Small world network strategies for studying protein structures and binding

Reviewing the Structure-Function Paradigm in Polyglutamine Disorders: A Synergistic Perspective on Theoretical and Experimental Approaches

Polyglutamine (polyQ) disorders are a group of neurodegenerative diseases characterized by the excessive expansion of CAG (cytosine, adenine, guanine) repeats within host proteins. The quest to unravel the complex diseases mechanism has led researchers to adopt both theoretical and experimental methods, each offering unique insights into the underlying pathogenesis. This review emphasizes the significance of combining multiple approaches in the study of polyQ disorders, focusing on the structure-function correlations and the relevance of polyQ-related protein dynamics in neurodegeneration. By integrating computational/theoretical predictions with experimental observations, one can establish robust structure-function correlations, aiding in the identification of key molecular targets for therapeutic interventions. PolyQ proteins' dynamics, influenced by their length and interactions with other molecular partners, play a pivotal role in the polyQ-related pathogenic cascade. Moreover, conformational dynamics of polyQ proteins can trigger aggregation, leading to toxic assembles that hinder proper cellular homeostasis. Understanding these intricacies offers new avenues for therapeutic strategies by fine-tuning polyQ kinetics, in order to prevent and control disease progression. Last but not least, this review highlights the importance of integrating multidisciplinary efforts to advancing research in this field, bringing us closer to the ultimate goal of finding effective treatments against polyQ disorders.

The alteration of structural network upon transient association between proteins studied using graph theory

Proteins such as enzymes perform their function by predominant non-covalent bond interactions between transiently interacting units. There is an impact on the overall structural topology of the protein, albeit transient nature of such interactions, that enable proteins to deactivate or activate. This aspect of the alteration of the structural topology is studied by employing protein structural networks, which are node-edge representative models of protein structure, reported as a robust tool for capturing interactions between residues. Several methods have been optimized to collect meaningful, functionally relevant information by studying alteration of structural networks. In this article, different methods of comparing protein structural networks are employed, along with spectral decomposition of graphs to study the subtle impact of protein-protein interactions. A detailed analysis of the structural network of interacting partners is performed across a dataset of around 900 pairs of bound complexes and corresponding unbound protein structures. The variation in network parameters at, around, and far away from the interface are analyzed. Finally, we present interesting case studies, where an allosteric mechanism of structural impact is understood from communication-path detection methods. The results of this analysis are beneficial in understanding protein stability, for future engineering, and docking studies.

Understanding structural variability in proteins using protein structural networks

Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.

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Monomeric, single domain protein structures can exhibit non-rigid behaviour and be highly variable.

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The comparison of protein structural networks can better discriminate conformations with similar backbones.

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Specific interactions between solvent accessible and inaccessible residues are poorly preserved.

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Network edge-variation offers insights on which interacting residues are likely to influence their dynamics and function.

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These side-chain network-based studies provide a framework to correlate protein structure-function relationships.

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.

Saturation Mutagenesis of the Transmembrane Region of HokC in Escherichia coli Reveals Its High Tolerance to Mutations

Cells adapt to different stress conditions, such as the antibiotics presence. This adaptation sometimes is achieved by changing relevant protein positions, of which the mutability is limited by structural constrains. Understanding the basis of these constrains represent an important challenge for both basic science and potential biotechnological applications. To study these constraints, we performed a systematic saturation mutagenesis of the transmembrane region of HokC, a toxin used by Escherichia coli to control its own population, and observed that 92% of single-point mutations are tolerated and that all the non-tolerated mutations have compensatory mutations that reverse their effect. We provide experimental evidence that HokC accumulates multiple compensatory mutations that are found as correlated mutations in the HokC family multiple sequence alignment. In agreement with these observations, transmembrane proteins show higher probability to present correlated mutations and are less densely packed locally than globular proteins; previous mutagenesis results on transmembrane proteins further support our observations on the high tolerability to mutations of transmembrane regions of proteins. Thus, our experimental results reveal the HokC transmembrane region high tolerance to loss-of-function mutations that is associated with low sequence conservation and high rate of correlated mutations in the HokC family sequences alignment, which are features shared with other transmembrane proteins.

