Residue centrality, functionally important residues, and active site shape: analysis of enzyme and non-enzyme families

Computational biophysical characterization of the effect of gatekeeper mutations on the binding of ponatinib to the FGFR kinase

Fibroblast Growth Factor Receptor (FGFR) is connected to numerous downstream signalling cascades regulating cellular behavior. Any dysregulation leads to a plethora of illnesses, including cancer. Therapeutics are available, but drug resistance driven by gatekeeper mutation impedes the treatment. Ponatinib is an FDA-approved drug against BCR-ABL kinase and has shown effective results against FGFR-mediated carcinogenesis. Herein, we undertake molecular dynamics simulation-based analysis on ponatinib against all the FGFR isoforms having Val to Met gatekeeper mutations. The results suggest that ponatinib is a potent and selective inhibitor for FGFR1, FGFR2, and FGFR4 gatekeeper mutations. The extensive electrostatic and van der Waals interaction network accounts for its high potency. The FGFR3_VM mutation has shown resistance towards ponatinib, which is supported by their lesser binding affinity than wild-type complexes. The disengaged molecular brake and engaged hydrophobic spine were believed to be the driving factors for weak protein-ligand interaction. Taken together, the inhibitory and structural characteristics exhibited by ponatinib may aid in thwarting resistance based on Val-to-Met gatekeeper mutations at an earlier stage of treatment and advance the design and development of other inhibitors targeted at FGFRs harboring gatekeeper mutations.

Polysaccharide utilization loci encoded DUF1735 likely functions as membrane-bound spacer for carbohydrate active enzymes

Proteins featuring the Domain of Unknown Function 1735 are frequently found in Polysaccharide Utilization Loci, yet their role remains unknown. The domain and vicinity analyzer programs we developed mine the Kyoto Encyclopedia of Genes and Genomes and UniProt to enhance the functional prediction of DUF1735. Our datasets confirmed the exclusive presence of DUF1735 in Bacteroidota genomes, with Bacteroidetes thetaiotaomicron harboring 46 copies. Notably, 97.8% of DUF1735 are encoded in PULs, and 89% are N-termini of multimodular proteins featuring C-termini like Laminin_G_3, F5/8-typeC, and GH18 domains. Predominantly possessing a predicted lipoprotein signal peptide and sharing an immunoglobulin-like β-sandwich fold with the BACON domain and the N-termini of SusE/F, DUF1735 likely functions as N-terminal, membrane-bound spacer for diverse C-termini involved in PUL-mediated carbohydrate utilization.

Allosteric regulatory control in dihydrofolate reductase is revealed by dynamic asymmetry

We investigated the relationship between mutations and dynamics in Escherichia coli dihydrofolate reductase (DHFR) using computational methods. Our study focused on the M20 and FG loops, which are known to be functionally important and affected by mutations distal to the loops. We used molecular dynamics simulations and developed position-specific metrics, including the dynamic flexibility index (DFI) and dynamic coupling index (DCI), to analyze the dynamics of wild-type DHFR and compared our results with existing deep mutational scanning data. Our analysis showed a statistically significant association between DFI and mutational tolerance of the DHFR positions, indicating that DFI can predict functionally beneficial or detrimental substitutions. We also applied an asymmetric version of our DCI metric (DCIasym ) to DHFR and found that certain distal residues control the dynamics of the M20 and FG loops, whereas others are controlled by them. Residues that are suggested to control the M20 and FG loops by our DCIasym metric are evolutionarily nonconserved; mutations at these sites can enhance enzyme activity. On the other hand, residues controlled by the loops are mostly deleterious to function when mutated and are also evolutionary conserved. Our results suggest that dynamics-based metrics can identify residues that explain the relationship between mutation and protein function or can be targeted to rationally engineer enzymes with enhanced activity.

Full-scale network analysis reveals properties of the FV protein structure organization

Blood coagulation is a vital process for humans and other species. Following an injury to a blood vessel, a cascade of molecular signals is transmitted, inhibiting and activating more than a dozen coagulation factors and resulting in the formation of a fibrin clot that ceases the bleeding. In this process, the Coagulation factor V (FV) is a master regulator, coordinating critical steps of this process. Mutations to this factor result in spontaneous bleeding episodes and prolonged hemorrhage after trauma or surgery. Although the role of FV is well characterized, it is unclear how single-point mutations affect its structure. In this study, to understand the effect of mutations, we created a detailed network map of this protein, where each node is a residue, and two residues are connected if they are in close proximity in the three-dimensional structure. Overall, we analyzed 63 point-mutations from patients and identified common patterns underlying FV deficient phenotypes. We used structural and evolutionary patterns as input to machine learning algorithms to anticipate the effects of mutations and anticipated FV-deficiency with fair accuracy. Together, our results demonstrate how clinical features, genetic data and in silico analysis are converging to enhance treatment and diagnosis of coagulation disorders.

A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A

Introduction: Blood coagulation is an essential process to cease bleeding in humans and other species. This mechanism is characterized by a molecular cascade of more than a dozen components activated after an injury to a blood vessel. In this process, the coagulation factor VIII (FVIII) is a master regulator, enhancing the activity of other components by thousands of times. In this sense, it is unsurprising that even single amino acid substitutions result in hemophilia A (HA)-a disease marked by uncontrolled bleeding and that leaves patients at permanent risk of hemorrhagic complications. Methods: Despite recent advances in the diagnosis and treatment of HA, the precise role of each residue of the FVIII protein remains unclear. In this study, we developed a graph-based machine learning framework that explores in detail the network formed by the residues of the FVIII protein, where each residue is a node, and two nodes are connected if they are in close proximity on the FVIII 3D structure. Results: Using this system, we identified the properties that lead to severe and mild forms of the disease. Finally, in an effort to advance the development of novel recombinant therapeutic FVIII proteins, we adapted our framework to predict the activity and expression of more than 300 in vitro alanine mutations, once more observing a close agreement between the in silico and the in vitro results. Discussion: Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.

Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

Characterizing the structural dynamics of proteins with heterogeneous conformational landscapes is crucial to understanding complex biomolecular processes. To this end, dimensionality reduction algorithms are used to produce low-dimensional embeddings of the high-dimensional conformational phase space. However, identifying a compact and informative set of input features for the embedding remains an ongoing challenge. Here, we propose to harness the power of Residue Interaction Networks (RINs) and their centrality measures, established tools to provide a graph theoretical view on molecular structure. Specifically, we combine the closeness centrality, which captures global features of the protein conformation at residue-wise resolution, with EncoderMap, a hybrid neural-network autoencoder/multidimensional-scaling like dimensionality reduction algorithm. We find that the resulting low-dimensional embedding is a meaningful visualization of the residue interaction landscape that resolves structural details of the protein behavior while retaining global interpretability. This feature-based graph embedding of temporal protein graphs makes it possible to apply the general descriptive power of RIN formalisms to the analysis of protein simulations of complex processes such as protein folding and multidomain interactions requiring no protein-specific input. We demonstrate this on simulations of the fast folding protein Trp-Cage and the multidomain signaling protein FAT10. Due to its generality and modularity, the presented approach can easily be transferred to other protein systems.

Intermediate-aided allostery mechanism for α-glucosidase by Xanthene-11v as an inhibitor using residue interaction network analysis

Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v.

The "violin model": Looking at community networks for dynamic allostery

Allosteric regulation of proteins continues to be an engaging research topic for the scientific community. Models describing allosteric communication have evolved from focusing on conformation-based descriptors of protein structural changes to appreciating the role of internal protein dynamics as a mediator of allostery. Here, we explain a "violin model" for allostery as a contemporary method for approaching the Cooper-Dryden model based on redistribution of protein thermal fluctuations. Based on graph theory, the violin model makes use of community network analysis to functionally cluster correlated protein motions obtained from molecular dynamics simulations. This Review provides the theory and workflow of the methodology and explains the application of violin model to unravel the workings of protein kinase A.

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

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.

Computational analyses reveal fundamental properties of the AT structure related to thrombosis

Summary

Blood coagulation is a vital process for humans and other species. Following an injury to a blood vessel, a cascade of molecular signals is transmitted, inhibiting and activating more than a dozen coagulation factors and resulting in the formation of a fibrin clot that ceases the bleeding. In this process, antithrombin (AT), encoded by the SERPINC1 gene is a key player regulating the clotting activity and ensuring that it stops at the right time. In this sense, mutations to this factor often result in thrombosis-the excessive coagulation that leads to the potentially fatal formation of blood clots that obstruct veins. Although this process is well known, it is still unclear why even single residue substitutions to AT lead to drastically different phenotypes. In this study, to understand the effect of mutations throughout the AT structure, we created a detailed network map of this protein, where each node is an amino acid, and two amino acids are connected if they are in close proximity in the three-dimensional structure. With this simple and intuitive representation and a machine-learning framework trained using genetic information from more than 130 patients, we found that different types of thrombosis have emerging patterns that are readily identifiable. Together, these results demonstrate how clinical features, genetic data and in silico analysis are converging to enhance the diagnosis and treatment of coagulation disorders.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

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.

CELSR1 variants are associated with partial epilepsy of childhood

CELSR1 gene, encoding cadherin EGF LAG seven-pass G-type receptor 1, is mainly expressed in neural stem cells during the embryonic period. It plays an important role in neurodevelopment. However, the relationship between CELSR1 and disease of the central nervous system has not been defined. In this study, we performed trios-based whole-exome sequencing in a cohort of 356 unrelated cases with partial epilepsy without acquired causes and identified CELSR1 variants in six unrelated cases. The variants included one de novo heterozygous nonsense variant, one de novo heterozygous missense variant, and four compound heterozygous missense variants that had one variant was located in the extracellular region and the other in the cytoplasm. The patients with biallelic variants presented severe epileptic phenotypes, whereas those with heterozygous variants were associated with a mild epileptic phenotype of benign epilepsy with centrotemporal spikes (BECTS). These variants had no or low allele frequency in the gnomAD database. The frequencies of the CELSR1 variants in this cohort were significantly higher than those in the control populations. The evidence from ClinGen Clinical-Validity Framework suggested a strong association between CELSR1 variants and epilepsy. These findings provide evidence that CELSR1 is potentially a candidate pathogenic gene of partial epilepsy of childhood.

A Machine Learning Framework Predicts the Clinical Severity of Hemophilia B Caused by Point-Mutations

Blood coagulation is a vital physiological mechanism to stop blood loss following an injury to a blood vessel. This process starts immediately upon damage to the endothelium lining a blood vessel, and results in the formation of a platelet plug that closes the site of injury. In this repair operation, an essential component is the coagulation factor IX (FIX), a serine protease encoded by the F9 gene and whose deficiency causes hemophilia B. If not treated by prophylaxis or gene therapy, patients with this condition are at risk of life-threatening bleeding episodes. In this sense, a deep understanding of the FIX protein and its activated form (FIXa) is essential to develop efficient therapeutics. In this study, we used well-studied structural analysis techniques to create a residue interaction network of the FIXa protein. Here, the nodes are the amino acids of FIXa, and two nodes are connected by an edge if the two residues are in close proximity in the FIXa 3D structure. This representation accurately captured fundamental properties of each amino acid of the FIXa structure, as we found by validating our findings against hundreds of clinical reports about the severity of HB. Finally, we established a machine learning framework named HemB-Class to predict the effect of mutations of all FIXa residues to all other amino acids and used it to disambiguate several conflicting medical reports. Together, these methods provide a comprehensive map of the FIXa protein architecture and establish a robust platform for the rational design of FIX therapeutics.

RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles

Residue interaction networks (RINs) are used to represent residue contacts in protein structures. Thanks to the advances in network theory, RINs have been proved effective as an alternative to coordinate data in the analysis of complex systems. The RING server calculates high quality and reliable non-covalent molecular interactions based on geometrical parameters. Here, we present the new RING 3.0 version extending the previous functionality in several ways. The underlying software library has been re-engineered to improve speed by an order of magnitude. RING now also supports the mmCIF format and provides typed interactions for the entire PDB chemical component dictionary, including nucleic acids. Moreover, RING now employs probabilistic graphs, where multiple conformations (e.g. NMR or molecular dynamics ensembles) are mapped as weighted edges, opening up new ways to analyze structural data. The web interface has been expanded to include a simultaneous view of the RIN alongside a structure viewer, with both synchronized and clickable. Contact evolution across models (or time) is displayed as a heatmap and can help in the discovery of correlating interaction patterns. The web server, together with an extensive help and tutorial, is available from URL: https://ring.biocomputingup.it/.

A Case Study of the Glycoside Hydrolase Enzyme Mechanism Using an Automated QM-Cluster Model Building Toolkit

Glycoside hydrolase enzymes are important for hydrolyzing the β-1,4 glycosidic bond in polysaccharides for deconstruction of carbohydrates. The two-step retaining reaction mechanism of Glycoside Hydrolase Family 7 (GH7) was explored with different sized QM-cluster models built by the Residue Interaction Network ResidUe Selector (RINRUS) software using both the wild-type protein and its E217Q mutant. The first step is the glycosylation, in which the acidic residue 217 donates a proton to the glycosidic oxygen leading to bond cleavage. In the subsequent deglycosylation step, one water molecule migrates into the active site and attacks the anomeric carbon. Residue interaction-based QM-cluster models lead to reliable structural and energetic results for proposed glycoside hydrolase mechanisms. The free energies of activation for glycosylation in the largest QM-cluster models were predicted to be 19.5 and 31.4 kcal mol⁻¹ for the wild-type protein and its E217Q mutant, which agree with experimental trends that mutation of the acidic residue Glu217 to Gln will slow down the reaction; and are higher in free energy than the deglycosylation transition states (13.8 and 25.5 kcal mol⁻¹ for the wild-type protein and its mutant, respectively). For the mutated protein, glycosylation led to a low-energy product. This thermodynamic sink may correspond to the intermediate state which was isolated in the X-ray crystal structure. Hence, the glycosylation is validated to be the rate-limiting step in both the wild-type and mutated enzyme.

Structural communication between the GTPase Sec4p and its activator Sec2p: Determinants of GEF activity and early deformations to nucleotide release

Ras GTPases are molecular switches that cycle between OFF and ON states depending on the bound nucleotide (i.e. GDP-bound and GTP-bound, respectively).

The Rab GTPase, Sec4p, plays regulatory roles in multiple steps of intracellular vesicle trafficking. Nucleotide release is catalyzed by the Guanine Nucleotide Exchange Factor (GEF) Sec2p.

Here, the integration of structural information with molecular dynamics (MD) simulations addressed a number of questions concerning the intrinsic and stimulated dynamics of Sec2p and Sec4p as well as the chain of structural deformations leading to GEF-assisted activation of the Rab GTPase.

Sec2p holds an intrinsic ability to adopt the conformation found in the crystallographic complexes with Sec4p, thus suggesting that the latter selects and shifts the conformational equilibrium towards a pre-existing bound-like conformation of Sec2p.

The anchoring of Sec4p to a suitable conformation of Sec2p favors the Sec2p-assisted pulling on itself of the α1/switch 1 (SWI) loop and of SWI, which loose any contact with GDP. Those deformations of Sec4p would occur earlier. Formation of the final Sec2p-Sec4p hydrophobic interface, accomplishes later. Disruption of the nucleotide cage would cause firstly loss of interactions with the guanine ring and secondly loss of interactions with the phosphates.

The ease in sampling the energy landscape and adopting a bound-like conformation likely favors the catalyzing ability of GEFs for Ras GTPases.

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.

CELSR3 variants are associated with febrile seizures and epilepsy with antecedent febrile seizures

Aims

To identify novel pathogenic gene of febrile seizures (FS)/epilepsy with antecedent FS (EFS+).

Methods

The trio‐based whole‐exome sequencing was performed in a cohort of 462 cases with FS/EFS+. Silico programs, sequence alignment, and protein modeling were used to predict the damaging of variants. Statistical testing was performed to analyze gene‐based burden of variants.

Results

Five heterozygous missense variants in CELSR3 were detected in five cases (families) with eight individuals (five females, three males) affected. Two variants were de novo, and three were identified in families with more than one individual affected. All the variants were predicted to be damaging in silico tools. Protein modeling showed that the variants resulted in disappearance of multiple hydrogen bonds and one disulfide bond, which potentially caused functional impairments of protein. The frequency of CELSR3 variants identified in this study was significantly higher than that in controls. All affected individuals were diagnosed with FS/EFS+, including six patients with FS and two patients with EFS+. All cases presented favorable outcomes without neurodevelopmental disorders.

