A network representation of protein structures: implications for protein stability

NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues

Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.

Communities in the iron superoxide dismutase amino acid network

Amino acid networks (AANs) analysis is a new way to reveal the relationship between protein structure and function. We constructed six different types of AANs based on iron superoxide dismutase (Fe-SOD) three-dimensional structure information. These Fe-SOD AANs have clear community structures when they were modularized by different methods. Especially, detected communities are related to Fe-SOD secondary structures. Regular structures show better correlations with detected communities than irregular structures, and loops weaken these correlations, which suggest that secondary structure is the unit element in Fe-SOD folding process. In addition, a comparative analysis of mesophilic and thermophilic Fe-SOD AANs' communities revealed that thermostable Fe-SOD AANs had more highly associated community structures than mesophilic one. Thermophilic Fe-SOD AANs also had more high similarity between communities and secondary structures than mesophilic Fe-SOD AANs. The communities in Fe-SOD AANs show that dense interactions in modules can help to stabilize thermophilic Fe-SOD.

Small-world networks of residue interactions in the Abl kinase complexes with cancer drugs: topology of allosteric communication pathways can determine drug resistance effects

Computational modelling of efficiency and robustness of the residue interaction networks and allosteric pathways in kinase structures can characterize protein kinase sensitivity to drug binding and drug resistance effects.

Structure-based network analysis of activation mechanisms in the ErbB family of receptor tyrosine kinases: the regulatory spine residues are global mediators of structural stability and allosteric interactions

The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced “superacceptor” activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues.

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.

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.

Functional versatility and structural adaptability of the Hsp90 chaperones are regulated by allosteric interactions that allow for diverse functions including modulation of ATP hydrolysis and binding with cochaperones and client proteins. By integrating molecular simulations and network-based approaches we have characterized conformational dynamics and allosteric interactions in different functional forms of Hsp90. The network centrality analysis and structural mapping of allosteric communications have revealed a small-world organization of the interaction network that is mediated by functionally important residues of the Hsp90 activity. We have found that effective allosteric communications in the Hsp90 chaperone may be provided by structurally stable residues that exhibit high centrality properties. Nucleotide-specific rewiring of the network topology and assortative organization of functional residues may protect the active form of the chaperone from random perturbations and detrimental mutations. These results have confirmed that allosteric interactions in the Hsp90 chaperone may be determined by a small-world network of functional residues that can regulate the network efficiency and resiliency by modulating the statistical ensemble of communication pathways in response to functional requirements of the ATPase cycle.

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

BACKGROUND

The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes.

RESULTS

Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/.

CONCLUSIONS

NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

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.

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/.

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.

Computational study on the inhibitor binding mode and allosteric regulation mechanism in hepatitis C virus NS3/4A protein

HCV NS3/4A protein is an attractive therapeutic target responsible for harboring serine protease and RNA helicase activities during the viral replication. Small molecules binding at the interface between the protease and helicase domains can stabilize the closed conformation of the protein and thus block the catalytic function of HCV NS3/4A protein via an allosteric regulation mechanism. But the detailed mechanism remains elusive. Here, we aimed to provide some insight into the inhibitor binding mode and allosteric regulation mechanism of HCV NS3/4A protein by using computational methods. Four simulation systems were investigated. They include: apo state of HCV NS3/4A protein, HCV NS3/4A protein in complex with an allosteric inhibitor and the truncated form of the above two systems. The molecular dynamics simulation results indicate HCV NS3/4A protein in complex with the allosteric inhibitor 4VA adopts a closed conformation (inactive state), while the truncated apo protein adopts an open conformation (active state). Further residue interaction network analysis suggests the communication of the domain-domain interface play an important role in the transition from closed to open conformation of HCV NS3/4A protein. However, the inhibitor stabilizes the closed conformation through interaction with several key residues from both the protease and helicase domains, including His57, Asp79, Asp81, Asp168, Met485, Cys525 and Asp527, which blocks the information communication between the functional domains interface. Finally, a dynamic model about the allosteric regulation and conformational changes of HCV NS3/4A protein was proposed and could provide fundamental insights into the allosteric mechanism of HCV NS3/4A protein function regulation and design of new potent inhibitors.