Catalytic and binding sites prediction in globular proteins through discrete Markov chains and network centrality measures

In this work we use a discrete Markov chain approach combined with network centrality measures to identify and predict the location of active sites in globular proteins. To accomplish this, we use a three-dimensional network of proteinC_αatoms as nodes connected through weighted edges which represent the varying interaction degree between protein's atoms. We compute the mean first passage time matrixH= {H_ji} for this Markov chain and evaluate the averaged number of steps ⟨H_j⟩ to reach single noden_jin order to identify such residues that, on the average, are at the least distant from every other node. We also carry out a graph theory analysis to evaluate closeness centralityC_c, betweenness centralityC_band eigenvector centralityC_emeasures which provide relevant information about the connectivity structure and topology of theC_αprotein networks. Finally we also performed an analysis of equivalent random and regular networks of the same sizeNin terms of the average path lengthLand the average clustering coefficient⟨C⟩comparing these with the corresponding values forC_αprotein networks. Our results show that the mean-first passage time matrixHand its related quantity ⟨H_j⟩ together withC_c,C_bandC_ecan not only predict with relative high accuracy the location of active sites in globular proteins but also exhibit a high feasibility to use them to predict the existence of new regions in protein's structure to identify new potential binding or catalytic activity or, in some cases, the presence of new allosteric pathways.

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.

Network-based strategies for protein characterization

Protein structure characterization is fundamental to understand protein properties, such as folding process and protein resistance to thermal stress, up to unveiling organism pathologies (e.g., prion disease). In this chapter, we provide an overview on how the spectral properties of the networks reconstructed from the Protein Contact Map (PCM) can be used to generate informative observables. As a specific case study, we apply two different network approaches to an example protein dataset, for the aim of discriminating protein folding state, and for the reconstruction of protein 3D structure.

Protein conformational switch discerned via network centrality properties

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensemble-informed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes.

Exploring the conformational dynamics and flexibility of intrinsically disordered HIV-1 Nef protein using molecular dynamic network approaches

Intrinsically disordered proteins represent a class of proteins that lack fixed and well-defined three-dimensional structures in solution. HIV-1 Nef is an intrinsically disordered peripheral membrane protein involved in the replication and pathogenesis of HIV-1 infection. Nef controls expression levels of cell surface CD4 molecules that are essential for adaptive immunity. Despite the lack of fixed and stable structures, Nef physically interacts with the host cellular proteins (AP-1/MHC-I) and modulates intracellular trafficking pathways. Therefore, it is essential to understand how this dynamic conformational flexibility affects Nef structures and function. In this study, we combined all-atom molecular dynamics (MD) simulations and dynamic network approaches to better understand the structure and dynamics of Nef in two different forms, the free unbound and the bound state. Using the MD simulation approach, we show that the intrinsically disordered Nef exhibit a large dynamic field with more atomic fluctuations and lesser thermodynamic stability in the unbound conditions. The conformations of Nef change over time, and this protein remains more compact, folded, and stable in the bound form. The dynamic network analysis revealed regions of the protein capable of modulating the conformational behavior of the disordered Nef. The average betweenness centrality (BC) unveiled residues that are critical for mediating protein-protein interactions. The average shortest path length (L) and the perturbation response scanning exposed residues that are likely to be important in steering protein conformational changes. Overall, the study demonstrates how all-atom MD simulations combined with the dynamic network approach can be used to gain further insights into the structure and dynamics-function relationship of intrinsically disordered HIV-1 Nef.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02698-8.

RBinds: A user-friendly server for RNA binding site prediction

RNA performs various biological functions by interacting with other molecules. The knowledge of RNA binding sites is essential for the understanding of RNA-protein or RNA-ligand complex structures and their mechanisms. However, the RNA binding site prediction study requires tedious programming scripts and manual handling. One user-friendly bioinformatics tool for RNA binding site prediction has been missing. This limitation motivated us to develop the RBinds, a user-friendly web server, to predict the RNA binding site using a simple graphical user interface. Some advanced features implemented in RBinds are (1) transforming the RNA structure to a network automatically; (2) analyzing the structural network properties to predict binding site; (3) constructing one annotated force-directed network; (4) providing a visualization tool for users to scale and rotate the structure; (5) offering the related tools to predict or simulate RNA structures. RBinds web server is a reliable and user-friendly tool and facilitates the RNA binding site study without installing programs locally. RBinds is freely accessible at http://zhaoserver.com.cn/RBinds/RBinds.html.