Conclusions

CELSR3 variants are potentially associated with FS/EFS+.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.

Role of Position K+4 in the Phosphorylation and Dephosphorylation Reaction Kinetics of the CheY Response Regulator

Two-component signaling is a primary method by which microorganisms interact with their environments. A kinase detects stimuli and modulates autophosphorylation activity. The signal propagates by phosphotransfer from the kinase to a response regulator, eliciting a response. Response regulators operate over a range of time scales, corresponding to their related biological processes. Response regulator active site chemistry is highly conserved, but certain variable residues can influence phosphorylation kinetics. An Ala-to-Pro substitution (K+4, residue 113) in the Escherichia coli response regulator CheY triggers a constitutively active phenotype; however, the A113P substitution is too far from the active site to directly affect phosphochemistry. To better understand the activating mechanism(s) of the substitution, we analyzed receiver domain sequences to characterize the evolutionary role of the K+4 position. Although most featured Pro, Leu, Ile, and Val residues, chemotaxis-related proteins exhibited atypical Ala, Gly, Asp, and Glu residues at K+4. Structural and in silico analyses revealed that CheY A113P adopted a partially active configuration. Biochemical data showed that A113P shifted CheY toward a more activated state, enhancing autophosphorylation. By characterizing CheY variants, we determined that this functionality was transmitted through a hydrophobic network bounded by the β5α5 loop and the α1 helix of CheY. This region also interacts with the phosphodonor CheA_P1, suggesting that binding generates an activating perturbation similar to the A113P substitution. Atypical residues like Ala at the K+4 position likely serve two purposes. First, restricting autophosphorylation may minimize background noise generated by intracellular phosphodonors such as acetyl phosphate. Second, optimizing interactions with upstream partners may help prime the receiver domain for phosphorylation.

Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII

Hemophilia A is an X-linked inherited blood coagulation disorder caused by the production and circulation of defective coagulation factor VIII protein. People living with this condition receive either prophylaxis or on-demand treatment, and approximately 30% of patients develop inhibitor antibodies, a serious complication that limits treatment options. Although previous studies performed targeted mutations to identify important residues of FVIII, a detailed understanding of the role of each amino acid and their neighboring residues is still lacking. Here, we addressed this issue by creating a residue interaction network (RIN) where the nodes are the FVIII residues, and two nodes are connected if their corresponding residues are in close proximity in the FVIII protein structure. We studied the characteristics of all residues in this network and found important properties related to disease severity, interaction to other proteins and structural stability. Importantly, we found that the RIN-derived properties were in close agreement with in vitro and clinical reports, corroborating the observation that the patterns derived from this detailed map of the FVIII protein architecture accurately capture the biological properties of FVIII.

Dynamic Community Composition Unravels Allosteric Communication in PDZ3

The third domain of PSD-95 (PDZ3) is a model for investigating allosteric communication in protein and ligand interactions. While motifs contributing to its binding specificity have been scrutinized, a conformational dynamical basis is yet to be established. Despite the miniscule structural changes due to point mutants, the observed significant binding affinity differences have previously been assessed with a focus on two α-helices located at the binding groove (α₂) and the C-terminus (α₃). Here, we employ a new computational approach to develop a generalized view on the molecular basis of PDZ3 binding selectivity and interaction communication for a set of point mutants of the protein (G330T, H372A, G330T-H372A) and its ligand (CRIPT, named L₁, and its T-2F variant, L₂) along with the wild type (WT). To analyze the dynamical aspects hidden in the conformations that are produced by molecular dynamics simulations, we utilize variations in community composition calculated based on the betweenness centrality measure from graph theory. We find that the highly charged N-terminus, which is located far from the ligand, has the propensity to share the same community with the ligand in the biologically functional complexes, indicating a distal segment might mediate the binding dynamics. N- and C-termini of PDZ3 share communities, and α₃ acts as a hub for the whole protein by sustaining the communication with all structural segments, albeit being a trait not unique to the functional complexes. Moreover, α₂ which lines the binding cavity frequently parts communities with the ligand and is not a controller of the binding but is rather a slave to the overall dynamics coordinated by the N-terminus. Thus, ligand binding fate in PDZ3 is traced to the population of community compositions extracted from dynamics despite the lack of significant conformational changes.

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

Network theory-based approaches provide valuable insights into the variations in global structural connectivity between different dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain-based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational changes between the closed and partially open states of the SARS-CoV-2 spike protein. These conformational changes in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally, we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance drug design strategies.

Network analysis of synonymous codon usage

MOTIVATION

Most amino acids are encoded by multiple synonymous codons, some of which are used more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionarily conserved. Analyses of their positions in protein 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding.

RESULTS

We model protein structures as networks and use network centrality to measure the structural position of an amino acid. We first validate that amino acids buried within the structural core are network-central, and those on the surface are not. Then, we study potential differences between network centralities and thus structural positions of amino acids encoded by conserved rare, non-conserved rare and commonly used codons. We find that in 84% of proteins, the three codon categories occupy significantly different structural positions. We examine protein groups showing different codon centrality trends, i.e. different relationships between structural positions of the three codon categories. We see several cases of all proteins from our data with some structural or functional property being in the same group. Also, we see a case of all proteins in some group having the same property. Our work shows that codon usage is linked to the final protein structure and thus possibly to co-translational protein folding.

