Protein Structure Network Analysis Research Articles

Rational Engineering of a β-Glucosidase (H0HC94) from Glycosyl Hydrolase Family I (GH1) to Improve Catalytic Performance on Cellobiose.

The conversion of lignocellulosic feedstocks by cellulases to glucose is a critical step in biofuel production. β-Glucosidases catalyze the final step in cellulose breakdown, producing glucose, and are often the rate-limiting step in biomass hydrolysis. The specific activity of most natural and engineered β-glucosidase is higher on the artificial substrate p-nitrophenyl β-d-glucopyranoside (pNPGlc) than on the natural substrate, cellobiose. We report an engineered β-glucosidase (Q319A H0HC94) with a 1.8-fold higher specific activity (366.3 ± 36 μmol/min/mg), a 1.5-fold increase in kcat (340.8 ± 27 s-1), and a 3-fold increase in catalytic efficiency (236.65 mM-1 s-1) over H0HC94 (WT) on cellobiose. Molecular dynamic simulations and protein structure network analysis indicate that the Q319A H0HC94 active site pocket is significantly remodeled compared to the WT, leading to changes in enzyme conformation, better accessibility of cellobiose inside the active site pocket, and higher enzymatic activity. This study shows the impact of rational engineering of a nonconserved residue to increase β-glucosidase substrate accessibility and catalytic efficiency by reducing crowding interaction between cellobiose and active site pocket residues near the gatekeeper region and increasing pocket volume and surface area. Thus, rational engineering of previously characterized enzymes could be an excellent strategy to improve cellulose hydrolysis.

Comparative analysis of structural dynamics and allosteric mechanisms of RecA/Rad51 family proteins: Integrated atomistic MD simulation and network-based analysis

Homologous recombination plays a key role in double-strand break repair, stalled replication fork repair, and meiosis. The RecA/Rad51 family recombinases catalyze the DNA strand invasion reaction that occurs during homologous recombination. However, the high sequence differences between homologous groups have hindered the thoroughly studies of this ancient protein family. The dynamic mechanisms of the family, particularly at the residual level, remain poorly understood. In this work, five representative RecA/Rad51 recombinase family members from all major kingdoms of living organisms: prokaryotes, eukaryotes, archaea, and viruses, were selected to explore the molecular mechanisms behind their conserved biological significance. A variety of techniques, including all-atom molecular dynamics simulation, perturbation response scanning, and protein structure network analysis, were used to examine the flexibility and correlation of protein domains, distribution of sensors and effectors and conserved hub residues. Furthermore, the potential communication routes between the ATP-binding region and the DNA-binding region of each recombinase were identified. Our results demonstrate the conserved molecular dynamics of these recombinases in the early stage of homologous recombination, including cooperative motions between regions, conserved sensing and effecting functional residue distribution, and conserved hub residues. Meanwhile, the unique ATP-DNA communication routes of each recombinase was also revealed. These results provide new insights into the mechanism of RecA/Rad51 family proteins, and provide new theoretical guidance for the development of allosteric inhibitors and the application of RecA/Rad51 family proteins.

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/.

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

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

Allosteric mechanism for SL RNA recognition by polypyrimidine tract binding protein RRM1: An atomistic MD simulation and network-based study

Polypyrimidine tract-binding protein (PTB), an RNA-binding protein, is involved in the regulation of diverse processes in mRNA metabolism. However, the allosteric modulation of its binding with RNA remains unclear. We explore the dynamic characteristics of PTB RNA recognition motif 1 (RRM1) in its RNA-free and wild-type/mutant RNA-bound states to understand the issues using molecular dynamics (MD) simulation, perturbation response scanning (PRS) and protein structure network (PSN) models. It is found that RNA binding strengthens RRM1 stability, while L151G mutation in α3 helix far away from the interface makes the complex unstable. The latter is caused by long-distance dynamic couplings, which makes intermolecular electrostatic and entropy energies unfavorable. The weakened couplings between interface β sheets and C-terminal parts upon mutation reveal RNA recognition is co-regulated by these regions. Interestingly, PRS analysis reveals the allostery caused by the perturbation on α3 helix has already been pre-encoded in the equilibrium dynamics of the protein structure. PSN analysis shows the details of the allosteric signal transmission, revealing the necessity of strong couplings between α3 helix and interface for maintaining the high binding affinity. This study sheds light on the mechanisms of PTB allostery and RNA recognition and can provide important information for drug design.