A ribokinase family conserved monovalent cation binding site enhances the MgATP-induced inhibition in E. coli phosphofructokinase-2

The presence of a regulatory site for monovalent cations that affects the conformation of the MgATP-binding pocket leading to enzyme activation has been demonstrated for ribokinases. This site is selective toward the ionic radius of the monovalent cation, accepting those larger than Na(+). Phosphofructokinase-2 (Pfk-2) from Escherichia coli is homologous to ribokinase, but unlike other ribokinase family members, presents an additional site for the nucleotide that negatively regulates its enzymatic activity. In this work, we show the effect of monovalent cations on the kinetic parameters of Pfk-2 together with its three-dimensional structure determined by x-ray diffraction in the presence of K(+) or Cs(+). Kinetic characterization of the enzyme shows that K(+) and Na(+) alter neither the kcat nor the KM values for fructose-6-P or MgATP. However, the presence of K(+) (but not Na(+)) enhances the allosteric inhibition induced by MgATP. Moreover, binding experiments show that K(+) (but not Na(+)) increases the affinity of MgATP in a saturable fashion. In agreement with the biochemical data, the crystal structure of Pfk-2 obtained in the presence of MgATP shows a cation-binding site at the conserved position predicted for the ribokinase family of proteins. This site is adjacent to the MgATP allosteric binding site and is only observed in the presence of Cs(+) or K(+). These results indicate that binding of the monovalent metal ions indirectly influences the allosteric site of Pfk-2 by increasing its affinity for MgATP with no alteration in the conformation of residues present at the catalytic site.

Residue interaction network analysis of Dronpa and a DNA clamp

Topology is an essential aspect of protein structure. The network paradigm is increasingly used to describe the topology and dynamics of proteins. In this paper, the effect of topology on residue interaction network was investigated for two different proteins: Dronpa and a DNA clamp, which have cylindrical and toroidal topologies, respectively. Network metrics including characteristic path lengths, clustering coefficients, and diameters were calculated to investigate their global topology parameters such as small-world properties and packing density. Measures of centrality including betweenness, closeness, and residue centrality were computed to predict residues critical to function. Additionally, the detailed topology of the hydrophobic pocket in Dronpa, and communication pathways across the interface in the DNA clamp, were investigated using the network. The results are presented and discussed with regard to existing residue interaction network properties of globular proteins and elastic network models on Dronpa and the DNA clamp. The topological principle underlying residue interaction networks provided insight into the architectural organization of proteins.

Ranking the quality of protein structure models using sidechain based network properties

Determining the correct structure of a protein given its sequence still remains an arduous task with many researchers working towards this goal. Most structure prediction methodologies result in the generation of a large number of probable candidates with the final challenge being to select the best amongst these. In this work, we have used Protein Structure Networks of native and modeled proteins in combination with Support Vector Machines to estimate the quality of a protein structure model and finally to provide ranks for these models. Model ranking is performed using regression analysis and helps in model selection from a group of many similar and good quality structures. Our results show that structures with a rank greater than 16 exhibit native protein-like properties while those below 10 are non-native like. The tool is also made available as a web-server ( http://vishgraph.mbu.iisc.ernet.in/GraProStr/native_non_native_ranking.html), where, 5 modelled structures can be evaluated at a given time.

Allosteric regulation of the Hsp90 dynamics and stability by client recruiter cochaperones: protein structure network modeling

The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect molecular mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the experiments, we have determined that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network analysis of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equilibrium. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network analysis has recapitulated a broad range of structural and mutagenesis experiments, particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be determined by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins.

Mapping the intramolecular signal transduction of G-protein coupled receptors

G-protein coupled receptors (GPCRs), a major gatekeeper of extracellular signals on plasma membrane, are unarguably one of the most important therapeutic targets. Given the recent discoveries of allosteric modulations, an allosteric wiring diagram of intramolecular signal transductions would be of great use to glean the mechanism of receptor regulation. Here, by evaluating betweenness centrality (CB ) of each residue, we calculate maps of information flow in GPCRs and identify key residues for signal transductions and their pathways. Compared with preexisting approaches, the allosteric hotspots that our CB -based analysis detects for A2 A adenosine receptor (A2 A AR) and bovine rhodopsin are better correlated with biochemical data. In particular, our analysis outperforms other methods in locating the rotameric microswitches, which are generally deemed critical for mediating orthosteric signaling in class A GPCRs. For A2 A AR, the inter-residue cross-correlation map, calculated using equilibrium structural ensemble from molecular dynamics simulations, reveals that strong signals of long-range transmembrane communications exist only in the agonist-bound state. A seemingly subtle variation in structure, found in different GPCR subtypes or imparted by agonist bindings or a point mutation at an allosteric site, can lead to a drastic difference in the map of signaling pathways and protein activity. The signaling map of GPCRs provides valuable insights into allosteric modulations as well as reliable identifications of orthosteric signaling pathways.

