GraSp-PSN: A web server for graph spectra based analysis of protein structure networks.

The function of a protein is most of the time achieved due to minute conformational changes in its structure due to ligand binding or environmental changes or other interactions. Hence the analysis of structure of proteins should go beyond the analysis of mere atom contacts and should include the emergent global structure as a whole. This can be achieved by graph spectra based analysis of protein structure networks. GraSp-PSN is a web server that can assist in (1) acquiring weighted protein structure network (PSN) and network parameters ranging from atomic level to global connectivity from the three dimensional coordinates of a protein, (2) generating scores for comparison of a pair of protein structures with detailed information of local to global connectivity, and (3) assigning perturbation scores to the residues and their interactions, that can prioritise them in terms of residue clusters. The methods implemented in the server are generic in nature and can be used for comparing networks in any discipline by uploading adjacency matrices in the server. The webserver can be accessed using the following link: https://pople.mbu.iisc.ac.in/.

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

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

A review of visualisations of protein fold networks and their relationship with sequence and function.

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Comparison of allosteric signaling in DnaK and BiP using mutual information between simulated residue conformations.

The heat shock protein 70 kDa (Hsp70) chaperone system serves as a critical component of protein quality control across a wide range of prokaryotic and eukaryotic organisms. Divergent evolution and specialization to particular organelles have produced numerous Hsp70 variants which share similarities in structure and general function, but differ substantially in regulatory aspects, including conformational dynamics and activity modulation by cochaperones. The human Hsp70 variant BiP (also known as GRP78 or HSPA5) is of therapeutic interest in the context of cancer, neurodegenerative diseases, and viral infection, including for treatment of the pandemic virus SARS-CoV-2. Due to the complex conformational rearrangements and high sequential variance within the Hsp70 protein family, it is in many cases poorly understood which amino acid mutations are responsible for biochemical differences between protein variants. In this study, we predicted residues associated with conformational regulation of human BiP and Escherichia coli DnaK. Based on protein structure networks obtained from molecular dynamics simulations, we analyzed the shared information between interaction timelines to highlight residue positions with strong conformational coupling to their environment. Our predictions, which focus on the binding processes of the chaperone's substrate and cochaperones, indicate residues filling potential signaling roles specific to either DnaK or BiP. By combining predictions of individual residues into conformationally coupled chains connecting ligand binding sites, we predict a BiP specific secondary signaling pathway associated with substrate binding. Our study sheds light on mechanistic differences in signaling and regulation between Hsp70 variants, which provide insights relevant to therapeutic applications of these proteins.

Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks.

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.

Multi-layer sequential network analysis improves protein 3D structural classification.

Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3D structural features. We recently proposed PSC approaches that model protein 3D structures as protein structure networks (PSNs) and analyze PSN-based protein features, which performed better than or comparable to state-of-the-art sequence or other 3D structure-based PSC approaches. However, existing PSN-based PSC approaches model the whole 3D structure of a protein as a static (i.e., single-layer) PSN. Because folding of a protein is a dynamic process, where some parts (i.e., sub-structures) of a protein fold before others, modeling the 3D structure of a protein as a PSN that captures the sub-structures might further help improve the existing PSC performance. Here, we propose to model 3D structures of proteins as multi-layer sequential PSNs that approximate 3D sub-structures of proteins, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on single-layer PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches). Indeed, we confirm this on 72 datasets spanning ~44 000 CATH and SCOPe protein domains.

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

Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function. The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis. Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold. In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output. PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail-an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This "neural upscaling" procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 µs atomistic molecular dynamics trajectory of Aß1-40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

Entropy-based distance cutoff for protein internal contact networks.