Increasing the Affinity of an O-Antigen Polysaccharide Binding Site in Shigella flexneri Bacteriophage Sf6 Tailspike Protein

Broad and unspecific use of antibiotics accelerates spread of resistances. Sensitive and robust pathogen detection is thus important for a more targeted application. Bacteriophages contain a large repertoire of pathogen-binding proteins. These tailspike proteins (TSP) often bind surface glycans and represent a promising design platform for specific pathogen sensors. We analysed bacteriophage Sf6 TSP that recognizes the O-polysaccharide of dysentery-causing Shigella flexneri to develop variants with increased sensitivity for sensor applications. Ligand polyrhamnose backbone conformations were obtained from 2D 1 H,1 H-trNOESY NMR utilizing methine-methine and methine-methyl correlations. They agreed well with conformations obtained from molecular dynamics (MD), validating the method for further predictions. In a set of mutants, MD predicted ligand flexibilities that were in good correlation with binding strength as confirmed on immobilized S. flexneri O-polysaccharide (PS) with surface plasmon resonance. In silico approaches combined with rapid screening on PS surfaces hence provide valuable strategies for TSP-based pathogen sensor design.

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.

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/

Exploring the potency of currently used drugs against HIV-1 protease of subtype D variant by using multiscale simulations

Acquired immune deficiency syndrome (AIDS) is caused by the human immunodeficiency virus (HIV), type 1 and 2. Further, the diversity in HIV-1 has given rise to many serotypes and recombinant strains. The currently used protease inhibitors have been developed for subtype B, although non-B subtype strains account for ∼ 90% of the global HIV infections. Subtype D is spreading rapidly and infecting a large population in North Africa and the Middle East. In the current study, molecular dynamics simulations in conjunction with the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) scheme was used to investigate the potency of four drugs, namely atazanavir (ATV), darunavir (DRV), lopinavir (LPV) and tipranavir (TPV) against the subtype D variant. Our calculations predicted that the potency of the inhibitors decreased in the order TPV > ATV > DRV > LPV. TPV was found to be the most potent against subtype D due to an increase in van der Waals and electrostatic interactions and reduction in the desolvation energy compared to other inhibitors. This result is further supported by the hydrogen bond interactions between inhibitors and protease. Furthermore, our analyses suggested that the binding of TPV induced a more closed conformation of the flap compared to apo or other complexes. It was observed that TPV/PR^D has a lower cavity volume relative to the other three complexes leading to a tighter binding. The open conformation of the flap was observed for LPV/PR^D. We expect that this study might be useful for designing more potent inhibitors against HIV-1 subtype D. Communicated by Ramaswamy H. Sarma.

Investigating Phosphorylation-Induced Conformational Changes in WNK1 Kinase by Molecular Dynamics Simulations

The With-No-Lysine (WNK) kinase is considered to be a master regulator for various cation-chloride cotransporters involved in maintaining cell-volume and ion homeostasis. Here, we have investigated the phosphorylation-induced structural dynamics of the WNK1 kinase bound to an inhibitor via atomistic molecular dynamics simulations. Results from our simulations show that the phosphorylation at Ser³⁸² could stabilize the otherwise flexible activation loop (A-loop). The intrahelix salt-bridge formed between Arg²⁶⁴ and Glu²⁶⁸ in the unphosphorylated system is disengaged after the phosphorylation, and Glu²⁶⁸ reorients itself and forms a stable salt-bridge with Arg³⁴⁸. The dynamic cross-correlation analysis shows that phosphorylation diminishes anticorrelated motions and increases correlated motions between different domains. Structural network analysis reveals that the phosphorylation causes structural rearrangements and shortens the communication path between the αC-helix and catalytic loop, making the binding pocket more suitable for accommodating the ligand. Overall, we have characterized the structural changes in the WNK kinase because of phosphorylation in the A-loop, which might help in designing rational drugs.

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.

Hierarchy and Assortativity as New Tools for Binding-Affinity Investigation: The Case of the TBA Aptamer-Ligand Complex

Aptamers are single stranded DNA, RNA, or peptide sequences having the ability to bind several specific targets (proteins, molecules as well as ions). Therefore, aptamer production and selection for therapeutic and diagnostic applications is very challenging. Usually, they are generated in vitro, although computational approaches have been recently developed for the in silico production. Despite these efforts, the mechanism of aptamer-ligand formation is not completely clear, and producing high-affinity aptamers is still quite difficult. This paper aims to develop a computational model able to describe aptamer-ligand affinity. Topological tools, such as the conventional degree distribution, the rank-degree distribution (hierarchy), and the node assortativity are employed. In doing so, the macromolecules tertiary-structures are mapped into appropriate graphs. These graphs reproduce the main topological features of the macromolecules, by preserving the distances between amino acids (nucleotides). Calculations are applied to the thrombin binding aptamer (TBA), and the TBA-thrombin complex produced in the presence of Na⁺ or K⁺. The topological analysis is able to detect several differences between complexes obtained in the presence of the two cations, as expected by previous investigations. These results support graph analysis as a novel computational tool for testing affinity. Otherwise, starting from the graphs, an electrical network can be obtained by using the specific electrical properties of amino acids and nucleobases. Therefore, a further analysis concerns with the electrical response, revealing that the resistance is sensitively affected by the presence of sodium or potassium, thus suggesting resistance as a useful physical parameter for testing binding affinity.