Differential modulation of functional dynamics and allosteric interactions in the Hsp90-cochaperone complexes with p23 and Aha1: a computational study

Allosteric interactions of the molecular chaperone Hsp90 with a large cohort of cochaperones and client proteins allow for molecular communication and event coupling in signal transduction networks. The integration of cochaperones into the Hsp90 system is driven by the regulatory mechanisms that modulate the progression of the ATPase cycle and control the recruitment of the Hsp90 clientele. In this work, we report the results of computational modeling of allosteric regulation in the Hsp90 complexes with the cochaperones p23 and Aha1. By integrating protein docking, biophysical simulations, modeling of allosteric communications, protein structure network analysis and the energy landscape theory we have investigated dynamics and stability of the Hsp90-p23 and Hsp90-Aha1 interactions in direct comparison with the extensive body of structural and functional experiments. The results have revealed that functional dynamics and allosteric interactions of Hsp90 can be selectively modulated by these cochaperones via specific targeting of the regulatory hinge regions that could restrict collective motions and stabilize specific chaperone conformations. The protein structure network parameters have quantified the effects of cochaperones on conformational stability of the Hsp90 complexes and identified dynamically stable communities of residues that can contribute to the strengthening of allosteric interactions. According to our results, p23-mediated changes in the Hsp90 interactions may provide "molecular brakes" that could slow down an efficient transmission of the inter-domain allosteric signals, consistent with the functional role of p23 in partially inhibiting the ATPase cycle. Unlike p23, Aha1-mediated acceleration of the Hsp90-ATPase cycle may be achieved via modulation of the equilibrium motions that facilitate allosteric changes favoring a closed dimerized form of Hsp90. The results of our study have shown that Aha1 and p23 can modulate the Hsp90-ATPase activity and direct the chaperone cycle by exerting the precise control over structural stability, global movements and allosteric communications in Hsp90.

Computational study on the drug resistance mechanism against HCV NS3/4A protease inhibitors vaniprevir and MK-5172 by the combination use of molecular dynamics simulation, residue interaction network, and substrate envelope analysis

Hepatitis C virus (HCV) NS3/4A protease is an important and attractive target for anti-HCV drug development and discovery. Vaniprevir (phase III clinical trials) and MK-5172 (phase II clinical trials) are two potent antiviral compounds that target NS3/4A protease. However, the emergence of resistance to these two inhibitors reduced the effectiveness of vaniprevir and MK-5172 against viral replication. Among the drug resistance mutations, three single-site mutations at residues Arg155, Ala156, and Asp168 in NS3/4A protease are especially important due to their resistance to nearly all inhibitors in clinical development. A detailed understanding of drug resistance mechanism to vaniprevir and MK-5172 is therefore very crucial for the design of novel potent agents targeting viral variants. In this work, molecular dynamics (MD) simulation, binding free energy calculation, free energy decomposition, residue interaction network (RIN), and substrate envelope analysis were used to study the detailed drug resistance mechanism of the three mutants R155K, A156T, and D168A to vaniprevir and MK-5172. MD simulation was used to investigate the binding mode for these two inhibitors to wild-type and resistant mutants of HCV NS3/4A protease. Binding free energy calculation and free energy decomposition analysis reveal that drug resistance mutations reduced the interactions between the active site residues and substituent in the P2 to P4 linker of vaniprevir and MK-5172. Furthermore, RIN and substrate envelope analysis indicate that the studied mutations of the residues are located outside the substrate (4B5A) binding site and selectively decrease the affinity of inhibitors but not the activity of the enzyme and consequently help NS3/4A protease escape from the effect of the inhibitors without influencing the affinity of substrate binding. These findings can provide useful information for understanding the drug resistance mechanism against vaniprevir and MK-5172. The results can also provide some potential clues for further design of novel inhibitors that are less susceptible to drug resistance.

