Abstract
Protein–protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally characterizing protein residues that contribute the most to protein–protein interaction affinity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein–protein interfaces provides important information about how to inhibit therapeutically relevant protein–protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein–protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A two-way and three-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.
