A Bayesian walker coupled with a computational workflow that generates the micro-evolution of SARS-CoV-2 and makes predictions of new mutations that can emerge

Abstract

In this work, a Bayesian walker was constructed that generates mutations that are more prone as per UNIPROT variant data. The Bayesian walker was used to search the mutational landscape of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) and a computational workflow was followed to evaluate whether a particular mutation would satisfy natural selection’s fitness criteria. For SARS-CoV-2, the empirical known fitness criteria derived from SARS-CoV-2 micro-evolution data is 3-fold criteria. Mutations that have emerged on the Spike protein of SARS-CoV-2 are ones that preserve the structural integrity of the Spike, retain, or increase infectivity and are Immune evasive as per literature reports. Based on the molecular mechanism of infectivity and Immune evasion of SARS-CoV-2, a molecular modelling workflow was adopted to investigate the evolutionary feasibility of the mutations generated by the walker, to check whether the mutations satisfy the 3-fold fitness criteria for SARS-CoV-2 micro-evolution. It was found that the walker (mutation generator) coupled with the computational workflow to evaluate the evolutionary fitness of the generated mutations, re-generated the mutants corresponding to the Alpha, Beta and Gamma variants of SARS-CoV-2 demonstrating the ability of the methodology to generate the micro-evolution of SARS-CoV-2. Having demonstrated the ability of the methodology to generate the micro-evolution of SARS-CoV-2, the computational methodology was used to make predictions for new mutants that could emerge. Communicated by Ramaswamy H. Sarma