Revealing SARS-CoV-2 M mutation cold and hot spots: Dynamic residue network analysis meets machine learning.

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (M) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots.