Analyzing and visualizing residue networks of protein structures

PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein-protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein-protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility. The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.

Mechanistic insights into the conformational changes and alterations in residual communications due to the mutations in the pncA Gene of Mycobacterium tuberculosis: A computational perspective for effective therapeutic solutions

Due to its emerging resistance to first-line anti-TB medications, tuberculosis (TB) is one of the most contagious illness in the world. According to reports, the effectiveness of treating TB is severely impacted by drug resistance, notably resistance caused by mutations in the pncA gene-encoded pyrazinamidase (PZase) to the front-line drug pyrazinamide (PZA). The present study focused on investigating the resistance mechanism caused by the mutations D12N, T47A, and H137R to better understand the structural and molecular events responsible for the resistance acquired by the pncA gene of Mycobacterium tuberculosis (MTB) at the structural level. Bioinformatics analysis predicted that all three mutations were deleterious and located near the active centre of the pncA, affecting its functional activity. Furthermore, molecular dynamics simulation (MDS) results established that mutations significantly reduced the structural stability and caused the rearrangement of FE2+ in the active centre of pncA. Moreover, essential dynamics analysis, including principal component analysis (PCA) and free energy landscape (FEL), concluded variations in the protein motion and decreased conformational space in the mutants. Additionally, the mutations potentially impacted the network topologies and altered the residual communications in the network. The complex simulation study results established the significant movement of the flap region from the active centre of mutant complexes, further supporting the flap region's significance in developing resistance to the PZA drug. This study advances our knowledge of the primary cause of the mechanism of PZA resistance and the structural dynamics of pncA mutants, which will help us to design new and potent chemical scaffolds to treat drug-resistant TB (DR-TB).

The influence of model building schemes and molecular dynamics sampling on QM-cluster models: the chorismate mutase case study

Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which limits comparison to in vivo structure and mechanism. The active site of chorismate mutase from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used as a case study to guide construction of QM-cluster models built first from the X-ray crystal structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network ResidUe Selector (RINRUS) software toolkit, developed by our group to simplify and automate the construction of QM-cluster models, is expanded to handle MD to QM-cluster model workflows. Several options, some employing novel topological clustering from residue interaction network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with density functional theory (DFT). The 250 QM-cluster models sampled provide a mean ΔG‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4 kcal mol-1 at 25 °C. While the difference between theory and experiment is consequential, the level of theory used is modest and therefore "chemical" accuracy is unexpected. More important are the comparisons made between QM-cluster models designed from the X-ray crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models, rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the field of computational enzymology using the model construction framework afforded with the RINRUS software toolkit.

Protein O-Fucosyltransferase Is Required for the Efficient Invasion of Hepatocytes by Plasmodium berghei Sporozoites

The O-fucosylation of the thrombospondin type I repeat (TSR) domain is important for TSR-containing proteins' optimal folding and stability. However, the importance of Plasmodium O-fucosyltransferase 2 (POFut2) remains unclear due to two different reports. Here, we disrupted the POFut2 gene in Plasmodium berghei and demonstrated that POFut2 KO parasites develop normally in blood and mosquito stages but show reduced infectivity in mice. We found that the reduced infectivity of POFut2 KO sporozoites was due to a diminished level of TRAP that affected the parasite gliding motility and hepatocyte infectivity. Using all-atom MD simulation, we also hypothesize that O-fucosylation impacts the TSR domain's stability more than its heparin binding capacity.

GEMIN4 Variants: Risk Profiling, Bioinformatics, and Dynamic Simulations Uncover Susceptibility to Bladder Carcinoma

BACKGROUND

The relationship between GEMIN4 genetic variants and cancer, especially bladder carcinoma (BLCA), has been explored without conclusive results. This study aims to elucidate the link between GEMIN4 polymorphisms and BLCA susceptibility through genetic analyses, bioinformatics, and molecular dynamics (MD) simulations.

METHODS

A cohort of 249 participants (121 BLCA patients and 128 unrelated controls) was enrolled. PCR was employed for allelic discrimination of GEMIN4 variants, followed by subgroup stratification, haplotype analyses, structural prediction using the AlphaFold2 prediction tool, subsequent MD simulations, structural analysis, and residue interaction mapping using Desmond, UCSF ChimeraX, and Cytoscape softwares.