AVAILABILITY AND IMPLEMENTATION

https://nd.edu/∼cone/CodonUsage/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TK, Ahmad MN, Okashah SS, Kamaraj B, Al-Subaie AM, C. GPD, Zayed H. Deciphering the Role of Filamin B Calponin-Homology Domain in Causing the Larsen Syndrome, Boomerang Dysplasia, and Atelosteogenesis Type I Spectrum Disorders via a Computational Approach

Filamins (FLN) are a family of actin-binding proteins involved in regulating the cytoskeleton and signaling phenomenon by developing a network with F-actin and FLN-binding partners. The FLN family comprises three conserved isoforms in mammals: FLNA, FLNB, and FLNC. FLNB is a multidomain monomer protein with domains containing an actin-binding N-terminal domain (ABD 1-242), encompassing two calponin-homology domains (assigned CH1 and CH2). Primary variants in FLNB mostly occur in the domain (CH2) and surrounding the hinge-1 region. The four autosomal dominant disorders that are associated with FLNB variants are Larsen syndrome, atelosteogenesis type I (AOI), atelosteogenesis type III (AOIII), and boomerang dysplasia (BD). Despite the intense clustering of FLNB variants contributing to the LS-AO-BD disorders, the genotype-phenotype correlation is still enigmatic. In silico prediction tools and molecular dynamics simulation (MDS) approaches have offered the potential for variant classification and pathogenicity predictions. We retrieved 285 FLNB missense variants from the UniProt, ClinVar, and HGMD databases in the current study. Of these, five and 39 variants were located in the CH1 and CH2 domains, respectively. These variants were subjected to various pathogenicity and stability prediction tools, evolutionary and conservation analyses, and biophysical and physicochemical properties analyses. Molecular dynamics simulation (MDS) was performed on the three candidate variants in the CH2 domain (W148R, F161C, and L171R) that were predicted to be the most pathogenic. The MDS analysis results showed that these three variants are highly compact compared to the native protein, suggesting that they could affect the protein on the structural and functional levels. The computational approach demonstrates the differences between the FLNB mutants and the wild type in a structural and functional context. Our findings expand our knowledge on the genotype-phenotype correlation in FLNB-related LS-AO-BD disorders on the molecular level, which may pave the way for optimizing drug therapy by integrating precision medicine.

Identification of Uncharacterized Components of Prokaryotic Immune Systems and Their Diverse Eukaryotic Reformulations

Nucleotide-activated effector deployment, prototyped by interferon-dependent immunity, is a common mechanistic theme shared by immune systems of several animals and prokaryotes. Prokaryotic versions include CRISPR-Cas with the CRISPR polymerase domain, their minimal variants, and systems with second messenger oligonucleotide or dinucleotide synthetase (SMODS). Cyclic or linear oligonucleotide signals in these systems help set a threshold for the activation of potentially deleterious downstream effectors in response to invader detection. We establish such a regulatory mechanism to be a more general principle of immune systems, which can also operate independently of such messengers. Using sensitive sequence analysis and comparative genomics, we identify 12 new prokaryotic immune systems, which we unify by this principle of threshold-dependent effector activation. These display regulatory mechanisms paralleling physiological signaling based on 3'-5' cyclic mononucleotides, NAD⁺-derived messengers, two- and one-component signaling that includes histidine kinase-based signaling, and proteolytic activation. Furthermore, these systems allowed the identification of multiple new sensory signal sensory components, such as a tetratricopeptide repeat (TPR) scaffold predicted to recognize NAD⁺-derived signals, unreported versions of the STING domain, prokaryotic YEATS domains, and a predicted nucleotide sensor related to receiver domains. We also identify previously unrecognized invader detection components and effector components, such as prokaryotic versions of the Wnt domain. Finally, we show that there have been multiple acquisitions of unidentified STING domains in eukaryotes, while the TPR scaffold was incorporated into the animal immunity/apoptosis signal-regulating kinase (ASK) signalosome.IMPORTANCE Both prokaryotic and eukaryotic immune systems face the dangers of premature activation of effectors and degradation of self-molecules in the absence of an invader. To mitigate this, they have evolved threshold-setting regulatory mechanisms for the triggering of effectors only upon the detection of a sufficiently strong invader signal. This work defines general templates for such regulation in effector-based immune systems. Using this, we identify several previously uncharacterized prokaryotic immune mechanisms that accomplish the regulation of downstream effector deployment by using nucleotide, NAD⁺-derived, two-component, and one-component signals paralleling physiological homeostasis. This study has also helped identify several previously unknown sensor and effector modules in these systems. Our findings also augment the growing evidence for the emergence of key animal immunity and chromatin regulatory components from prokaryotic progenitors.

Identification of potential allosteric binding sites in cathepsin K based on intramolecular communication

Network theory methods and molecular dynamics (MD) simulations are accepted tools to study allosteric regulation. Indeed, dynamic networks built upon correlation analysis of MD trajectories provide detailed information about communication paths between distant sites. In this context, we aimed to understand whether the efficiency of intramolecular communication could be used to predict the allosteric potential of a given site. To this end, we performed MD simulations and network theory analyses in cathepsin K (catK), whose allosteric sites are well defined. To obtain a quantitative measure of the efficiency of communication, we designed a new protocol that enables the comparison between properties related to ensembles of communication paths obtained from different sites. Further, we applied our strategy to evaluate the allosteric potential of different catK cavities not yet considered for drug design. Our predictions of the allosteric potential based on intramolecular communication correlate well with previous catK experimental and theoretical data. We also discuss the possibility of applying our approach to other proteins from the same family.

