Physics-Based Protein Networks Might Recover Effectful Mutations─a Case Study on Cathepsin G

Molecular dynamics simulations have been remarkably effective for observing and analyzing structures and dynamics of proteins, with longer trajectories being computed every day. Still, often, relevant time scales are not observed. Adequately analyzing the generated trajectories can highlight the interesting areas within a protein such as mutation sites or allosteric hotspots, which might foreshadow dynamics untouched by the simulations. We employ a physics-based protein network and propose that such a network can adequately analyze the protein dynamics. The analysis is conducted on simulations of cathepsin G and neutrophil elastase, which are remarkably similar but with different specificities. However, a single mutation in cathepsin G recovers the specificity of neutrophil elastase. The physics-based network built on the interactions between residues instead of the distances can pinpoint the active triad in the proteins studied. Overall, the network seems to capture the structural behavior better than purely distance-based networks.