Protein structure networks (PSNs) have long been used to provide a coarse yet meaningful representation of protein structure, dynamics, and internal communication pathways. An important question is what criteria should be applied to construct the network so that to include relevant interresidue contacts while avoiding unnecessary connections. To address this issue, we systematically considered varying residue distance cutoff length and the probability threshold for contact formation to construct PSNs based on atomistic molecular dynamics in order to assess the amount of mutual information within the resulting representations. We found that the minimum in mutual information is universally achieved at the cutoff length of 5 Å, irrespective of the applied contact formation probability threshold in all considered, distinct proteins. Assuming that the optimal PSNs should be characterized by the least amount of redundancy, which corresponds to the minimum in mutual information, this finding suggests an objective criterion for cutoff distance and supports the existing preference towards its customary selection around 5 Å length, typically based to date on heuristic criteria.

Structural aspects of rod opsin and their implication in genetic diseases.

Vision in dim-light conditions is triggered by photoactivation of rhodopsin, the visual pigment of rod photoreceptor cells. Rhodopsin is made of a protein, the G protein coupled receptor (GPCR) opsin, and the chromophore 11-cis-retinal. Vertebrate rod opsin is the GPCR best characterized at the atomic level of detail. Since the release of the first crystal structure 20 years ago, a huge number of structures have been released that, in combination with valuable spectroscopic determinations, unveiled most aspects of the photobleaching process. A number of spontaneous mutations of rod opsin have been found linked to vision-impairing diseases like autosomal dominant or autosomal recessive retinitis pigmentosa (adRP or arRP, respectively) and autosomal congenital stationary night blindness (adCSNB). While adCSNB is mainly caused by constitutive activation of rod opsin, RP shows more variegate determinants affecting different aspects of rod opsin function. The vast majority of missense rod opsin mutations affects folding and trafficking and is linked to adRP, an incurable disease that awaits light on its molecular structure determinants. This review article summarizes all major structural information available on vertebrate rod opsin conformational states and the insights gained so far into the structural determinants of adCSNB and adRP linked to rod opsin mutations. Strategies to design small chaperones with therapeutic potential for selected adRP rod opsin mutants will be discussed as well.

Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein-Protein Binding.

Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the structure and function of the protein. However, little is known about the capability of such analyses to identify residues involved in the formation of macromolecular complexes. Here, we performed six different centrality measures on the RINs generated from the complexes of the SKEMPI 2 database of changes in protein-protein binding upon mutation in order to evaluate the capability of each of these measures to identify major binding residues. The analyses were performed with and without the crystallographic water molecules, in addition to the protein residues. We also investigated the use of a weight factor based on the inter-residue distances to improve the detection of these residues. We show that for the identification of major binding residues, closeness, degree, and PageRank result in good precision, whereas betweenness, eigenvector, and residue centrality analyses give a higher sensitivity. Including water in the analysis improves the sensitivity of all measures without losing precision. Applying weights only slightly raises the sensitivity of eigenvector centrality analysis. We finally show that a combination of multiple centrality analyses is the optimal approach to identify residues that play a role in protein-protein interaction.

Network Re-Wiring During Allostery and Protein-Protein Interactions: A Graph Spectral Approach.

The process of allostery is often guided by subtle changes in the non-covalent interactions between residues of a protein. These changes may be brought about by minor perturbations by natural processes like binding of a ligand or protein-protein interaction. The challenge lies in capturing minute changes at the residue interaction level and following their propagation at local as well as global distances. While macromolecular effects of the phenomenon of allostery are inferred from experiments, a computational microscope can elucidate atomistic-level details leading to such macromolecular effects. Network formalism has served as an attractive means to follow this path and has been pursued further for the past couple of decades. In this chapter some concepts and methods are summarized, and recent advances are discussed. Specifically, the changes in strength of interactions (edge weight) and their repercussion on the overall protein organization (residue clustering) are highlighted. In this review, we adopt a graph spectral method to probe these subtle changes in a quantitative manner. Further, the power of this method is demonstrated for capturing re-ordering of side-chain interactions in response to ligand binding, which culminates into formation of a protein-protein complex in ß2-adrenergic receptors.

The PyInteraph Workflow for the Study of Interaction Networks From Protein Structural Ensembles.