RESULTS

The rs.2740348*G variant demonstrated a protective role against BLCA in allelic (OR = 0.55, p = 0.002) and recessive (OR = 0.54, p = 0.017) models, whereas the rs.7813*T variant increased BLCA risk under the recessive model (OR = 1.90, p = 0.019). Haplotype analysis revealed a significant association between GEMIN4 haplotype (rs.2740348*C/rs.7813*T) with increased BLCA risk (OR = 2.01, p = 0.004). Univariate analysis revealed associations of the variants with albumin levels and absolute neutrophil count in BLCA patients. Pathogenicity evaluation categorized p.Gln450Glu as neutral and p.Arg1033Cys as deleterious. MD simulations revealed structural alterations and conformational shifts in the GEMIN4 protein induced by the Glu450 and Cys1033 mutations.

CONCLUSIONS

The study highlights the dual role of GEMIN4 variants in BLCA susceptibility, with rs.2740348 conferring protection and rs.7813 increasing risk. The Glu450 residue positively impacted protein stability, while Cys1033 had a detrimental effect on protein function. These findings underscore the significance of GEMIN4 variants in BLCA susceptibility and pave the way for future diagnostic and therapeutic initiatives.

Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.

In Silico Design and Evaluation of a Novel Therapeutic Agent Against the Spike Protein as a Novel Treatment Strategy for COVID-19 Treatment

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that is associated with severe damage to other human organs. It causes by a novel coronavirus, and it is spreading all over the world. To date, there is some approved vaccine or therapeutic agent which could be effective against this disease. But their effectiveness against mutated strains is not studied completely. The spike glycoprotein on the surface of the coronaviruses gives the virus the ability to bind to host cell receptors and enter cells. Inhibition of attachment of these spikes can lead to virus neutralization by inhibiting viral entrance.

AIMS

In this study, we tried to use the virus entrance strategy against itself by utilizing virus receptor (ACE-2) in order to design an engineered protein consisting of a human Fc antibody fragment and a part of ACE-2, which reacts with virus RBD, and we also evaluated this interaction by computational methods and in silico methods. Subsequently, we have designed a new protein structure to bind with this site and inhibit the virus from attaching to its cell receptor, mechanically or chemically.

METHODS

Various in silico software, bioinformatics, and patent databases were used to retrieve the requested gene and protein sequences. The physicochemical properties and possibility of allergenicity were also examined. Three-dimensional structure prediction and molecular docking were also performed to develop the most suitable therapeutic protein.

RESULTS

The designed protein consisted of a total of 256 amino acids with a molecular weight of 28984.62 and 5.92 as a theoretical isoelectric point. Instability and aliphatic index and grand average of hydropathicity are 49.99, 69.57 and -0.594, respectively.

CONCLUSIONS

In silico studies can provide a good opportunity to study viral proteins and new drugs or compounds since they do not need direct exposure to infectious agents or equipped laboratories. The suggested therapeutic agent should be further characterized in vitro and in vivo.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABBREVIATIONS

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.

The Glycine N-Methyltransferase Case Study: Another Challenge for QM-Cluster Models?

The methyl transfer reaction between SAM and glycine catalyzed by glycine N-methyltransferase (GNMT) was examined using QM-cluster models generated by Residue Interaction Network ResidUe Selector (RINRUS). RINRUS is a Python-based tool that can build QM-cluster models with rules-based processing of the active site residue interaction network. This way of enzyme model-building allows quantitative analysis of residue and fragment contributions to kinetic and thermodynamic properties of the enzyme. Many residue fragments are important for the GNMT catalytic reaction, such as Gly137, Asn138, and Arg175, which interact with the glycine substrate, and Trp30, Asp85, and Tyr242, which interact with the SAM cofactor. Our study shows that active site fragments that interact with the glycine substrate and the SAM cofactor must both be included in the QM-cluster models. Even though the proposed mechanism is a simple one-step reaction, GNMT may be a rather challenging case study for QM-cluster models because convergence in energetics requires models with >350 atoms. "Maximal" QM-cluster models built with either qualitative contact count ranking or quantitative interaction energies from functional group symmetry adapted perturbation theory provide acceptable results. Hence, important residue fragments that contribute to the energetics of the methyl-transfer reaction in GNMT are correctly identified in the RIN. Observations from this work suggest new directions to better establish an effective approach for constructing atomic-level enzyme models.

PyPCN: protein contact networks in PyMOL

MOTIVATION

Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations.

RESULTS

PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/pcnproject/PyPCN.