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.

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.

The effect of edge definition of complex networks on protein structure identification

The main objective of this study is to explore the contribution of complex network together with its different definitions of vertexes and edges to describe the structure of proteins. Protein folds into a specific conformation for its function depending on interactions between residues. Consequently, in many studies, a protein structure was treated as a complex system comprised of individual components residues, and edges were interactions between residues. What is the proper time for representing a protein structure as a network? To confirm the effect of different definitions of vertexes and edges in constructing the amino acid interaction networks, protein domains and the structural unit of proteins were described using this method. The identification performance of 2847 proteins with domain/domains proved that the structure of proteins was described well when R_Cα was around 5.0–7.5 Å, and the optimal cutoff value for constructing the protein structure networks was 5.0 Å (C_α-C_α distances) while the ideal community division method was community structure detection based on edge betweenness in this study.

Small world network strategies for studying protein structures and binding

Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

Variant of the Thermomyces lanuginosus lipase with improved kinetic stability: a candidate for enzyme replacement therapy

Lipases with high kinetic stability and enzymatic efficiency in the human gastro-intestinal tract may help against exocrine pancreatic insufficiency. Here we mimic gastric conditions to study how bile salts and pH affect the stability and activity of Thermomyces lanuginosus lipase (TlL) and its stabler variant StL using spectroscopy, calorimetry and gel electrophoresis. Both enzymes resist trypsin digestion with and without bile salts. Bile salts activate native TlL and StL equally well, bind weakly to denatured TlL and StL at lower pH and precipitate native TlL and StL at pH 4. StL refolds more efficiently than TlL from gastric pH in bile salts, regaining activity when refolding from pH as low as 1.8 and above while TlL cannot go below pH 2.6. StL also unfolds 10-40 fold more slowly in the denaturant guanidinium chloride and the anionic surfactant SDS. We ascribe StL's superior performance to general alterations in its electrostatic potential which makes it more acid-resistant. These superior properties make StL a good candidate for pancreatic enzyme replacement therapy.

A modified amino acid network model contains similar and dissimilar weight

For a more detailed description of the interaction between residues, this paper proposes an amino acid network model, which contains two types of weight—similar weight and dissimilar weight. The weight of the link is based on a self-consistent statistical contact potential between different types of amino acids. In this model, we can get a more reasonable representation of the distance between residues. Furthermore, with the network parameter, average shortest path length, we can get a more accurate reflection of the molecular size. This amino acid network is a “small-world” network, and the network parameter is sensitive to the conformation change of protein. For some disease-related proteins, the highly central residues of the amino acid network are highly correlated with the hot spots. In the compound with the related drug, these residues either interacted directly with the drug or with the residue which is in contact with the drug.

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.

Interaction signatures stabilizing the NAD(P)-binding Rossmann fold: a structure network approach

The fidelity of the folding pathways being encoded in the amino acid sequence is met with challenge in instances where proteins with no sequence homology, performing different functions and no apparent evolutionary linkage, adopt a similar fold. The problem stated otherwise is that a limited fold space is available to a repertoire of diverse sequences. The key question is what factors lead to the formation of a fold from diverse sequences. Here, with the NAD(P)-binding Rossmann fold domains as a case study and using the concepts of network theory, we have unveiled the consensus structural features that drive the formation of this fold. We have proposed a graph theoretic formalism to capture the structural details in terms of the conserved atomic interactions in global milieu, and hence extract the essential topological features from diverse sequences. A unified mathematical representation of the different structures together with a judicious concoction of several network parameters enabled us to probe into the structural features driving the adoption of the NAD(P)-binding Rossmann fold. The atomic interactions at key positions seem to be better conserved in proteins, as compared to the residues participating in these interactions. We propose a "spatial motif" and several "fold specific hot spots" that form the signature structural blueprints of the NAD(P)-binding Rossmann fold domain. Excellent agreement of our data with previous experimental and theoretical studies validates the robustness and validity of the approach. Additionally, comparison of our results with statistical coupling analysis (SCA) provides further support. The methodology proposed here is general and can be applied to similar problems of interest.