Understanding contact patterns of protein structures from protein contact map and investigation of unique patterns in the globin-like folded domains

Proteins are biochemical compounds made up of one or more polypeptides in a specific order, typically folded into a functionally active form. Proteins are categorized into four different structural classes according to the topology of α-helices and β-strands. In this study, we modeled these four structural classes as an undirected network depicting amino acids as nodes and interaction between them as edges. Results infer that basic protein classes can be easily recognized as well as distinguished by utilizing protein contact maps (PCM). Toward studying the globin-like fold, the helix-loop-helix region contacts were seen to be of a unique pattern, and these remained in all the folds. Further, the averaged diagonal contacts were analyzed and identified those contacts in α/β proteins were higher in comparison with the other class. Interesting, we noticed that anti-parallel beta sheets were dominant in all-β and α + β classes that lead to similar diagonal patterns. Network properties of all four basic classes were analyzed and found to possess small-world property. Findings infer that PCM may assist classify protein structure classes and it also helps in evaluating the predicted protein structures.

Protein Residue Networks from Energetic and Geometric Data: Are They Identical?

Protein residue networks (PRN) from energetic and geometric data are probably not identical. PRNs constructed from ab initio pair interaction energies are analyzed for the first time and compared to PRN based on center of mass separation. We use modern, previously unused algorithms such as global and local efficiencies to quantitatively confirm that both types of PRNs do exhibit small-world character. The main novelty finding is that interaction energy-based PRNs preserve small-world character even when clustered. A node hierarchy independent of the cutoff energy used for the edge creation is characteristic for them. Efficiency centrality identifies hubs responsible for such behavior. The interaction energy-based PRNs seem to comply with the scale-free network model with respect to efficiency centrality distribution as opposed to distance based PRNs. Community detection is introduced into protein network research as an extension beyond cluster analysis to study tertiary and quaternary structures.

Physical crosslinking of hyaluronic acid in the presence of phospholipids in an aqueous nano-environment

Hyaluronic acid and phospholipids are two components in the synovial joint cavity that contribute to joint lubrication synergistically. Molecular dynamics simulations were performed and hydrogen bonds in hyaluronic acid were analyzed to identify specific sites that are responsible for its physical cross-linking. Two molecular masses of hyaluronic acid, 10 kDa and 160 kDa, were considered. We use molecular dynamics simulations and the small world network approach to investigate dynamic couplings using a distance map applied to oxygen atoms in a chain of hyaluronic acid in the presence of phospholipids and water. The distance characterizing the coupling can be defined in various ways to bring out the most evident differences between various scenarios of the polymer chain conformation We show herein a physical distance understood as H-bond length and classes of these distances which are defined in a coarse-grained picture of the molecule. Simulation results indicate that addition of phospholipids has little influence on hyaluronic acid crosslinking. However, longer chains and addition of lipids promote appreciably long lasting (resilient) networks that may be of importance in biological systems. Specific sites for hydrogen bonding of phospholipids to hyaluronic acid have also been identified.