Phosphorylation-Induced Conformational Dynamics and Inhibition of Janus Kinase 1 by Suppressors of Cytokine Signaling 1.

The dysfunction of the JAK/STAT (Janus kinase/signal transducers and activators of transcription) pathway results in several pathophysiological conditions, including autoimmune disorders. The negative feedback regulators of the JAK/STAT signaling pathway, suppressors of cytokine signaling (SOCS), act as a natural inhibitor of JAK and inhibit aberrant activity. SOCS1 is the most potent member of the SOCS family, whose kinase inhibitory region targets the substrate-binding groove of JAK with high affinity and blocks the phosphorylation of JAK kinases. Overall, we performed an aggregate of 13 μs molecular dynamics simulations on the activation loop's three different phosphorylation (double and single) states. Results from our simulations show that the single Tyr1034 phosphorylation could stabilize the JAK1/SOCS1 complex as well as the flexible activation segment. The phosphate-binding loop (P-loop) shows conformational variability at dual and single phosphorylated states. Principal component analysis and protein structure network (PSN) analysis reveal that the different phosphorylation states and SOCS1 binding induce intermediate inactive conformations of JAK1, which could be a better target for future JAK1 selective drug design. PSN analysis suggests that the com-pY1034 system is stabilized due to higher values of network hubs than the other two complex systems. Moreover, the binding free energy calculations suggest that pTyr1034 states show a higher affinity toward SOCS1 than the dual and pTyr1035 states. We believe that the mechanistic understanding of JAK1/SOCS1 complexation will aid future studies related to peptide inhibitors based on SOCS1.

Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation.

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.

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.

Role of Conformational Change and Glucose Binding Sites in the Enhanced Glucose Tolerance of Agrobacterium tumefaciens 5A GH1 β-Glucosidase Mutants.

β-Glucosidases are often inhibited by their reaction product glucose and a barrier to the efficient lignocellulosic biomass hydrolysis to glucose. We had previously reported the mutants, C174V, and H229S, with a nearly 2-fold increased glucose tolerance over the wild type (WT), H0HC94, encoded in Agrobacterium tumefaciens 5A (apparent Ki,Glc = 686 mM). We report our steady-state and time-resolved intrinsic fluorescence spectroscopy, circular dichroism, and isothermal titration calorimetry (ITC) studies to further understand increased glucose tolerance. Changes in the mutants' emission intensity and the differential change in quenching rate in the absence and presence of glucose reflect changes in protein conformation by glucose. Time-resolved lifetime and anisotropy measurements further indicated the microenvironment differences across solvent-exposed tryptophan residues and a higher hydrodynamic radius due to glucose binding, respectively. ITC measurements confirmed the increase of glucose binding sites in the mutants. The experiment results were supported by molecular dynamics simulations, which revealed significant variations in the glucose-protein hydrogen-bonding profiles. Protein structure network analysis of the simulated structures further indicates the mutants' conformation change than the WT. Computational studies also indicated additional glucose binding sites in mutants. Our results indicate the role of glucose binding in modulating the enzyme response to glucose.

Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations

The main protease, Mpro/3CLpro, plays an essential role in processing polyproteins translated from viral RNA to produce functional viral proteins and therefore serve as an attractive target for discovering COVID-19 therapeutics. The availability of both monomer and dimer crystal bound with a common ligand, ‘13b’ (α-ketoamide inhibitor), opened up opportunities to understand the Mpro mechanism of action. A comparative analysis of both forms of Mpro was carried out to elucidate the binding site architectural differences in the presence and absence of ‘13b’. Molecular dynamics simulations suggest that the presence of ‘13b’ enhances the stability of Mpro than the unbound APO form. The N- and C- terminals of both the protomers stabilize each other, and making it's interface essential for the active form of Mpro. In comparison to monomer, the relatively high affinity of ‘13b’ is gained in dimer pocket due to the high stability of the pocket by the interaction of S1 residue of chain B with residues F140, E166 and H172 of chain A, which is absent in monomer. The comprehensive essential dynamics, protein structure network analysis and thermodynamic profiling highlight the hot-spots, pivotal in molecular recognition process at protein-ligand and protein-protein interaction levels, cross-validated through computational alanine scanning study. A comparative description of ‘13b’ binding mechanism in both forms illustrates valuable insights into the inhibition mechanism and the selection of critical residues suitable for the structure-based approaches for the identification of more potent Mpro inhibitors. Communicated by Ramaswamy H. Sarma

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.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease (Mpro) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.