A study on allosteric communication in U1A-snRNA binding interactions: network analysis combined with molecular dynamics data

The allosteric regulation during the binding interactions between small nuclear RNAs (snRNAs) and the associated protein factors is critical to the function of spliceosomes in alternative RNA splicing. Although network models combined with molecular dynamics simulations have shown to be powerful tools for the analysis of protein allostery, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems. In this work, we use a residual network model combined with a coarse-grained Gaussian network model (GNM) to investigate the binding interactions between the snRNA and the human U1A protein which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle, and to identify the residues that play an important role in the allosteric communication in U1A during this process. We also utilize the Girvan-Newman method to detect the structural organization in U1A-snRNA recognition and interactions. Our results reveal that: (Ι) not only the residues at the binding sites that are traditionally considered to play a major role in U1A-snRNA association, but those residues that are far away from the RNA binding interface participate in the U1A's allosteric signal transmission induced by the RNA binding; (Π) the structure of U1A protein is well organized with different communities acting different roles for its RNA binding and allosteric regulation. The study demonstrates that the combination of the residual network and elastic network models is an effective and efficient method which can be readily extended to the investigation of the allosteric communication for other macromolecular interaction systems.

Identification of Selective Novel Hits against Plasmodium falciparum Prolyl tRNA Synthetase Active Site and a Predicted Allosteric Site Using in silico Approaches

Recently, there has been increased interest in aminoacyl tRNA synthetases (aaRSs) as potential malarial drug targets. These enzymes play a key role in protein translation by the addition of amino acids to their cognate tRNA. The aaRSs are present in all Plasmodium life cycle stages, and thus present an attractive malarial drug target. Prolyl tRNA synthetase is a class II aaRS that functions in charging tRNA with proline. Various inhibitors against Plasmodium falciparum ProRS (PfProRS) active site have been designed. However, none have gone through clinical trials as they have been found to be highly toxic to human cells. Recently, a possible allosteric site was reported in PfProRS with two possible allosteric modulators: glyburide and TCMDC-124506. In this study, we sought to identify novel selective inhibitors targeting PfProRS active site and possible novel allosteric modulators of this enzyme. To achieve this, virtual screening of South African natural compounds against PfProRS and the human homologue was carried out using AutoDock Vina. The modulation of protein motions by ligand binding was studied by molecular dynamics (MD) using the GROningen MAchine for Chemical Simulations (GROMACS) tool. To further analyse the protein global motions and energetic changes upon ligand binding, principal component analysis (PCA), and free energy landscape (FEL) calculations were performed. Further, to understand the effect of ligand binding on the protein communication, dynamic residue network (DRN) analysis of the MD trajectories was carried out using the MD-TASK tool. A total of ten potential natural hit compounds were identified with strong binding energy scores. Binding of ligands to the protein caused observable global and residue level changes. Dynamic residue network calculations showed increase in betweenness centrality (BC) metric of residues at the allosteric site implying these residues are important in protein communication. A loop region at the catalytic domain between residues 300 and 350 and the anticodon binding domain showed significant contributions to both PC1 and PC2. Large motions were observed at a loop in the Z-domain between residues 697 and 710 which was also in agreement with RMSF calculations that showed increase in flexibility of residues in this region. Residues in this loop region are implicated in ATP binding and thus a change in dynamics may affect ATP binding affinity. Free energy landscape (FEL) calculations showed that the holo protein (protein-ADN complex) and PfProRS-SANC184 complexes were stable, as shown by the low energy with very few intermediates and hardly distinguishable low energy barriers. In addition, FEL results agreed with backbone RMSD distribution plots where stable complexes showed a normal RMSD distribution while unstable complexes had multimodal RMSD distribution. The betweenness centrality metric showed a loss of functional importance of key ATP binding site residues upon allosteric ligand binding. The deep basins in average L observed at the allosteric region imply that there is high accessibility of residues at this region. To further analyse BC and average L metrics data, we calculated the ΔBC and ΔL values by taking each value in the holo protein BC or L matrix less the corresponding value in the ligand-bound complex BC or L matrix. Interestingly, in allosteric complexes, residues located in a loop region implicated in ATP binding had negative ΔL values while in orthosteric complexes these residues had positive ΔL values. An increase in contact frequency between residues Ser263, Thr267, Tyr285, and Leu707 at the allosteric site and residues Thr397, Pro398, Thr402, and Gln395 at the ATP binding TXE loop was observed. In summary, this study identified five potential orthosteric inhibitors and five allosteric modulators against PfProRS. Allosteric modulators changed ATP binding site dynamics, as shown by RMSF, PCA, and DRN calculations. Changes in dynamics of the ATP binding site and increased contact frequency between residues at the proposed allosteric site and the ATP binding site may explain how allosteric modulators distort the ATP binding site and thus might inhibit PfProRS. The scaffolds of the identified hits in the study can be used as a starting point for antimalarial inhibitor development with low human cytotoxicity.

Regulation of MIF Enzymatic Activity by an Allosteric Site at the Central Solvent Channel

In proteins with multiple functions, such as macrophage migration inhibitory factor (MIF), the study of its intramolecular dynamic network can offer a unique opportunity to understand how a single protein is able to carry out several nonoverlapping functions. A dynamic mechanism that controls the MIF-induced activation of CD74 was recently discovered. In this study, the regulation of tautomerase activity was explored. The catalytic base Pro1 is found to form dynamic communications with the same allosteric node that regulates CD74 activation. Signal transmission between the allosteric and catalytic sites take place through intramolecular aromatic interactions and a hydrogen bond network that involves residues and water molecules of the MIF solvent channel. Once thought to be a consequence of trimerization, a regulatory function for the solvent channel is now defined. These results provide mechanistic insights into the regulation of catalytic activity and the role of solvent channel water molecules in MIF catalysis.