Biophysical Rationale for the Selective Inhibition of PTP1B over TCPTP by Nonpolar Terpenoids

Protein tyrosine phosphatases (PTPs) are emerging drug targets for many diseases, including cancer, autoimmunity, and neurological disorders. A high degree of structural similarity between their catalytic domains, however, has hindered the development of selective pharmacological agents. Our previous research uncovered two unfunctionalized terpenoid inhibitors that selectively inhibit PTP1B over T-cell PTP (TCPTP), two PTPs with high sequence conservation. Here, we use molecular modeling, with supporting experimental validation, to study the molecular basis of this unusual selectivity. Molecular dynamics (MD) simulations suggest that PTP1B and TCPTP share a h-bond network that connects the active site to a distal allosteric pocket; this network stabilizes the closed conformation of the catalytically essential WPD loop, which it links to the L-11 loop and neighboring α3 and α7 helices on the other side of the catalytic domain. Terpenoid binding to either of two proximal C-terminal sites─an α site and a β site─can disrupt the allosteric network; however, binding to the α site forms a stable complex only in PTP1B. In TCPTP, two charged residues disfavor binding at the α site in favor of binding at the β site, which is conserved between the two proteins. Our findings thus indicate that minor amino acid differences at the poorly conserved α site enable selective binding, a property that might be enhanced with chemical elaboration, and illustrate more broadly how minor differences in the conservation of neighboring─yet functionally similar─allosteric sites can affect the selectivity of inhibitory scaffolds (e.g., fragments).

Structural impact of pathogenic SNPs on β-tubulin using molecular dynamics study

Single nucleotide polymorphisms (SNPs) in the TUBB1 (β-tubulin) gene have been implicated as the primary cause of macro thrombocytopenia. Therefore it is essential to identify the potential SNPs which are harmful to cause diseases such as macro thrombocytopenia. The impact caused by these variants on β-tubulin is twofold, both structural and functional. Multiple in-silico tools were used to scrutinise the most deleterious nsSNPs (non-synonymous SNPs) via sequence and structure-based approaches. Further, the β-tubulin protein model incorporating identified mutants was subjected to MD (molecular dynamic) simulations to analyse the impact on protein structure. A total of 2974 SNPs of TUBB1 were retrieved from various sources, and 32 nsSNPs were identified. By screening through sequence-based technique, 13 variants were detected as deleterious and further structure-based filtration was carried out to find thermally destabilising variants. Finally, three variants have been detected as highly destabilising by the mCSM server and chosen for the MD study. All three variants are present in the N-terminal, Intermediate, and C-terminal regions, breaking the spatial arrangement required for microtubule assembly. The spatial arrangement of these variants is in deviation with respect to WT (wild type) β-tubulin. The protein model was subjected to a simulation period of 100 ns. The FEL analysis revealed multiple clusters with minor populations indicating the unstable conformation adapted by the β-tubulin. The normal mode vector analysis exhibited high-intensity flexible motions at the C-terminal end, responsible for binding with MAPs (microtubule-associated proteins), an essential region in microtubule assembly. All these results reveal that the SNP's predicted eventually influence the spatial arrangement of β-tubulin, which would disturb the stacking arrangement of αβ tubulin dimer in microtubule assembly. The present study may set a path to cure the diseases like macro thrombocytopenia.Communicated by Ramaswamy H. Sarma.

AQcalc: A web server that identifies weak molecular interactions in protein structures

Weak molecular interactions play an important role in protein structure and function. Computational tools that identify weak molecular interactions are, therefore, valuable for the study of proteins. Here, we present AQcalc, a web server (https://aqcalcbiocomputing.com/) that can be used to identify anion-quadrupole (AQ) interactions, which are weak interactions involving aromatic residue (Trp, Tyr, and Phe) ring edges and anions (Asp, Glu, and phosphate ion) both within proteins and at their interfaces (protein-protein, protein-nucleic acids, and protein-lipid bilayer). AQcalc identifies AQ interactions as well as clusters involving AQ, cation-π, and salt bridges, among others. Utilizing AQcalc we analyzed weak interactions in protein models, even in the absence of experimental structures, to understand the contributions of weak interactions to deleterious structural changes, including those associated with oncogenic and germline disease variants. We identified several deleterious variants with disrupted AQ interactions (comparable in frequency to cation-π disruptions). Amyloid fibrils utilize AQ to bury anions at frequencies that far exceed those observed for globular proteins. AQ interactions were detected three and five times more frequently than the hydrogen-bonded AQ (HBAQ) in fibril structures and protein-lipid bilayer interfaces, respectively. By contrast, AQ and HBAQ interactions were detected with similar frequencies in globular proteins. Collectively, these findings suggest AQcalc will be effective in facilitating fine structural analysis. As other web utilities designed to identify protein residue interaction networks do not report AQ interactions, wide use of AQcalc will enrich our understanding of residue interaction networks and facilitate hypothesis testing by identifying and experimentally characterizing these comparably weak but important interactions.

