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Register a new userThree-fold mechanism of inhibition of SARS-CoV-2 infection by the interaction of the spike glycoprotein with heparin
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Heparin has been found to have antiviral activity against SARS-CoV-2. Here, by means of sliding window docking, molecular dynamics simulations and biochemical assays, we investigate the binding mode of heparin to the virus spike glycoprotein and the molecular basis for its antiviral activity. The simulations show that heparin binds at long, mostly positively charged patches on the spike, thereby masking the basic residues of the receptor binding domain and of the S1/S2 site. Experiments corroborated the simulation results by showing that heparin inhibits the cleavage of spike by furin by binding to the basic S1/S2 site. Our results indicate that heparin exerts its antiviral activity by both direct and allosteric mechanisms. Furthermore, the simulations provide insights into how heparan sulfate proteoglycans on the host cell can facilitate viral infection. Our results will aid the rational optimization of heparin derivatives for SARS-CoV-2 antiviral therapy.
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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.
Currently, there is no effective antiviral drugs nor vaccine for coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its high conservativeness and low similarity with human genes, SARS-CoV-2 main protease (M$^{text{pro}}$) is one of the most favorable drug targets. However, the current understanding of the molecular mechanism of M$^{text{pro}}$ inhibition is limited by the lack of reliable binding affinity ranking and prediction of existing structures of M$^{text{pro}}$-inhibitor complexes. This work integrates mathematics and deep learning (MathDL) to provide a reliable ranking of the binding affinities of 92 SARS-CoV-2 M$^{text{pro}}$ inhibitor structures. We reveal that Gly143 residue in M$^{text{pro}}$ is the most attractive site to form hydrogen bonds, followed by Cys145, Glu166, and His163. We also identify 45 targeted covalent bonding inhibitors. Validation on the PDBbind v2016 core set benchmark shows the MathDL has achieved the top performance with Pearsons correlation coefficient ($R_p$) being 0.858. Most importantly, MathDL is validated on a carefully curated SARS-CoV-2 inhibitor dataset with the averaged $R_p$ as high as 0.751, which endows the reliability of the present binding affinity prediction. The present binding affinity ranking, interaction analysis, and fragment decomposition offer a foundation for future drug discovery efforts.
A recent experimental study found that the binding affinity between the cellular receptor human angiotensin converting enzyme 2 (ACE2) and receptor-binding domain (RBD) in spike (S) protein of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is more than 10-fold higher than that of the original severe acute respiratory syndrome coronavirus (SARS-CoV). However, main-chain structures of the SARS-CoV-2 RBD are almost the same with that of the SARS-CoV RBD. Understanding physical mechanism responsible for the outstanding affinity between the SARS-CoV-2 S and ACE2 is the urgent challenge for developing blockers, vaccines and therapeutic antibodies against the coronavirus disease 2019 (COVID-19) pandemic. Considering the mechanisms of hydrophobic interaction, hydration shell, surface tension, and the shielding effect of water molecules, this study reveals a hydrophobic-interaction-based mechanism by means of which SARS-CoV-2 S and ACE2 bind together in an aqueous environment. The hydrophobic interaction between the SARS-CoV-2 S and ACE2 protein is found to be significantly greater than that between SARS-CoV S and ACE2. At the docking site, the hydrophobic portions of the hydrophilic side chains of SARS-CoV-2 S are found to be involved in the hydrophobic interaction between SARS-CoV-2 S and ACE2. We propose a method to design live attenuated viruses by mutating several key amino acid residues of the spike protein to decrease the hydrophobic surface areas at the docking site. Mutation of a small amount of residues can greatly reduce the hydrophobic binding of the coronavirus to the receptor, which may be significant reduce infectivity and transmissibility of the virus.
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.
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.
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