Residue-Level Allostery Propagates through the Effective Coarse-Grained Hessian

The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.

Homology Modeling and Virtual Screening Studies of Antigen MLAA-42 Protein: Identification of Novel Drug Candidates against Leukemia-An In Silico Approach

Monocytic leukemia-associated antigen-42 (MLAA-42) is associated with excessive cell division and progression of leukemia. Thus, human MLAA-42 is considered as a promising target for designing of new lead molecules for leukemia treatment. Herein, the 3D model of the target was generated by homology modeling technique. The model was then evaluated using various cheminformatics servers. Moreover, the virtual screening studies were performed to explore the possible binding patterns of ligand molecules to MLAA's active site pocket. Thirteen ligand molecules from the ChemBank™ database were identified as they showed good binding affinities, scaffold diversity, and preferential ADME properties which may act as potent drug candidates against leukemia. The study provides the way to identify novel therapeutics with optimal efficacy, targeting MLAA-42.

Mechanistic Insights into the Effect of Ligands on Structural Stability and Selectivity of Sulfotransferase 2A1 (SULT2A1)

Cytosolic sulfotransferases (SULTs) acting as phase II metabolic enzymes can be used in the sulfonation of small molecules by transferring a sulfonate group from the unique co-factor 3'-phosphoadenosine 5'-phosphosulfate (PAPS) to the substrates. In the present study, molecular dynamics (MD) simulations and ensemble docking study were employed to theoretically characterize the mechanism for the effect of co-factor (PAP) and ligands (LCA, raloxifene, α-hydroxytamoxifen, ouabain, and 3'-phosphoadenylyl sulfate) on structural stability and selectivity of SULT2A1 from the perspective of the dynamic behavior of SULT2A1 structures. Structural stability and network analyses indicated that the cooperation between PAP and LCA may enhance the thermal stability and compact communication in enzymes. During the MD simulations, the obviously rigid region and inward displacement were detected in the active-site cap (loop16) of the conformation containing PAP, which may be responsible for the significant changes in substrate accessibility and catalytic activity. The smaller substrates such as LCA could bind stably to the active pocket in the presence of PAP. However, the substrates or inhibitors with a large spatial structure needed to bind to the open conformation (without PAP) prior to PAPS binding.

Identification of potential leukocyte antigen-related protein (PTP-LAR) inhibitors through 3D QSAR pharmacophore-based virtual screening and molecular dynamics simulation

Owing to its negative regulatory role in insulin signaling, protein tyrosine phosphatase of leukocyte antigen-related protein (PTP-LAR) was widely thought as a potential drug target for diabetes. Now, it was urgent to search for potential LAR inhibitors targeting diabetes. Initially, the pharmacophore models of LAR inhibitors were established with the application of the HypoGen module. The cost analysis, test set validation, as well as Fischer's test was used to verify the efficiency of pharmacophore model. Then, the best pharmacophore model (Hypo-1-LAR) was applied for the virtual screening of the ZINC database. And 30 compounds met the Lipinski's rule of five. Among them, 10 compounds with better binding affinity than the known LAR inhibitor (BDBM50296375) were discovered by docking studies. Finally, molecular dynamics simulations and post-analysis experiments (RMSD, RMSF, PCA, DCCM and RIN) were conducted to explore the effect of ligands (ZINC97018474 and Compound 1) on LAR and preliminary understand why ZINC97018474 had better inhibitory activity than Compound 1 (BDBM50296375). Communicated by Ramaswamy H. Sarma.

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

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

Network analysis of dynamically important residues in protein structures mediating ligand-binding conformational changes

According to the generalized conformational selection model, ligand binding involves the co-existence of at least two conformers with different ligand-affinities in a dynamical equilibrium. Conformational transitions between them should be guaranteed by intramolecular vibrational dynamics associated to each conformation. These motions are, therefore, related to the biological function of a protein. Positions whose mutations are found to alter these vibrations the most can be defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. In a previous study, we have shown that these positions are evolutionarily conserved. They correspond to buried aliphatic residues mostly localized in regular structured regions of the protein like β-sheets and α-helices. In the present paper, we perform a network analysis of these key positions for a large dataset of paired protein structures in the ligand-free and ligand-bound form. We observe that networks of interactions between these key positions present larger and more integrated networks with faster transmission of the information. Besides, networks of residues result that are robust to conformational changes. Our results reveal that the conformational diversity of proteins seems to be guaranteed by a network of strongly interconnected key positions rather than individual residues.

Oxidative opening of the aromatic ring: Tracing the natural history of a large superfamily of dioxygenase domains and their relatives

A diverse collection of enzymes comprising the protocatechuate dioxygenases (PCADs) has been characterized in several extradiol aromatic compound degradation pathways. Structural studies have shown a relationship between PCADs and the more broadly-distributed, functionally enigmatic Memo domain linked to several human diseases. To better understand the evolution of this PCAD-Memo protein superfamily, we explored their structural and functional determinants to establish a unified evolutionary framework, identifying 15 clearly-delineable families, including a previously-underappreciated diversity in five Memo clade families. We place the superfamily's origin within the greater radiation of the nucleoside phosphorylase/hydrolase-peptide/amidohydrolase fold prior to the last universal common ancestor of all extant organisms. In addition to identifying active-site residues across the superfamily, we describe three distinct, structurally-variable regions emanating from the core scaffold often housing conserved residues specific to individual families. These were predicted to contribute to the active-site pocket, potentially in substrate specificity and allosteric regulation. We also identified several previously-undescribed conserved genome contexts, providing insight into potentially novel substrates in PCAD clade families. We extend known conserved contextual associations for the Memo clade beyond previously-described associations with the AMMECR1 domain and a radical S-adenosylmethionine family domain. These observations point to two distinct yet potentially overlapping contexts wherein the elusive molecular function of the Memo domain could be finally resolved, thereby linking it to nucleotide base and aliphatic isoprenoid modification. In total, this report throws light on the functions of large swaths of the experimentally-uncharacterized PCAD-Memo families.

