A Graph-Theoretic Method for Analysing Structural Protein Variability in Viral Variants

Abstract

This study presents a graph-theoretic framework to analyze codon networks derived from SARS-CoV-2 spike protein sequences. By applying Minimum Connected Dominating Sets (MCDS) and community detection methods, we identify key codons that maintain both global connectivity and local structural integrity. Centrality measures are used to determine the most influential codons, highlighting their importance in network stability. Additionally, statistical analysis provides insights into the structural robustness of the spike protein across different variants.