Exploring the Activation Mechanism of a Metabotropic Glutamate Receptor Homodimer via Molecular Dynamics Simulation.

Metabotropic glutamate receptors of class C GPCRs exist as constitutive dimers, which play important roles in activating excitatory synapses of the central nervous system. However, the activation mechanism induced by agonists has not been clarified in experiments. To address the problem, we used microsecond all-atom molecular dynamics (MD) simulation couple with protein structure network (PSN) to explore the glutamate-induced activation for the mGluR1 homodimer. The results indicate that glutamate binding stabilizes not only the closure of Venus flytrap domains but also the polar interaction of LB2-LB2, in turn keeping the extracelluar domain in the active state. The activation of the extracelluar domain drives transmembrane domains (TMDs) of the two protomers closer and induces asymmetric activation for the TMD domains of the two protomers. One protomer with lower binding affinity to the agonist is activated, while the other protomer with higher binding energy is still in the inactive state. The PSN analysis identifies the allosteric regulation pathway from the ligand-binding pocket in the extracellular domain to the G-protein binding site in the intracellular TMD region and further reveals that the asymmetric activation is attributed to a combination of trans-pathway and cis-pathway regulations from two glumatates, rather than a single activation pathway. These observations could provide valuable molecular information for understanding of the structure and the implications in drug efficacy for the class C GPCR dimers.

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.

Evolutionary-Conserved Allosteric Properties of Three Neuronal Calcium Sensor Proteins.

Neuronal Calcium Sensors (NCS) are highly conserved proteins specifically expressed in neurons. Calcium (Ca2+)-binding to their EF-hand motifs results in a conformational change, which is crucial for the recognition of a specific target and the downstream biological process. Here we present a comprehensive analysis of the allosteric communication between Ca2+-binding sites and the target interfaces of three NCS, namely NCS1, recoverin (Rec), and GCAP1. In particular, Rec was investigated in different Ca2+-loading states and in complex with a peptide from the Rhodopsin Kinase (GRK1) while NCS1 was studied in a Ca2+-loaded state in complex with either the same GRK1 target or a peptide from the D2 Dopamine receptor. A Protein Structure Network (PSN) accounting for persistent non-covalent interactions between amino acids was built for each protein state based on exhaustive Molecular Dynamics simulations. Structural network analysis helped unveiling the role of key amino acids in allosteric mechanisms and their evolutionary conservation among homologous proteins. Results for NCS1 highlighted allosteric inter-domain interactions between Ca2+-binding motifs and residues involved in target recognition. Robust long range, allosteric protein-target interactions were found also in Rec, in particular originating from the EF3 motif. Interestingly, Tyr 86, involved the hydrophobic packing of the N-terminal domain, was found to be a key residue for both intra- and inter-molecular communication with EF3, regardless of the presence of target or Ca2+ ions. Finally, based on a comprehensive topological PSN analysis for Rec, NCS1, and GCAP1 and multiple sequence alignments with homolog proteins, we propose that an evolution-driven correlation may exist between the amino acids mediating the highest number of persistent interactions (high-degree hubs) and their conservation. Such conservation is apparently fundamental for the specific structural dynamics required in signaling events.

A Small Chaperone Improves Folding and Routing of Rhodopsin Mutants Linked to Inherited Blindness

SummaryThe autosomal dominant form of retinitis pigmentosa (adRP) is a blindness-causing conformational disease largely linked to mutations of rhodopsin. Molecular simulations coupled to the graph-based protein structure network (PSN) analysis and in vitro experiments were conducted to determine the effects of 33 adRP rhodopsin mutations on the structure and routing of the opsin protein. The integration of atomic and subcellular levels of analysis was accomplished by the linear correlation between indices of mutational impairment in structure network and in routing. The graph-based index of structural perturbation served also to divide the mutants in four clusters, consistent with their differences in subcellular localization and responses to 9-cis retinal. The stability core of opsin inferred from PSN analysis was targeted by virtual screening of over 300,000 anionic compounds leading to the discovery of a reversible orthosteric inhibitor of retinal binding more effective than retinal in improving routing of three adRP mutants.