PyInteraph is a software package designed for the analysis of structural communication from conformational ensembles, such as those derived from in silico simulations, under the formalism of protein structure networks. We demonstrate its usage for the calculation and analysis of intramolecular interaction networks derived from three different types of interactions, as well as with a more general protocol based on distances between centers of mass. We use the xPyder PyMOL plug-in to visualize such networks on the three-dimensional structure of the protein. We showcase our protocol on a molecular dynamics trajectory of the Cyclophilin A wild-type enzyme, a well-studied protein in which different allosteric mechanisms have been investigated.

Network-based protein structural classification.

Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922.

Dynamics and structural communication in the ternary complex of fully phosphorylated V2 vasopressin receptor, vasopressin, and ß-arrestin 1.

G protein-coupled receptors (GPCRs) are critically regulated by arrestins, which not only desensitize G-protein signaling but also initiate a G protein-independent wave of signaling. The information from structure determination was herein exploited to build a structural model of the ternary complex, comprising fully phosphorylated V2 vasopressin receptor (V2R), the agonist arginine vasopressin (AVP), and ß-arrestin 1 (ß-arr1). Molecular simulations served to explore dynamics and structural communication in the ternary complex. Flexibility and mechanical profiles reflect fold of V2R and ß-arr1. Highly conserved amino acids tend to behave as hubs in the structure network and contribute the most to the mechanical rigidity of V2R seven-helix bundle and of ß-arr1. Two structurally and dynamically distinct receptor-arrestin interfaces assist the twist of the N- and C-terminal domains (ND and CD, respectively) of ß-arr1 with respect to each other, which is linked to arrestin activation. While motion of the ND is essentially assisted by the fully phosphorylated C-tail of V2R (V2RCt), that of CD is assisted by the second and third intracellular loops and the cytosolic extensions of helices 5 and 6. In the presence of the receptor, the ß-arr1 inter-domain twist angle correlates with the modes describing the essential subspace of the ternary complex. ß-arr1 motions are also influenced by the anchoring to the membrane of the C-edge-loops in the ß-arr1-CD. Overall fluctuations reveal a coupling between motions of the agonist binding site and of ß-arr1-ND, which are in allosteric communication between each other. Mechanical rigidity points, often acting as hubs in the structure network and distributed along the main axis of the receptor helix bundle, contribute to establish a preferential communication pathway between agonist ligand and the ND of arrestin. Such communication, mediated by highly conserved amino acids, involves also the first amino acid in the arrestin C-tail, which is highly dynamic and is involved in clathrin-mediated GPCR internalization.

Both intra and inter-domain interactions define the intrinsic dynamics and allosteric mechanism in DNMT1s.

DNA methyltransferase 1 (DNMT1), a large multidomain enzyme, is believed to be involved in the passive transmission of genomic methylation patterns via methylation maintenance. Yet, the molecular mechanism of interaction networks underlying DNMT1 structures, dynamics, and its biological significance has yet to be fully characterized. In this work, we used an integrated computational strategy that combined coarse-grained and atomistic simulations with coevolution information and network modeling of the residue interactions for the systematic investigation of allosteric dynamics in DNMT1. The elastic network modeling has proposed that the high plasticity of RFTS has strengthened the correlated behaviors of DNMT1 structures through the hinge sites located at the RFTS-CD interface, which mediate the collective motions between domains. The perturbation response scanning (PRS) analysis combined with the enrichment analysis of disease mutations have further highlighted the allosteric potential of the RFTS domain. Furthermore, the long-range paths connect the intra-domain interactions through the TRD interface and catalytic interface, emphasizing some key inter-domain interactions as the bridges in the global allosteric regulation of DNMT1. The observed interplay between conserved intra-domain networks and dynamical plasticity encoded by inter-domain interactions provides insights into the intrinsic dynamics and functional evolution, as well as the design of allosteric modulators of DNMT1 based on the TRD interface.

Influence of Disease-Causing Mutations on Protein Structural Networks.