Thermostability of Lipase A and Dynamic Communication Based on Residue Interaction Network

OBJECTIVE

Dynamic communication caused by mutation affects protein stability. The main objective of this study is to explore how mutations affect communication and to provide further insight into the relationship between heat resistance and signal propagation of Bacillus subtilis lipase (Lip A).

METHODS

The relationship between dynamic communication and Lip A thermostability is studied by long-time MD simulation and residue interaction network. The Dijkstra algorithm is used to get the shortest path of each residue pair. Subsequently, time-series frequent paths and spatio-temporal frequent paths are mined through an Apriori-like algorithm.

RESULTS

Time-series frequent paths show that the communication between residue pairs, both in wild-type lipase (WTL) and mutant 6B, becomes chaotic with an increase in temperature; however, more residues in 6B can maintain stable communication at high temperature, which may be associated with the structural rigidity. Furthermore, spatio-temporal frequent paths reflect the interactions among secondary structures. For WTL at 300K, β7, αC, αB, the longest loop, αA and αF contact frequently. The 310-helix between β3 and αA is penetrated by spatio-temporal frequent paths. At 400K, only αC can be frequently transmitted. For 6B, when at 300K, αA and αF are in more tight contact by spatio-temporal frequent paths though I157M and N166Y. Moreover, the rigidity of the active site His156 and the C-terminal of Lip A are increased, as reflected by the spatio-temporal frequent paths. At 400K, αA and αF, 310-helix between β3 and αA, the longest loop, and the loop where the active site Asp133 is located can still maintain stable communication.

CONCLUSION

From the perspective of residue dynamic communication, it is obviously found that mutations cause changes in interactions between secondary structures and enhance the rigidity of the structure, contributing to the thermal stability and functional activity of 6B.

Validation and quality assessment of macromolecular structures using complex network analysis

Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure.

Exploring allosteric communication in multiple states of the bacterial ribosome using residue network analysis

Antibiotic resistance is one of the most important problems of our era and hence the discovery of new effective therapeutics is urgent. At this point, studying the allosteric communication pathways in the bacterial ribosome and revealing allosteric sites/residues is critical for designing new inhibitors or repurposing readily approved drugs for this enormous machine. To shed light onto molecular details of the allosteric mechanisms, here we construct residue networks of the bacterial ribosomal complex at four different states of translation by using an effective description of the intermolecular interactions. Centrality analysis of these networks highlights the functional roles of structural components and critical residues on the ribosomal complex. High betweenness scores reveal pathways of residues connecting numerous sites on the structure. Interestingly, these pathways assemble highly conserved residues, drug binding sites, and known allosterically linked regions on the same structure. This study proposes a new residue-level model to test how distant sites on the molecular machine may be linked through hub residues that are critically located on the contact topology to inherently form communication pathways. Findings also indicate intersubunit bridges B1b, B3, B5, B7, and B8 as critical targets to design novel antibiotics.

Allosteric binding sites in Rab11 for potential drug candidates

Rab11 is an important protein subfamily in the RabGTPase family. These proteins physiologically function as key regulators of intracellular membrane trafficking processes. Pathologically, Rab11 proteins are implicated in many diseases including cancers, neurodegenerative diseases and type 2 diabetes. Although they are medically important, no previous study has found Rab11 allosteric binding sites where potential drug candidates can bind to. In this study, by employing multiple clustering approaches integrating principal component analysis, independent component analysis and locally linear embedding, we performed structural analyses of Rab11 and identified eight representative structures. Using these representatives to perform binding site mapping and virtual screening, we identified two novel binding sites in Rab11 and small molecules that can preferentially bind to different conformations of these sites with high affinities. After identifying the binding sites and the residue interaction networks in the representatives, we computationally showed that these binding sites may allosterically regulate Rab11, as these sites communicate with switch 2 region that binds to GTP/GDP. These two allosteric binding sites in Rab11 are also similar to two allosteric pockets in Ras that we discovered previously.

Visualization and analysis of non-covalent contacts using the Protein Contacts Atlas

Visualizations of biomolecular structures empower us to gain insights into biological functions, generate testable hypotheses, and communicate biological concepts. Typical visualizations (such as ball and stick) primarily depict covalent bonds. In contrast, non-covalent contacts between atoms, which govern normal physiology, pathogenesis, and drug action, are seldom visualized. We present the Protein Contacts Atlas, an interactive resource of non-covalent contacts from over 100,000 PDB crystal structures. We developed multiple representations for visualization and analysis of non-covalent contacts at different scales of organization: atoms, residues, secondary structure, subunits, and entire complexes. The Protein Contacts Atlas enables researchers from different disciplines to investigate diverse questions in the framework of non-covalent contacts, including the interpretation of allostery, disease mutations and polymorphisms, by exploring individual subunits, interfaces, and protein-ligand contacts and by mapping external information. The Protein Contacts Atlas is available at http://www.mrc-lmb.cam.ac.uk/pca/ and also through PDBe.