Protein structure networks provide insight into active site flexibility in esterase/lipases from the carnivorous plant Drosera capensis.

In plants, esterase/lipases perform transesterification reactions, playing an important role in the synthesis of useful molecules, such as those comprising the waxy coatings of leaf surfaces. Plant genomes and transcriptomes have provided a wealth of data about expression patterns and the circumstances under which these enzymes are upregulated, e.g. pathogen defense and response to drought; however, predicting their functional characteristics from genomic or transcriptome data is challenging due to weak sequence conservation among the diverse members of this group. Although functional sequence blocks mediating enzyme activity have been identified, progress to date has been hampered by the paucity of information on the structural relationships among these regions and how they affect substrate specificity. Here we present methodology for predicting overall protein flexibility and active site flexibility based on molecular modeling and analysis of protein structure networks (PSNs). We define two new types of specialized PSNs: sequence region networks (SRNs) and active site networks (ASNs), which provide parsimonious representations of molecular structure in reference to known features of interest. Our approach, intended as an aid to target selection for poorly characterized enzyme classes, is demonstrated for 26 previously uncharacterized esterase/lipases from the genome of the carnivorous plant Drosera capensis and validated using a case/control design. Analysis of the network relationships among functional blocks and among the chemical moieties making up the catalytic triad reveals potentially functionally significant differences that are not apparent from sequence analysis alone.

Structure prediction and network analysis of chitinases from the Cape sundew, Drosera capensis

BackgroundCarnivorous plants possess diverse sets of enzymes with novel functionalities applicable to biotechnology, proteomics, and bioanalytical research. Chitinases constitute an important class of such enzymes, with future applications including human-safe antifungal agents and pesticides. Here, we compare chitinases from the genome of the carnivorous plant Drosera capensis to those from related carnivorous plants and model organisms. MethodsUsing comparative modeling, in silico maturation, and molecular dynamics simulation, we produce models of the mature enzymes in aqueous solution. We utilize network analytic techniques to identify similarities and differences in chitinase topology. ResultsHere, we report molecular models and functional predictions from protein structure networks for eleven new chitinases from D. capensis, including a novel class IV chitinase with two active domains. This architecture has previously been observed in microorganisms but not in plants. We use a combination of comparative and de novo structure prediction followed by molecular dynamics simulation to produce models of the mature forms of these proteins in aqueous solution. Protein structure network analysis of these and other plant chitinases reveal characteristic features of the two major chitinase families. General significanceThis work demonstrates how computational techniques can facilitate quickly moving from raw sequence data to refined structural models and comparative analysis, and to select promising candidates for subsequent biochemical characterization. This capability is increasingly important given the large and growing body of data from high-throughput genome sequencing, which makes experimental characterization of every target impractical.

Molecular mechanism of carbon nanotube to activate Subtilisin Carlsberg in polar and non-polar organic media.

In the work, we mainly used molecular dynamics (MD) simulation and protein structure network (PSN) to study subtilisin Carlsberg (SC) immobilized onto carbon nanotube (CNT) in water, acetonitrile and heptane solvents, in order to explore activation mechanism of enzymes in non-aqueous media. The result indicates that the affinity of SC with CNT follows the decreasing order of water > acetonitrile > heptane. The overall structure of SC and the catalytic triad display strong robustness to the change of environments, responsible for the activity retaining. However, the distances between two β-strands of substrate-binding pocket are significantly expanded by the immobilization in the increasing order of water < acetonitrile < heptane, contributing to the highest substrate-binding energy in heptane media. PSN analysis further reveals that the immobilization enhances structural communication paths to the substrate-binding pocket, leading to its larger change than the free-enzymes. Interestingly, the increase in the number of the pathways upon immobilization is not dependent on the absorbed extent but the desorbed one, indicating significant role of shifting process of experimental operations in influencing the functional region. In addition, some conserved and important hot-residues in the paths are identified, providing molecular information for functional modification.

Structure network analysis to gain insights into GPCR function.

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