The interactions between residues in a protein tertiary structure can be studied effectively using the approach of protein structure network (PSN). A PSN is a node-edge representation of the structure with nodes representing residues and interactions between residues represented by edges. In this study, we have employed weighted PSNs to understand the influence of disease-causing mutations on proteins of known 3D structures. We have used manually curated information on disease mutations from UniProtKB/Swiss-Prot and their corresponding protein structures of wildtype and disease variant from the protein data bank. The PSNs of the wildtype and disease-causing mutant are compared to analyse variation of global and local dissimilarity in the overall network and at specific sites. We study how a mutation at a given site can affect the structural network at a distant site which may be involved in the function of the protein. We have discussed specific examples of the disease cases where the protein structure undergoes limited structural divergence in their backbone but have large dissimilarity in their all atom networks and vice versa, wherein large conformational alterations are observed while retaining overall network. We analyse the effect of variation of network parameters that characterize alteration of function or stability.

Effects of Membrane and Biological Target on the Structural and Allosteric Properties of Recoverin: A Computational Approach.

Recoverin (Rec) is a prototypical calcium sensor protein primarily expressed in the vertebrate retina. The binding of two Ca2+ ions to the functional EF-hand motifs induces the extrusion of a myristoyl group that increases the affinity of Rec for the membrane and leads to the formation of a complex with rhodopsin kinase (GRK1). Here, unbiased all-atom molecular dynamics simulations were performed to monitor the spontaneous insertion of the myristoyl group into a model multicomponent biological membrane for both isolated Rec and for its complex with a peptide from the GRK1 target. It was found that the functional membrane anchoring of the myristoyl group is triggered by persistent electrostatic protein-membrane interactions. In particular, salt bridges between Arg43, Arg46 and polar heads of phosphatidylserine lipids are necessary to enhance the myristoyl hydrophobic packing in the Rec-GRK1 assembly. The long-distance communication between Ca2+-binding EF-hands and residues at the interface with GRK1 is significantly influenced by the presence of the membrane, which leads to dramatic changes in the connectivity of amino acids mediating the highest number of persistent interactions (hubs). In conclusion, specific membrane composition and allosteric interactions are both necessary for the correct assembly and dynamics of functional Rec-GRK1 complex.

Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods.

Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aß1-40) and its "Arctic" variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.

Adaptive Steered Molecular Dynamics Combined With Protein Structure Networks Revealing the Mechanism of Y68I/G109P Mutations That Enhance the Catalytic Activity of D-psicose 3-Epimerase From Clostridium Bolteae.

The scarcity, richness, and other important physiological functions of D-psicose make it crucial to increase the yield of D-psicose. The production of D-psicose can be accomplished by D-psicose 3-epimerase (DPEase) from Clostridium bolteae (CbDPEase) catalyzing the substrate D-fructose. Although the catalytic efficiency of the CbDPEase has been raised via using the site-directed mutagenesis (Y68I/G109P) technique, structure-activity relationship in the wild-type CbDPEase and Y68I/G109P mutant is currently poorly understood. In our study, a battery of molecular modeling methods [homology modeling, adaptive steered molecular dynamics (ASMD) simulations, and Molecular Mechanics/Generalized Born Surface Area (MM-GB/SA)], combined with protein structure networks, were employed to theoretically characterize the reasons for the differences in the abilities of the D-fructose catalyzed by the wild-type CbDPEase and Y68I/G109P mutant. Protein structure networks demonstrated that site-directed mutagenesis enhanced the connectivity between D-fructose and CbDPEase, leading to the increased catalytic efficiency mediated by the functional residues with high betweenness. During the dissociation of the D-fructose from the Y68I/G109P mutant, planes of benzene rings of F248 and W114 could be continuously parallel to the stretching direction of D-fructose. It made the tunnel have an open state and resulted in the stable donor-π interactions between D-fructose and the benzene rings around 18Å. The stronger substrate-protein interactions were detected in the Y68I/G109P mutant, instead of in the wild-type CbDPEase, which were consistent with the binding free energy and Potential Mean of Force (PMF) results. The theoretical results illustrated the reasons that Y68I/G109P mutations increased the catalytic efficiency of CbDPEase and could be provided the new clue for further DPEase engineering.