Allosteric Modulation of Human Hsp90α Conformational Dynamics

Central to Hsp90's biological function is its ability to interconvert between various conformational states. Drug targeting of Hsp90's regulatory mechanisms, including its modulation by cochaperone association, presents as an attractive therapeutic strategy for Hsp90 associated pathologies. In this study, we utilized homology modeling techniques to calculate full-length structures of human Hsp90α in closed and partially open conformations and used these structures as a basis for several molecular dynamics based analyses aimed at elucidating allosteric mechanisms and modulation sites in human Hsp90α. Atomistic simulations demonstrated that bound adenosine triphosphate (ATP) stabilizes the dimer by "tensing" each protomer, while adenosine diphosphate (ADP) and apo configurations "relax" the complex by increasing global flexibility, the former case resulting in a fully open "v-like" conformation. Dynamic residue network analysis revealed regions of the protein involved in intraprotein communication and identified several key communication hubs that correlate with known functional sites. Pairwise comparison of betweenness centrality, shortest path, and residue fluctuations revealed that a proportional relationship exists between the latter two measurables and an inverse relationship between these two and betweenness centrality. This analysis showed how protein flexibility, degree of compactness, and the distance cutoff used for network construction influence the correlations between these metrics. These findings are novel and suggest shortest path and betweenness centrality to be more relevant quantities to follow for detecting functional residues in proteins compared to residue fluctuations. Perturbation response scanning analysis identified several potential residue sites capable of modulating conformational change in favor of interstate conversion. For the ATP-bound open conformation, these sites were found to overlap with known Aha1 and client binding sites, demonstrating how naturally occurring forces associated with cofactor binding could allosterically modulate conformational dynamics.

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/.

Characteristics Analyses and Comparisons of the Protein Structure Networks Constructed by Different Methods

Protein structure networks (PSNs) were widely used in analyses of protein structure and function. In this work, we analyzed and compared the characters of PSNs by different methods. The degrees of the different types of the nodes were found to be associated with the amino acid characters, including SAS, secondary structure, hydropathy and the volume of amino acids. It showed that PSNs by the methods of CA10, SC10 and AT5 inherited more amino acid characters and had higher correlations with the original protein structures. And PSNs by these three methods would be powerful tools in understanding the characters of protein structures.

Weighted protein residue networks based on joint recurrences between residues

BACKGROUND

Weighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the β-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively.

RESULTS

The joint recurrence weighted network approach performs well in pointing out key protein residues. Furthermore, it seems to emphasize residues with medium to high relative solvent accessibility that lie in loop regions between secondary structure elements of the protein.

CONCLUSIONS

Protein residue networks that use joint recurrences extracted from molecular dynamics simulations of a solvated protein perform well in pointing to hotspot residues and hotspot clusters. This approach uses no distance cutoff threshold, and does not exclude any interactions between the residues, including water-mediated interactions.

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.

Allosteric signalling in the outer membrane translocation domain of PapC usher

PapC ushers are outer-membrane proteins enabling assembly and secretion of P pili in uropathogenic E. coli. Their translocation domain is a large β-barrel occluded by a plug domain, which is displaced to allow the translocation of pilus subunits across the membrane. Previous studies suggested that this gating mechanism is controlled by a β-hairpin and an α-helix. To investigate the role of these elements in allosteric signal communication, we developed a method combining evolutionary and molecular dynamics studies of the native translocation domain and mutants lacking the β-hairpin and/or the α-helix. Analysis of a hybrid residue interaction network suggests distinct regions (residue ‘communities’) within the translocation domain (especially around β12–β14) linking these elements, thereby modulating PapC gating. Antibiotic sensitivity and electrophysiology experiments on a set of alanine-substitution mutants confirmed functional roles for four of these communities. This study illuminates the gating mechanism of PapC ushers and its importance in maintaining outer-membrane permeability.

DOI:http://dx.doi.org/10.7554/eLife.03532.001

Escherichia coli is a bacterium that commonly lives in the intestines of mammals, including humans, where it is usually harmless and can even be beneficial to its host. However, some types of E. coli produce hair-like filaments called P pili that allow the bacteria to attach to the human urinary tract and cause disease. To pass through the outer membrane of the E. coli cell, the filaments have to travel through a protein in the membrane called PapC usher.

The PapC usher protein—which is also involved in the assembly of the P pili filaments—contains a tube-like part called a β-barrel that is usually blocked by another part of the protein called the ‘plug domain’. For the P pili to pass through the β-barrel, the plug domain has to move. This movement is controlled by two parts of the PapC protein, known as the α-helix and the β-hairpin, but it is not clear how.

To address this question, Farabella et al. made computer models of the normal PapC protein and versions that lacked the α-helix and/or the β-hairpin. Looking at these structural models and analyzing the evolution of PapC proteins helped to predict that certain regions of the β-barrel may be involved in controlling the movement of the plug domain, and this was then confirmed experimentally. Farabella et al. propose that these regions—together with the α-helix and β-hairpin—control the opening and closing of the β-barrel.

Further work is needed to investigate how other parts of the PapC protein are involved in P pili formation. These new insights could prove useful in the development of alternative treatments to fight bacterial infection.

DOI:http://dx.doi.org/10.7554/eLife.03532.002

A novel memristive multilayer feedforward small-world neural network with its applications in PID control

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

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.