Journal ArticleRosetta custom score functions accurately predict ΔΔ <i>G</i> of mutations at protein–protein interfaces using machine learningShringari, Sumant R.; Giannakoulias, Sam; Ferrie, John J.; Petersson, E. JamesProtein–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 ΔΔ G values associated with interfacial mutations.2020-06-2310172056Chemical Communications56506774 to 67771359-7345https://doi.org/10.1039/d0cc01959c1708759National Science Foundation