Design, synthesis, activity and molecular dynamics studies of 1,3,4-thiadiazole derivatives as selective allosteric inhibitors of SHP2 for the treatment of cancer

Overexpression or gene mutation of SHP2 is closely linked with a variety of cancers and has been identified as a crucial anticancer target. In the study, we took SHP2 allosteric inhibitor SHP099 as the lead compound, and 32 1,3,4-thiadiazole derivatives were identified as selective allosteric inhibitors of SHP2. In vitro enzyme activity test showed that some compounds had high inhibition on full length SHP2, and almost no activity on homologous protein SHP1, exhibiting high selectivity. Compound YF704 (4w) had the best inhibition activity, with IC₅₀ value of 0.25 ± 0.02 μM, and also showed strong inhibitory activity on SHP2-E76K and SHP2-E76A, with IC₅₀ values of 6.88 ± 0.69 μM and 1.38 ± 0.12 μM, respectively. CCK8 proliferation test found that multiple compounds would effectively inhibit the proliferation of a variety of cancer cells. Among them, the IC₅₀ values of compound YF704 on MV4-11 and NCI-H358 cells were 3.85 ± 0.34 μM and 12.01 ± 0.62 μM, respectively. Specially, these compounds were sensitive to NCI-H358 cells containing KRAS^G12C mutation, thus overcoming the problem that SHP099 was insensitive to such cells. Apoptosis experiment showed that compound YF704 would effectively induce apoptosis of MV4-11 cells. Western blot showed that compound YF704 would downregulate the phosphorylation levels of Erk1/2 and Akt in MV4-11 and NCI-H358 cells. Molecular docking study show that compound YF704 would effectively bind to the allosteric region of SHP2 and form hydrogen bond interactions with key residues Thr108, Arg111 and Phe113. Molecular dynamics study further revealed the binding mechanism of SHP2 and compound YF704. In conclusion, we hope to provide potential SHP2 selective inhibitors and provide valuable clues for cancer treatment.

C-terminal lysine residues enhance plasminogen activation by inducing conformational flexibility and stabilization of activator complex of staphylokinase with plasmin

Staphylokinase (SAK), a potent fibrin-specific plasminogen activator secreted by Staphylococcus aureus, carries a pair of lysine at the carboxy-terminus that play a key role in plasminogen activation. The underlaying mechanism by which C-terminal lysins of SAK modulate its function remains unknown. This study has been undertaken to unravel role of C-terminal lysins of SAK in plasminogen activation. While deletion of C-terminal lysins (Lys135, Lys136) drastically impaired plasminogen activation by SAK, addition of lysins enhanced its catalytic activity 2-2.5-fold. Circular dichroism analysis revealed that C-terminally modified mutants of SAK carry significant changes in their beta sheets and secondary structure. Structure models and RING (residue interaction network generation) studies indicated that the deletion of lysins has conferred extensive topological alterations in SAK, disrupting vital interactions at the interface of SAK.plasmin complex, thereby leading significant impairment in its functional activity. In contrast, addition of lysins at the C-terminus enhanced its conformational flexibility, creating a stronger coupling at the interface of SAK.plasmin complex and making it more efficient for plasminogen activation. Taken together, these studies provided new insights on the role of C-terminal lysins in establishment of precise intermolecular interactions of SAK with the plasmin for the optimal function of activator complex.

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.

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.

A biophysical rationale for the selective inhibition of PTP1B over TCPTP by nonpolar terpenoids

Protein tyrosine phosphatases (PTPs) are emerging drug targets for many diseases, including type 2 diabetes, obesity, and cancer. However, a high degree of structural similarity between the catalytic domains of these enzymes has made the development of selective pharmacological inhibitors an enormous challenge. Our previous research uncovered two unfunctionalized terpenoid inhibitors that selectively inhibit PTP1B over TCPTP, two PTPs with high sequence conservation. Here, we use molecular modeling with experimental validation to study the molecular basis of this unusual selectivity. Molecular dynamics (MD) simulations indicate that PTP1B and TCPTP contain a conserved h-bond network that connects the active site to a distal allosteric pocket; this network stabilizes the closed conformation of the catalytically influential WPD loop, which it links to the L-11 loop and α 3 and α 7 helices-the C-terminal side of the catalytic domain. Terpenoid binding to either of two proximal allosteric sites-an α site and a β site-can disrupt the allosteric network. Interestingly, binding to the α site forms a stable complex with only PTP1B; in TCPTP, where two charged residues disfavor binding at the α site, the terpenoids bind to the β site, which is conserved between the two proteins. Our findings indicate that minor amino acid differences at the poorly conserved α site enable selective binding, a property that might be enhanced with chemical elaboration, and illustrate, more broadly, how minor differences in the conservation of neighboring-yet functionally similar-allosteric sites can have very different implications for inhibitor selectivity.