Intramolecular interactions stabilizing compact conformations of the intrinsically disordered kinase-inhibitor domain of Sic1: a molecular dynamics investigation

Cyclin-dependent kinase inhibitors (CKIs) are key regulatory proteins of the eukaryotic cell cycle, which modulate cyclin-dependent kinase (Cdk) activity. CKIs perform their inhibitory effect by the formation of ternary complexes with a target kinase and its cognate cyclin. These regulators generally belong to the class of intrinsically disordered proteins (IDPs), which lack a well-defined and organized three-dimensional (3D) structure in their free state, undergoing folding upon binding to specific partners. Unbound IDPs are not merely random-coil structures, but can present intrinsically folded structural units (IFSUs) and collapsed conformations. These structural features can be relevant to protein function in vivo. The yeast CKI Sic1 is a 284-amino acid IDP that binds to Cdk1 in complex with the Clb5,6 cyclins, preventing phosphorylation of G1 substrates and, therefore, entrance to the S phase. Sic1 degradation, triggered by multiple phosphorylation events, promotes cell-cycle progression. Previous experimental studies pointed out a propensity of Sic1 and its isolated domains to populate both extended and compact conformations. The present contribution provides models for compact conformations of the Sic1 kinase-inhibitory domain (KID) by all-atom molecular dynamics (MD) simulations in explicit solvent and in the absence of interactors. The results are integrated by spectroscopic and spectrometric data. Helical IFSUs are identified, along with networks of intramolecular interactions. The results identify a group of putative hub residues and networks of electrostatic interactions, which are likely to be involved in the stabilization of the globular states.

Light on the structural communication in Ras GTPases

The graph theory was combined with fluctuation dynamics to investigate the structural communication in four small G proteins, Arf1, H-Ras, RhoA, and Sec4. The topology of small GTPases is such that it requires the presence of the nucleotide to acquire a persistent structural network. The majority of communication paths involves the nucleotide and does not exist in the unbound forms. The latter are almost devoid of high-frequency paths. Thus, small Ras GTPases acquire the ability to transfer signals in the presence of nucleotide, suggesting that it modifies the intrinsic dynamics of the protein through the establishment of regions of hyperlinked nodes with high occurrence of correlated motions. The analysis of communication paths in the inactive (S(GDP)) and active (S(GTP)) states of the four G proteins strengthened the separation of the Ras-like domain into two dynamically distinct lobes, i.e. lobes 1 and 2, representing, respectively, the N-terminal and C-terminal halves of the domain. In the framework of this separation, interfunctional states and interfamily differences could be inferred. The structure network undergoes a reshaping depending on the bound nucleotide. Nucleotide-dependent divergences in structural communication reach the maximum in Arf1 and the minimum in RhoA. In Arf1, the nucleotide-dependent paths essentially express a communication between the G box 4 (G4) and distal portions of lobe 1. In the S(GDP) state, the G4 communicates with the N-term, while, in the S(GTP) state, the G4 communicates with the switch II. Clear differences could be also found between Arf1 and the other three G proteins. In Arf1, the nucleotide tends to communicate with distal portions of lobe 1, whereas in H-Ras, RhoA, and Sec4 it tends to communicate with a cluster of aromatic/hydrophobic amino acids in lobe 2. These differences may be linked, at least in part, to the divergent membrane anchoring modes that would involve the N-term for the Arf family and the C-term for the Rab/Ras/Rho families.

Scale-free behaviour of amino acid pair interactions in folded proteins

The protein structure is a cumulative result of interactions between amino acid residues interacting with each other through space and/or chemical bonds. Despite the large number of high resolution protein structures, the "protein structure code" has not been fully identified. Our manuscript presents a novel approach to protein structure analysis in order to identify rules for spatial packing of amino acid pairs in proteins. We have investigated 8706 high resolution non-redundant protein chains and quantified amino acid pair interactions in terms of solvent accessibility, spatial and sequence distance, secondary structure, and sequence length. The number of pairs found in a particular environment is stored in a cell in an 8 dimensional data tensor. When plotting the cell population against the number of cells that have the same population size, a scale free organization is found. When analyzing which amino acid paired residues contributed to the cells with a population above 50, pairs of Ala, Ile, Leu and Val dominate the results. This result is statistically highly significant. We postulate that such pairs form "structural stability points" in the protein structure. Our data shows that they are in buried α-helices or β-strands, in a spatial distance of 3.8-4.3Å and in a sequence distance >4 residues. We speculate that the scale free organization of the amino acid pair interactions in the 8D protein structure combined with the clear dominance of pairs of Ala, Ile, Leu and Val is important for understanding the very nature of the protein structure formation. Our observations suggest that protein structures should be considered as having a higher dimensional organization.

