Short And Long-Range Effects Of Single-Point Mutations In Sars-Cov2 Main Protease M Pro Using Residue Network Representation

We present a graph-theoretic framework for assessing the connectivity and structural impact of single-point mutations in biological enzymes. The method maps the threedimensional crystallographic structure of a given enzyme into a C α -Protein Residue Network (PRN) where nodes n k represent central C α carbons in amino acids and edges represent physical interactions. We quantify residue relevance via centrality measures; in particular, we focus on betweenness, closeness, and eigenvector centralities. In silico mutagenesis (node impairment) is then applied to simulate single-point mutations. Using the SARS-CoV-2 main protease (Mpro) as a model, we analyze both apo and inhibitor-bound (holo) states. Our analysis identifies betweenness centrality as the most sensitive metric to perturbation. We show that simulated disruption of the catalytic residue Cys145 causes profound network disconnection, an effect amplified over tenfold in the holo state. Furthermore, we find that perturbation of the allosteric “160-loop” residue His164 rivals the effect of mutating the catalytic His41, underscoring its role as a critical hub for dimer stability and long-range communication. This network-based approach provides a rapid, topology and connectivity-driven strategy to analyze and target potential active and allosteric sites, offering a complementary tool for guiding targeted drug design.