilities implemented in molecular viewers. CyToStruct is available at https://bitbucket.org/sergeyn/cytostruct/wiki/Home and in the Cytoscape App Store. Given the coordinates of a molecular complex, our web server (http://trachel-srv.cs.haifa.ac.il/rachel/ppi/) automatically generates all files needed to visualize the complex as a Cytoscape network with CyToStruct bridging to PyMOL, UCSF Chimera, VMD, and Jmol.

Analysis of the structure and dynamics of human serum albumin

Human serum albumin (HSA) is a biologically relevant protein that binds a variety of drugs and other small molecules. No less than 50 structures are deposited in the RCSB Protein Data Bank (PDB). Based on these structures, we first performed a clustering analysis. Despite the diversity of ligands, only two well defined conformations are detected, with a deviation of 0.46 nm between the average structures of the two clusters, while deviations within each cluster are smaller than 0.08 nm. Those two conformations are representative of the apoprotein and the HSA-myristate complex already identified in previous literature. Considering the structures within each cluster as a representative sample of the dynamical states of the corresponding conformation, we scrutinize the structural and dynamical differences between both conformations. Analysis of the fluctuations within each cluster set reveals that domain II is the most rigid one and better matches both structures. Then, taking this domain as reference, we show that the structural difference between both conformations can be expressed in terms of twist and hinge motions of domains I and III, respectively. We also characterize the dynamical difference between conformations by computing correlations and principal components for each set of dynamical states. The two conformations display different collective motions. The results are compared with those obtained from the trajectories of short molecular dynamics simulations, giving consistent outcomes. Let us remark that, beyond the relevance of the results for the structural and dynamical characterization of HAS conformations, the present methodology could be extended to other proteins in the PDB archive.

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.

Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs

Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non-activated STAT5 proteins. Our theoretical prediction based on results obtained with MONETA was validated for KIT by in vitro experiments. MONETA is a versatile analytical and visualization tool entirely devoted to the understanding of the functioning/malfunctioning of allosteric regulation in proteins - a crucial basis to guide the discovery of next-generation allosteric drugs.

Molecular dynamics study of HIV-1 RT-DNA-nevirapine complexes explains NNRTI inhibition and resistance by connection mutations

HIV-1 reverse transcriptase (RT) is a multifunctional enzyme that is targeted by nucleoside analogs (NRTIs) and non-nucleoside RT inhibitors (NNRTIs). NNRTIs are allosteric inhibitors of RT, and constitute an integral part of several highly active antiretroviral therapy regimens. Under selective pressure, HIV-1 acquires resistance against NNRTIs primarily by selecting mutations around the NNRTI pocket. Complete RT sequencing of clinical isolates revealed that spatially distal mutations arising in connection and the RNase H domain also confer NNRTI resistance and contribute to NRTI resistance. However, the precise structural mechanism by which the connection domain mutations confer NNRTI resistance is poorly understood. We performed 50-ns molecular dynamics (MD) simulations, followed by essential dynamics, free-energy landscape analyses, and network analyses of RT-DNA, RT-DNA-nevirapine (NVP), and N348I/T369I mutant RT-DNA-NVP complexes. MD simulation studies revealed altered global motions and restricted conformational landscape of RT upon NVP binding. Analysis of protein structure network parameters demonstrated a dissortative hub pattern in the RT-DNA complex and an assortative hub pattern in the RT-DNA-NVP complex suggesting enhanced rigidity of RT upon NVP binding. The connection subdomain mutations N348I/T369I did not induce any significant structural change; rather, these mutations modulate the conformational dynamics and alter the long-range allosteric communication network between the connection subdomain and NNRTI pocket. Insights from the present study provide a structural basis for the biochemical and clinical findings on drug resistance caused by the connection and RNase H mutations.

An automated approach to network features of protein structure ensembles

Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

The construction of an amino acid network for understanding protein structure and function

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.