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/.

A chemical group graph representation for efficient high-throughput analysis of atomistic protein simulations

Graphs are rapidly becoming a powerful and ubiquitous tool for the analysis of protein structure and for event detection in dynamical protein systems. Despite their rise in popularity, however, the graph representations employed to date have shared certain features and parameters that have not been thoroughly investigated. Here, we examine and compare variations on the construction of graph nodes and graph edges. We propose a graph representation based on chemical groups of similar atoms within a protein rather than residues or secondary structure and find that even very simple analyses using this representation form a powerful event detection system with significant advantages over residue-based graph representations. We additionally compare graph edges based on probability of contact to graph edges based on contact strength and analyses of the entire graph structure to an alternative and more computationally tractable node-based analysis. We develop the simplest useful technique for analyzing protein simulations based on these comparisons and use it to shed light on the speed with which static protein structures adjust to a solvated environment at room temperature in simulation.

Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in protein's structural organization

Background

The three-dimensional structure of a protein can be described as a graph where nodes represent residues and the strength of non-covalent interactions between them are edges. These protein contact networks can be separated into long and short-range interactions networks depending on the positions of amino acids in primary structure. Long-range interactions play a distinct role in determining the tertiary structure of a protein while short-range interactions could largely contribute to the secondary structure formations. In addition, physico chemical properties and the linear arrangement of amino acids of the primary structure of a protein determines its three dimensional structure. Here, we present an extensive analysis of protein contact subnetworks based on the London van der Waals interactions of amino acids at different length scales. We further subdivided those networks in hydrophobic, hydrophilic and charged residues networks and have tried to correlate their influence in the overall topology and organization of a protein.

Results

The largest connected component (LCC) of long (LRN)-, short (SRN)- and all-range (ARN) networks within proteins exhibit a transition behaviour when plotted against different interaction strengths of edges among amino acid nodes. While short-range networks having chain like structures exhibit highly cooperative transition; long- and all-range networks, which are more similar to each other, have non-chain like structures and show less cooperativity. Further, the hydrophobic residues subnetworks in long- and all-range networks have similar transition behaviours with all residues all-range networks, but the hydrophilic and charged residues networks don’t. While the nature of transitions of LCC’s sizes is same in SRNs for thermophiles and mesophiles, there exists a clear difference in LRNs. The presence of larger size of interconnected long-range interactions in thermophiles than mesophiles, even at higher interaction strength between amino acids, give extra stability to the tertiary structure of the thermophiles. All the subnetworks at different length scales (ARNs, LRNs and SRNs) show assortativity mixing property of their participating amino acids. While there exists a significant higher percentage of hydrophobic subclusters over others in ARNs and LRNs; we do not find the assortative mixing behaviour of any the subclusters in SRNs. The clustering coefficient of hydrophobic subclusters in long-range network is the highest among types of subnetworks. There exist highly cliquish hydrophobic nodes followed by charged nodes in LRNs and ARNs; on the other hand, we observe the highest dominance of charged residues cliques in short-range networks. Studies on the perimeter of the cliques also show higher occurrences of hydrophobic and charged residues’ cliques.

Conclusions

The simple framework of protein contact networks and their subnetworks based on London van der Waals force is able to capture several known properties of protein structure as well as can unravel several new features. The thermophiles do not only have the higher number of long-range interactions; they also have larger cluster of connected residues at higher interaction strengths among amino acids, than their mesophilic counterparts. It can reestablish the significant role of long-range hydrophobic clusters in protein folding and stabilization; at the same time, it shed light on the higher communication ability of hydrophobic subnetworks over the others. The results give an indication of the controlling role of hydrophobic subclusters in determining protein’s folding rate. The occurrences of higher perimeters of hydrophobic and charged cliques imply the role of charged residues as well as hydrophobic residues in stabilizing the distant part of primary structure of a protein through London van der Waals interaction.

