Deciphering the temporal and spatial mutation dynamics of the SARS-CoV-2 spike glycoprotein.

We present a statistical pipeline with two parallel procedures to analyze SARS-CoV-2 spike evolution: (1) probability sequence density analysis for probing its sequence space, and (2) leading mutations by the composite metric. This metric integrates mutation eigenvector information with pairwise couplings to outline evolutionarily significant mutations, coined leading mutations, from massive data sets. Our results reveal progressive increase in sequence mutation rates over time, alongside scaling behaviors predictive of variant emergence and evolutionary trends in spike mutation patterns. These findings characterize the mechanisms by which the spike glycoprotein acquires new mutations, offering insights into its evolutionary dynamics.