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.

Residue interaction network and molecular dynamics simulation study on the binding of S239D/I332E Fc variant with enhanced affinity to FcγRIIIa receptor

Engineering of Fc has been adapted as an efficient method for enhanced or reduced affinity towards Fc receptors in the development of therapeutic antibodies. S239D/I332E mutation of Fc induces approximately two logs greater affinity to the FcγRIIIa receptor and has been extensively employed in various Fc engineering studies. It is known that the mutation gives rise to the formation of salt bridges between the mutated residues of Fc and FcγRIIIa, but the overall effect of the mutation in the binding interface of the Fc-FcγRIIIa complex is still unclear. In this study, the molecular interactions in the binding interface of mutant Fc and FcγRIIIa were analyzed and compared with those of wild-type Fc binding through residue interaction network (RIN) analysis and molecular dynamics (MD) simulation. RIN analysis identified specific molecular interactions and Hub residues in the interfaces, and their numbers were increased by introducing the mutation, with maintaining most of the molecular interactions in the wild-type complex. MD simulation study revealed that the numbers of stable electrostatic interactions and stable Hub residues in the mutant complex were higher than those in the wild-type complex. The introduced mutations were shown to form further charge-charge attractive interactions in addition to the identified salt bridges without generating any repulsive interactions. These results are expected to provide further structural insight into Fc variants' design based on the S239D/I332E mutation.

Deciphering the structural and functional impact of missense mutations in Egr1-DNA interacting interface: an integrative computational approach

Early growth response-1 (Egr1) is a zinc-finger transcription factor that plays a critical role in controlling cell growth, proliferation, differentiation, angiogenesis, and apoptosis. Egr1 is induced by many growth factors, cytokines, and stress signals and is also known to be involved in several pathological conditions like cancer, neurological and ocular disorders. The DNA binding domain of Egr1 is a highly conserved Cys2His2 (C2H2) zinc finger (ZNF) domain which specifically binds to GC-rich consensus sequence GcG (G/T) GGGCG and activates transcription. As the C2H2 domain specifically recognizes its DNA target, the mutations spanning this region shall perturb DNA recognition and may hinder transcription of target genes. Therefore, in this study, the missense mutations occurring specifically at the DNA binding domain (DBD) of Egr1 were probed by computational approaches involving in silico screening of pathogenic and functional mutants coupled with extensive molecular dynamics simulations, to determine the mutants that affect its structural stability and interactions with DNA. From the pathogenicity analysis of 38 missense mutations spanning Egr1-DBD, 17 were predicted as pathogenic, and 7 amongst these were found to have functional impact on Egr1. On combined analysis of molecular dynamics simulation, Residue interaction analysis and Egr1-DNA interaction analysis results, the mutants R371C and R375C showed least impact, whilst, H382R tend to increase the structural stability, whereas R360H, H390R, E393V, and H414Y conferred greater impact by altering the structural stability and DNA interactions. Hence, this study exposes the prospects of considering these 4 deleterious mutations for clinical significance, but needs further experimental validation.HighlightsEgr1's DNA binding domain is a highly conserved Cys2His2 (C2H2) zinc finger domain that specifically recognizes its DNA target.Mutations spanning in the DNA binding domain shall perturb DNA recognition and may hinder transcription.Among the missense mutations, mutants R360H, H390R, E393V, and H414Y were inferred to have a greater impact on Egr1 by altering the structural stability and DNA interactions.

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.

Allosteric Inhibition of PTP1B by a Nonpolar Terpenoid

Protein tyrosine phosphatases (PTPs) are promising drug targets for treating a wide range of diseases such as diabetes, cancer, and neurological disorders, but their conserved active sites have complicated the design of selective therapeutics. This study examines the allosteric inhibition of PTP1B by amorphadiene (AD), a terpenoid hydrocarbon that is an unusually selective inhibitor. Molecular dynamics (MD) simulations carried out in this study suggest that AD can stably sample multiple neighboring sites on the allosterically influential C-terminus of the catalytic domain. Binding to these sites requires a disordered α7 helix, which stabilizes the PTP1B-AD complex and may contribute to the selectivity of AD for PTP1B over TCPTP. Intriguingly, the binding mode of AD differs from that of the most well-studied allosteric inhibitor of PTP1B. Indeed, biophysical measurements and MD simulations indicate that the two molecules can bind simultaneously. Upon binding, both inhibitors destabilize the α7 helix by disrupting interactions at the α3-α7 interface and prevent the formation of hydrogen bonds that facilitate closure of the catalytically essential WPD loop. These findings indicate that AD is a promising scaffold for building allosteric inhibitors of PTP1B and illustrate, more broadly, how unfunctionalized terpenoids can engage in specific interactions with protein surfaces.