Residue centrality in alpha helical polytopic transmembrane protein structures

Transmembrane proteins serve as receptors, transporters or as enzymes. They mediate a broad range of fundamental cellular activities including signal transduction, cell trafficking and photosynthesis. In this study, we analyzed the significance of central residues in the polytopic transmembrane proteins. Each protein is represented as an undirected graph, where residues represent nodes and inter-residue interactions as the edges. Residue centrality was calculated by removing the nodes and its corresponding edges from the protein contact network. Results revealed that 80% of the predicted central residues had normalized conservation values below the mean since they were slowly evolving conserved sites. We also found that 56% of amino acids were interacting with the ligand molecules and metal ions. Predicted central residues in the polytopic transmembrane proteins were found to account for 84% of binding and active site amino acids. From mutation sensitivity analysis, it was observed that 89% of central residues had deleterious mutations whose probabilities were greater than their mean value. Interestingly, we find that z-score values of each amino acid positively correlate with the conservation scores and also with the degrees of each node. Results show that 87% of central residues are hub residues.

Computational and statistical analyses of amino acid usage and physico-chemical properties of the twelve late embryogenesis abundant protein classes

Late Embryogenesis Abundant Proteins (LEAPs) are ubiquitous proteins expected to play major roles in desiccation tolerance. Little is known about their structure - function relationships because of the scarcity of 3-D structures for LEAPs. The previous building of LEAPdb, a database dedicated to LEAPs from plants and other organisms, led to the classification of 710 LEAPs into 12 non-overlapping classes with distinct properties. Using this resource, numerous physico-chemical properties of LEAPs and amino acid usage by LEAPs have been computed and statistically analyzed, revealing distinctive features for each class. This unprecedented analysis allowed a rigorous characterization of the 12 LEAP classes, which differed also in multiple structural and physico-chemical features. Although most LEAPs can be predicted as intrinsically disordered proteins, the analysis indicates that LEAP class 7 (PF03168) and probably LEAP class 11 (PF04927) are natively folded proteins. This study thus provides a detailed description of the structural properties of this protein family opening the path toward further LEAP structure - function analysis. Finally, since each LEAP class can be clearly characterized by a unique set of physico-chemical properties, this will allow development of software to predict proteins as LEAPs.

Exploration of the relationship between topology and designability of conformations

Protein structures are evolutionarily more conserved than sequences, and sequences with very low sequence identity frequently share the same fold. This leads to the concept of protein designability. Some folds are more designable and lots of sequences can assume that fold. Elucidating the relationship between protein sequence and the three-dimensional (3D) structure that the sequence folds into is an important problem in computational structural biology. Lattice models have been utilized in numerous studies to model protein folds and predict the designability of certain folds. In this study, all possible compact conformations within a set of two-dimensional and 3D lattice spaces are explored. Complementary interaction graphs are then generated for each conformation and are described using a set of graph features. The full HP sequence space for each lattice model is generated and contact energies are calculated by threading each sequence onto all the possible conformations. Unique conformation giving minimum energy is identified for each sequence and the number of sequences folding to each conformation (designability) is obtained. Machine learning algorithms are used to predict the designability of each conformation. We find that the highly designable structures can be distinguished from other non-designable conformations based on certain graphical geometric features of the interactions. This finding confirms the fact that the topology of a conformation is an important determinant of the extent of its designability and suggests that the interactions themselves are important for determining the designability.

Computational prediction of heme-binding residues by exploiting residue interaction network

Computational identification of heme-binding residues is beneficial for predicting and designing novel heme proteins. Here we proposed a novel method for heme-binding residue prediction by exploiting topological properties of these residues in the residue interaction networks derived from three-dimensional structures. Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network. HemeNet, a support vector machine (SVM) based predictor, was developed to identify heme-binding residues by combining topological features with existing sequence and structural features. The results showed that incorporation of network-based features significantly improved the prediction performance. We also compared the residue interaction networks of heme proteins before and after heme binding and found that the topological features can well characterize the heme-binding sites of apo structures as well as those of holo structures, which led to reliable performance improvement as we applied HemeNet to predicting the binding residues of proteins in the heme-free state. HemeNet web server is freely accessible at http://mleg.cse.sc.edu/hemeNet/.