Biophysical Interpretation of Evolutionary Consequences on the SARS-CoV2 Main Protease through Molecular Dynamics Simulations and Network Topology Analysis

In this study, we present a combined analysis procedure between atomistic molecular dynamics (MD) simulations and network topology to obtain more understanding on the evolutionary consequences on protein stability and substrate binding of the main protease enzyme of SARS-CoV2. Communicability matrices of the protein residue networks (PRNs) were extracted from MD trajectories of both Mpro enzymes in complex with the nsp8/9 peptide substrate to compare the local communicability within both proteases that would affect the enzyme function, along with biophysical details on global protein conformation, flexibility, and contribution of amino acid side chains to both intramolecular and intermolecular interactions. The analysis displayed the significance of the mutated residue 46 with the highest communicability gain to the binding pocket closure. Interestingly, the mutated residue 134 with the highest communicability loss corresponded to a local structural disruption of the adjacent peptide loop. The enhanced flexibility of the disrupted loop connecting to the catalytic residue Cys145 introduced an extra binding mode that brought the substrate in proximity and could facilitate the reaction. This understanding might provide further help in the drug development strategy against SARS-CoV2 and prove the capability of the combined techniques of MD simulations and network topology analysis as a reverse protein engineering tool.

Automated summary

In this study, a combined analysis procedure involving atomistic molecular dynamics (MD) simulations and network topology was used to investigate the evolutionary consequences on the stability and substrate binding of the main protease enzyme of SARS-CoV2. The analysis revealed the significance of specific mutated residues in affecting the enzyme function and identified an extra binding mode that could facilitate the reaction. This understanding could contribute to the development of drugs against SARS-CoV2 and demonstrates the potential of using MD simulations and network topology analysis as a reverse protein engineering tool.