Biology subjects

Verkhivker, G. M.

Publications and source records attributed to Verkhivker, G. M..

3 recordsLinked to original sources

bioRxiv2022

Hierarchical Computational Modeling and Dynamic Network Analysis of Allosteric Regulation in the SARS-CoV-2 Spike Omicron Trimer Structures: Omicron Mutations Cooperate to Allosterically Control Balance of Protein Stability and Conformational Adaptability

Structural and computational studies of the Omicron spike protein in various functional states and complexes provided important insights into molecular mechanisms underlying binding, high transmissibility, and escaping immune defense. However, the regulatory roles and functional coordination of the Omicron mutations are poorly understood and often ignored in the proposed mechanisms. In this work, we explored the hypothesis that the SARS-CoV-2 spike protein can function as a robust allosterically regulated machinery in which Omicron mutational sites are dynamically coupled and form a central engine of the allosteric network that regulates the balance between conformational plasticity, protein stability, and functional adaptability. In this study, we employed coarse-grained dynamics simulations of multiple full-length SARS-CoV-2 spike Omicron trimers structures in the closed and open states with the local energetic frustration analysis and collective dynamics mapping to understand the determinants and key hotspots driving the balance of protein stability and conformational adaptability. We have found that the Omicron mutational sites at the inter-protomer regions form regulatory clusters that control functional transitions between the closed and open states. Through perturbation-based modeling of allosteric interaction networks and diffusion analysis of communications in the closed and open spike states, we quantify the allosterically regulated activation mechanism and uncover specific regulatory roles of the Omicron mutations. The network modeling demonstrated that Omicron mutations form the inter-protomer electrostatic bridges that connect local stable communities and function as allosteric switches of signal transmission. The results of this study are consistent with the experiments, revealing distinct and yet complementary role of the Omicron mutational sites as a network of hotspots that enable allosteric modulation of structural stability and conformational changes which are central for spike activation and virus transmissibility.

biophysics↗

bioRxiv2021

Dynamic Profiling of Binding and Allosteric Propensities of the SARS-CoV-2 Spike Protein with Different Classes of Antibodies: Mutational and Perturbation-Based Scanning Reveal Allosteric Duality of Functionally Adaptable Hotspots

Structural and biochemical studies of the SARS-CoV-2 spike complexes with highly potent antibodies have revealed multiple conformation-dependent epitopes and a broad range of recognition modes linked to different neutralization responses In this study, we combined atomistic simulations with mutational and perturbation-based scanning approaches to perform in silico profiling of binding and allosteric propensities of the SARS-CoV-2 spike protein residues in complexes with B38, P2B-2F6, EY6A and S304 antibodies representing three different classes. Conformational dynamics analysis revealed that binding-induced modulation of soft modes can elicit the unique protein response to different classes of antibodies. Mutational scanning heatmaps and sensitivity analysis revealed the binding energy hotspots for different classes of antibodies that are consistent with the experimental deep mutagenesis, showing that differences in the binding affinity caused by global circulating variants in spike positions K417, E484 and N501 are relatively moderate and may not fully account for the observed antibody resistance effects. Through functional dynamics analysis and perturbation-response scanning of the SARS-CoV-2 spike protein residues in the unbound form and antibody-bound forms, we examine how antibody binding can modulate allosteric propensities of spike protein residues and determine allosteric hotspots that control signal transmission and global conformational changes. These results show that residues K417, E484, and N501 targeted by circulating mutations correspond to a group of versatile allosteric centers in which small perturbations can modulate collective motions, alter the global allosteric response and elicit binding resistance. We suggest that SARS-CoV-2 S protein may exploit plasticity of specific allosteric hotspots to generate escape mutants that alter response to antibody binding without compromising activity of the spike protein.

biophysics↗

bioRxiv2020

Impact of emerging mutations on the dynamic properties the SARS-CoV-2 main protease: an in silico investigation

The new coronavirus (SARS-CoV-2) is a global threat to world health and its economy. Its main protease (Mpro), which functions as a dimer, cleaves viral precursor proteins in the process of viral maturation. It is a good candidate for drug development owing to its conservation and the absence of a human homolog. An improved understanding of the protein behaviour can accelerate the discovery of effective therapies in order to reduce mortality. 100 ns all-atom molecular dynamics simulations of 50 homology modelled mutant Mpro dimers were performed at pH 7 from filtered sequences obtained from the GISAID database. Protease dynamics were analysed using RMSD, RMSF, Rg, the averaged betweenness centrality and geometry calculations. Domains from each Mpro protomer were found to generally have independent motions, while the dimer-stabilising N-finger region was found to be flexible in most mutants. A mirrored interprotomer pocket was found to be correlated to the catalytic site using compaction dynamics, and can be a potential allosteric target. The high number of titratable amino acids of Mpro may indicate an important role of pH on enzyme dynamics, as previously reported for SARS-CoV. Independent coarse-grained Monte Carlo simulations suggest a link between rigidity/mutability and enzymatic function.

bioinformatics↗