Rosetta custom score functions accurately predict ΔΔGof mutations at protein-protein interfaces using machine learning

Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify hotspots have remained elusive despite the myriad of modeling and machine learning methods tested. Here, for the first time, we demonstrate that nonlinear reweighting of energy terms from Rosetta, through the use of machine learning, exhibits improved predictability of Delta Delta Gvalues associated with interfacial mutations.