COVID-19 has brought a great challenge to our health and global economics, and the coronavirus still evolves and many variants continue to emerge. It is a dire need to develop computational models to facilitate us to recognize those variants of concern (VOC) or variant of interest (VOI) from a great number of variants. Here we develop a set of computational models through both sequence-based and structure-based analysis to predict the infectability of a Spike mutant. First, we constructed a classification model using the correlation matrix from statistical coupling analysis as the descriptor to discriminate native sequences from the decoys; we then constructed regression models using the key descriptors of the classification model to predict the infectability estimated using the frequencies of SARS-CoV-2 variants (https://www.ncbi.nlm.nih.gov/activ); third, we performed molecular dynamics simulations (MDS) for 20 variants of high consequence (VOHC), and calculated the free energies of the virial Spike protein binding to hACE2 protein using the endpoint MM-PBSA-WSAS method. The protein stability due to mutation was then estimated by a variety of methods using the MDS sampled conformations; Variants that can enhance Spike-hACE binding are likely to be more infectious. last, we conducted correlation analysis using the MDS trajectories to identify the interaction pathways triggered by Spike-hACE2 binding. The host proteases furin and TMPRSS2 play a key role during SARS-CoV-2 entry. The pathways that lead to the critical residues of the Spike protein needed for furin recognition were identified and applied to interpret a variant’s infectability. With the established models from sequence and structural analysis, we are able to predict the infectability of emerging Sars-CoV-2 variants. The molecular mechanisms elucidated by this study could be applied in rational design of small molecule or peptide drugs targeting the Spike protein of the coronavirus.

Predict infectability of SARS-CoV-2 spike mutations using a combination of evolution analysis, site-site correlation analysis and binding free-energy analysis

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