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Register a new userExploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation
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SARS-CoV-2 is what has caused the COVID-19 pandemic. Early viral infection is mediated by the SARS-CoV-2 homo-trimeric Spike (S) protein with its receptor binding domains (RBDs) in the receptor-accessible state. We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs). Our study reveals that the NTD acts as a wedge and plays a crucial regulatory role in the conformational changes of the S protein. The complete RBD structural transition is allowed only when the neighboring NTD that typically prohibits the RBDs movements as a wedge detaches and swings away. Based on this NTD wedge model, we propose that the NTD-RBD interface should be a potential drug target.
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The SARS-CoV-2 spike (S) protein facilitates viral infection, and has been the focus of many structure determination efforts. This paper studies the conformations of loops in the S protein based on the available Protein Data Bank (PDB) structures. Loops, as flexible regions of the protein, are known to be involved in binding and can adopt multiple conformations. We identify the loop regions of the S protein, and examine their structural variability across the PDB. While most loops had essentially one stable conformation, 17 of 44 loop regions were observed to be structurally variable with multiple substantively distinct conformations. Loop modeling methods were then applied to the S protein loop targets, and loops with multiple conformations were found to be more challenging for the methods to predict accurately. Sequence variants and the up/down structural states of the receptor binding domain were also considered in the analysis.
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major worldwide public health emergency that has infected over $1.5$ million people. The partially open state of S1 subunit in spike glycoprotein is considered vital for its infection with host cell and is represented as a key target for neutralizing antibodies. However, the mechanism elucidating the transition from the closed state to the partially open state still remains unclear. Here, we applied a combination of Markov state model, transition path theory and random forest to analyze the S1 motion. Our results explored a promising complete conformational movement of receptor-binding domain, from buried, partially open, to detached states. We also numerically confirmed the transition probability between those states. Based on the asymmetry in both the dynamics behavior and backbone C$alpha$ importance, we further suggested a relation between chains in the trimer spike protein, which may help in the vaccine design and antibody neutralization.
Biomolecules binding is influenced by many factors and its assessment constitutes a very hard challenge in computational structural biology. In this respect, the evaluation of shape complementarity at molecular interfaces is one of the key factors to be considered. Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on in-silico mutagenesis of antibody binding site residues, where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and a given epitope on a target protein surface. To quantify the complementarities occurring at the interface, we relied on a method we recently developed, which employs the 2D Zernike descriptors. To this end, we preliminary considered an experimental structural dataset of antibody-antigen complexes, where our method statistically identifies, in terms of shape complementarity, pairs of interacting regions from non-interacting ones. We thus constructed our protocol against a molecular region in the N-terminal domain of SARS-CoV-2 Spike protein, already experimentally identified in interaction with antibodies in humans. We, therefore, optimized the shape of several possible template antibodies for the interaction with such a region. Lastly, we performed an independent molecular docking validation of the results of our protocol, thus evaluating also if the mutagenesis protocol introduced residues with chemical characteristics compatible with the target region.
Network theory-based approaches provide valuable insights into the variations in global structural connectivity between differing 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 change between the closed and partially open states of the SARS-CoV-2 spike protein. This conformational change 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 the drug design strategies.
The deadly coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has gone out of control globally. Despite much effort by scientists, medical experts, and society in general, the slow progress on drug discovery and antibody therapeutic development, the unknown possible side effects of the existing vaccines, and the high transmission rate of the SARS-CoV-2, remind us of the sad reality that our current understanding of the transmission, infectivity, and evolution of SARS-CoV-2 is unfortunately very limited. The major limitation is the lack of mechanistic understanding of viral-host cell interactions, the viral regulation, protein-protein interactions, including antibody-antigen binding, protein-drug binding, host immune response, etc. This limitation will likely haunt the scientific community for a long time and have a devastating consequence in combating COVID-19 and other pathogens. Notably, compared to the long-cycle, highly cost, and safety-demanding molecular-level experiments, the theoretical and computational studies are economical, speedy, and easy to perform. There exists a tsunami of the literature on molecular modeling, simulation, and prediction of SARS-CoV-2 that has become impossible to fully be covered in a review. To provide the reader a quick update about the status of molecular modeling, simulation, and prediction of SARS-CoV-2, we present a comprehensive and systematic methodology-centered narrative in the nick of time. Aspects such as molecular modeling, Monte Carlo (MC) methods, structural bioinformatics, machine learning, deep learning, and mathematical approaches are included in this review. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are assessing the current status in the field.
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