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.

d-StructMAn: Containerized structural annotation on the scale from genetic variants to whole proteomes

Background

Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution.

Results

We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins.

Conclusions

d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.

Design, construction and in vivo functional assessment of a hinge truncated sFLT01

Gene therapy for the treatment of ocular neovascularization has reached clinical trial phases. The AAV2-sFLT01 construct was already evaluated in a phase 1 open-label trial administered intravitreally to patients with advanced neovascular age-related macular degeneration. SFLT01 protein functions by binding to VEGF and PlGF molecules and inhibiting their activities simultaneously. It consists of human VEGFR1/Flt-1 (hVEGFR1), a polyglycine linker, and the Fc region of human IgG1. The IgG1 upper hinge region of the sFLT01 molecule makes it vulnerable to radical attacks and prone to causing immune reactions. This study pursued two goals: (i) minimizing the immunogenicity and vulnerability of the molecule by designing a truncated molecule called htsFLT01 (hinge truncated sFLT01) that lacked the IgG1 upper hinge and lacked 2 amino acids from the core hinge region; and (ii) investigating the structural and functional properties of the aforesaid chimeric molecule at different levels (in silico, in vitro, and in vivo). Molecular dynamics simulations and molecular mechanics energies combined with Poisson-Boltzmann and surface area continuum solvation calculations revealed comparable free energy of binding and binding affinity for sFLT01 and htsFLT01 to their cognate ligands. Conditioned media from human retinal pigment epithelial (hRPE) cells that expressed htsFLT01 significantly reduced tube formation in HUVECs. The AAV2-htsFLT01 virus suppressed vascular development in the eyes of newborn mice. The htsFLT01 gene construct is a novel anti-angiogenic tool with promising improvements compared to existing treatments.

Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena which play a crucial role in maintaining normal cellular physiology, and dysregulation of this protein has been reported to induce oncogenic transformation of the cells. However, despite its key role in many cellular processes, the complete structure of human TTF1 has not been elucidated to date, neither experimentally nor computationally. Therefore, understanding the structure of human TTF1 is crucial for studying its functions and interactions with other cellular factors. The aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the Protein Data Bank (PDB), the complete structure of human TTF1 was constructed by ab initio modelling. The structural stability was determined with molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The frequently occurring conformation of human TTF1 was selected by trajectory clustering, and the central residues of this conformation were determined by centrality analyses of the Residue Interaction Network (RIN) of TTF1. Two residue clusters, one in the oligomerization domain and other in the C-terminal domain, were found to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of this essential mammalian protein, and the results obtained herein will provide structural insights for future research including that in cancer biology and related studies.Communicated by Ramaswamy H. Sarma.

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.

C-Mannosyltransferase Is Essential for Malaria Transmission in Plasmodium berghei

C-Mannosylation of the thrombospondin type I repeat (TSR) domains is one of the most important factors involved in their function. It occurs on the first tryptophan of the WXXWXXC conserved motif where the tryptophan is usually surrounded by arginine or lysine forming the ligand-binding stretch of this sticky domain. It is found in its canonical or modified forms in many Plasmodium proteins. TSR containing proteins such as thrombospondin-like anonymous protein (TRAP), circumsporozoite protein (CSP), CSP and TRAP related protein (CTRP), and secreted protein with altered thrombospondin repeat (SPATR) have all been shown to be important for various parasite processes and life cycle stages. Here, we show that C-mannosylation catalyzing enzyme C-mannosyltransferase (CmanT) plays an essential role in malaria transmission in Plasmodium berghei. Disruption of the CmanT does not affect asexual blood stage propagation or gametocyte development but abolishes the formation of oocysts in mosquitoes. CmanT knockout (CmanT^-) parasites showed normal ookinete formation; however, these ookinetes failed in their ability to glide. CmanT^- was complemented by reintroducing the gene, restoring mosquito transmission to wild-type level. We also investigated the effect of C-mannosylation on the folding and heparin-binding capacity of the Plasmodium falciparum TRAP TSR domain in silico, which suggested that this phenotype should be due to its involvement in the global stabilization of TSR residue side chain interactions.

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.

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.

