MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions
Ning Zhang,
Yuting Chen,
Haoyu Lu,
Feiyang Zhao,
Roberto Vera Alvarez,
Alexander Goncearenco,
Anna R. Panchenko,
Minghui Li
Affiliations
Ning Zhang
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
Yuting Chen
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
Haoyu Lu
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
Feiyang Zhao
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
Roberto Vera Alvarez
National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
Alexander Goncearenco
National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
Anna R. Panchenko
National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA; Corresponding author
Minghui Li
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; Corresponding author
Summary: Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors. : Protein Folding; Bioinformatics; 3D Reconstruction of Protein Subject Areas: Protein Folding, Bioinformatics, 3D Reconstruction of Protein