RT Journal Article
SR Electronic
T1 Feature Design for Protein Interface hotspots using KFC2 and Rosetta
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 514372
DO 10.1101/514372
A1 Franziska Seeger
A1 Anna Little
A1 Yang Chen
A1 Tina Woolf
A1 Haiyan Cheng
A1 Julie C. Mitchell
YR 2019
UL http://biorxiv.org/content/early/2019/01/16/514372.abstract
AB Protein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally charac-terizing protein residues that contribute the most to protein-protein interaction affin-ity 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 2-way and 3-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.