Theta-Defensins to Counter COVID-19 as Furin Inhibitors: In Silico Efficiency Prediction and Novel Compound Design

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was characterized as a pandemic by the World Health Organization (WHO) in Dec. 2019. SARS-CoV-2 binds to the cell membrane through spike proteins on its surface and infects the cell. Furin, a host-cell enzyme, possesses a binding site for the spike protein. Thus, molecules that block furin could potentially be a therapeutic solution. Defensins are antimicrobial peptides that can hypothetically inhibit furin because of their arginine-rich structure. Theta-defensins, a subclass of defensins, have attracted attention as drug candidates due to their small size, unique structure, and involvement in several defense mechanisms. Theta-defensins could be a potential treatment for COVID-19 through furin inhibition and an anti-inflammatory mechanism. Note that inflammatory events are a significant and deadly condition that could happen at the later stages of COVID-19 infection. Here, the potential of theta-defensins against SARS-CoV-2 infection was investigated through in silico approaches. Based on docking analysis results, theta-defensins can function as furin inhibitors. Additionally, a novel candidate peptide against COVID-19 with optimal properties regarding antigenicity, stability, electrostatic potential, and binding strength was proposed. Further in vitro/in vivo investigations could verify the efficiency of the designed novel peptide.

Known allosteric proteins have central roles in genetic disease

Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, contribute to genetic disease and comorbidities much more than non-allosteric proteins, and there is an association between being allosteric and involvement in disease; 2) they are enriched in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system; 3) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 4) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and less due to their dynamical properties.

Identification of potential target endoribonuclease NSP15 inhibitors of SARS-COV-2 from natural products through high-throughput virtual screening and molecular dynamics simulation

SARS‐CoV‐2 wreaks havoc around the world, triggering the COVID‐19 pandemic. It has been confirmed that the endoribonuclease NSP15 is crucial to the viral replication, and thus identified as a potential drug target against COVID‐19. The NSP15 protein was used as the target to conduct high‐throughput virtual screening on 30,926 natural products from the NPASS database to identify potential NSP15 inhibitors. And 100 ns molecular dynamics simulations were performed on the NSP15 and NSP15‐NPC198199 system. In all, 10 natural products with high docking scores with NSP15 protein were obtained, among which compound NPC198199 scored the highest. The analysis of the binding mode between NPC198199 and NSP15 found that NPC198199 would form H‐bond interactions with multiple key residues at the catalytic site. Subsequently, a series of post‐dynamics simulation analyses (including RMSD, RMSF, PCA, DCCM, RIN, binding free energy, and H‐bond occupancy) were performed to further explore inhibitory mechanism of compound NPC198199 on NSP15 protein at the molecular level. The research strongly indicates that the 10 natural compounds screened can be used as potential inhibitors of NSP15, and provides valuable information for the subsequent drug discovery of anti‐SARS‐CoV‐2.

Antimicrobial Peptides from Human Microbiome Against Multidrug Efflux Pump of Pseudomonas aeruginosa: a Computational Study

The excess use of antibiotics has led to the evolution of multidrug-resistant pathogenic strains causing worldwide havoc. These multidrug-resistant strains require potent inhibitors. Pseudomonas aeruginosa is a lead cause of nosocomial infections and also feature in the critical priority list of the world health organization (WHO) for the development of new antibiotics against their antimicrobial resistance. Antimicrobial peptides (AMPs) found in almost every life form from microorganisms to humans are known to defend their hosts against various pathogens. Owing to the diversity of the human microbiome, in this study, we have identified the cell-penetrating AMPs from the human microbiome and studied their inhibitory activity against the outer membrane protein OprM of the MexAB-OprM, a constitutively expressed multidrug efflux pump of the Ps. aeruginosa. Screening of the AMPs from the human microbiome resulted in the identification of 147 cell-penetrating AMPs (CPAMPs). The virtual screening of these CPAMPs against the OprM protein showed significant inhibitory results with the top docked AMP showing binding affinity exceeding -30 kcal/mol. The molecular dynamic simulation determined the interaction stabilities between the AMPs and the OprM at the binding site. Further, the residue interaction networks (RINs) are analyses to identify the inhibitory patterns. Later, these patterns were confirmed by MM-PBSA analysis suggesting that the AMPs are majorly stabilized by electrostatic interactions at the binding site. Thus, the high binding affinity and insights from the molecular interaction signify that the identified CPAMPs from the human microbiome can be further explored as inhibitory agents against multidrug-resistant Ps. aeruginosa.

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.

