Throughout the ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus has undergone continuous genomic evolution, leading to the emergence of numerous variants characterized by increased transmissibility and the ability to evade immune responses. The spike glycoprotein of SARS-CoV-2, a critical factor in infection and the primary target for antibodies, experiences mutations that significantly influence its evolutionary trajectory. This study introduces a statistical method, Dynamic Expedition of Leading Mutations (deLemus), to investigate the evolutionary dynamics of the spike protein.

The first part outlines the identification of leading mutational sites by analyzing single amino acid polymorphisms (SAP) and utilizing singular value...[ Read more ]

Throughout the ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus has undergone continuous genomic evolution, leading to the emergence of numerous variants characterized by increased transmissibility and the ability to evade immune responses. The spike glycoprotein of SARS-CoV-2, a critical factor in infection and the primary target for antibodies, experiences mutations that significantly influence its evolutionary trajectory. This study introduces a statistical method, Dynamic Expedition of Leading Mutations (deLemus), to investigate the evolutionary dynamics of the spike protein.

The first part outlines the identification of leading mutational sites by analyzing single amino acid polymorphisms (SAP) and utilizing singular value decomposition (SVD) on a two-dimensional mutation matrix. The L-index is proposed as a metric to quantify mutation strength at each amino acid site, revealing the outlined leading mutations that have been confirmed across the reported variants. Next, a three-dimensional matrix is created by expanding the two-dimensional mutation matrix with amino acid information. Tensor decomposition and the pair coupling matrix (PCM) are employed to identify leading mutations through the novel method, Leading Mutations by Composite Metric (LMCM). This approach successfully reveals the dynamic landscape of evolutionary mutations in the spike glycoprotein across domains and time points.

The thesis concludes with a comprehensive analysis of the spike glycoprotein’s evolutionary behavior. It examines mutation rates across time, domains, and variants, discusses the scaling relationship of mutations and deletions, and analyzes amino acid polymorphism and glycan sequon patterns to gain further insights into spike protein evolution. Overall, this thesis provides profound insights into the evolutionary trajectory of SARS-CoV-2, offering valuable lessons for mitigating future outbreaks.