The emergence of SARS-CoV-2 and its variants that critically affect global public
health requires characterization of mutations and their evolutionary pattern from
specific Variants of Interest (VOIs) to Variants of Concern (VOCs). Leveraging the
concept of equilibrium statistical mechanics, we introduce a new responsive quantity
defined as “Mutational Response Function (MRF)” aptly quantifying
domain-wise average entropy-fluctuation in the spike glycoprotein sequence of SARS-CoV-2
based on its evolutionary database. As the evolution transits from a specific variant to
VOC, we find that the evolutionary crossover is accompanied by a dramatic change in MRF,
upholding the characteristic of a dynamic phase transition. With this entropic
information, we have developed an ancestral-based machine learning method that helps
predict future domain-specific mutations. The feedforward binary classification model
pinpoints possible residues prone to future mutations that have implications for
enhanced fusogenicity and pathogenicity of the virus. We believe such MRF analyses
followed by a statistical mechanics augmented ML approach could help track different
evolutionary stages of such species and identify a critical evolutionary transition that
is alarming.
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