Hotspots for mutations in the SARS-CoV-2 spike glycoprotein: a correspondence analysis

Spike glycoprotein (Sgp) is liable for binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the host receptors. Since Sgp is the main target for vaccine and drug designing, elucidating its mutation pattern could help in this regard. This study is aimed at investigating the correspondence of specific residues to the SgpSARS-CoV-2 functionality by explorative interpretation of sequence alignments. Centrality analysis of the Sgp dissects the importance of these residues in the interaction network of the RBD-ACE2 (receptor-binding domain) complex and furin cleavage site. Correspondence of RBD to threonine500 and asparagine501 and furin cleavage site to glutamine675, glutamine677, threonine678, and alanine684 was observed; all residues are exactly located at the interaction interfaces. The harmonious location of residues dictates the RBD binding property and the flexibility, hydrophobicity, and accessibility of the furin cleavage site. These species-specific residues can be assumed as real targets of evolution, while other substitutions tend to support them. Moreover, all these residues are parts of experimentally identified epitopes. Therefore, their substitution may affect vaccine efficacy. Higher rate of RBD maintenance than furin cleavage site was predicted. The accumulation of substitutions reinforces the probability of the multi-host circulation of the virus and emphasizes the enduring evolutionary events.

A rigorous framework for detecting SARS-CoV-2 spike protein mutational ensemble from genomic and structural features

The recent release of SARS-CoV-2 genomic data from several countries has provided clues into the potential antigenic drift of the coronavirus population. In particular, the genomic instability observed in the spike protein necessitates immediate action and further exploration in the context of viral-host interactions. By temporally tracking 527,988 SARS-CoV-2 genomes, we identified invariant and hypervariable regions within the spike protein. We evaluated combination of mutations from SARS-CoV-2 lineages and found that maximum number of lineage-defining mutations were present in the N-terminal domain (NTD). Based on mutant 3D-structural models of known Variants of Concern (VOCs), we found that structural properties such as accessibility, secondary structural type, and intra-protein interactions at local mutation sites are greatly altered. Further, we observed significant differences between intra-protein networks of wild-type and Delta mutant, with the latter showing dense intra-protein contacts. Extensive molecular dynamics simulations of D614G mutant spike structure with hACE2 further revealed dynamic features with 47.7% of mutations mapping on flexible regions of spike protein. Thus, we propose that significant changes within spike protein structure have occurred that may impact SARS-CoV-2 pathogenesis, and repositioning of vaccine candidates is required to contain the spread of COVID-19 pathogen.

Epistatic interactions promote persistence of NS3-Q80K in HCV infection by compensating for protein folding instability

The Q80K polymorphism in the NS3-4A protease of the hepatitis C virus is associated with treatment failure of direct-acting antiviral agents. This polymorphism is highly prevalent in genotype 1a infections and stably transmitted between hosts. Here, we investigated the underlying molecular mechanisms of evolutionarily conserved coevolving amino acids in NS3-Q80K and revealed potential implications of epistatic interactions in immune escape and variants persistence. Using purified protein, we characterized the impact of epistatic amino acid substitutions on the physicochemical properties and peptide cleavage kinetics of the NS3-Q80K protease. We found that Q80K destabilized the protease protein fold (p < 0.0001). Although NS3-Q80K showed reduced peptide substrate turnover (p < 0.0002), replicative fitness in an H77S.3 cell culture model of infection was not significantly inferior to the WT virus. Epistatic substitutions at residues 91 and 174 in NS3-Q80K stabilized the protein fold (p < 0.0001) and leveraged the WT protease stability. However, changes in protease stability inversely correlated with enzymatic activity. In infectious cell culture, these secondary substitutions were not associated with a gain of replicative fitness in NS3-Q80K variants. Using molecular dynamics, we observed that the total number of residue contacts in NS3-Q80K mutants correlated with protein folding stability. Changes in the number of contacts reflected the compensatory effect on protein folding instability by epistatic substitutions. In summary, epistatic substitutions in NS3-Q80K contribute to viral fitness by mechanisms not directly related to RNA replication. By compensating for protein-folding instability, epistatic interactions likely protect NS3-Q80K variants from immune cell recognition.

Cheminformatic quantum mechanical enzyme model design: A catechol-O-methyltransferase case study

To accurately simulate the inner workings of an enzyme active site with quantum mechanics (QM), not only must the reactive species be included in the model but also important surrounding residues, solvent, or coenzymes involved in crafting the microenvironment. Our lab has been developing the Residue Interaction Network Residue Selector (RINRUS) toolkit to utilize interatomic contact network information for automated, rational residue selection and QM-cluster model generation. Starting from an x-ray crystal structure of catechol-O-methyltransferase, RINRUS was used to construct a series of QM-cluster models. The reactant, product, and transition state of the methyl transfer reaction were computed for a total of 550 models, and the resulting free energies of activation and reaction were used to evaluate model convergence. RINRUS-designed models with only 200-300 atoms are shown to converge. RINRUS will serve as a cornerstone for improved and automated cheminformatics-based enzyme model design.

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

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

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.