MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions
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 red...
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| Published in | iScience Vol. 23; no. 3; p. 100939 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
27.03.2020
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2589-0042 2589-0042 |
| DOI | 10.1016/j.isci.2020.100939 |
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| Abstract | 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.
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•A new method to predict binding affinity changes upon single and multiple mutations•Improved performance in evaluating the effects of mutations increasing binding affinity•Generation of the structural model of a mutant complex
Protein Folding; Bioinformatics; 3D Reconstruction of Protein |
|---|---|
| AbstractList | 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.
[Display omitted]
•A new method to predict binding affinity changes upon single and multiple mutations•Improved performance in evaluating the effects of mutations increasing binding affinity•Generation of the structural model of a mutant complex
Protein Folding; Bioinformatics; 3D Reconstruction of Protein 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 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. 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. • A new method to predict binding affinity changes upon single and multiple mutations • Improved performance in evaluating the effects of mutations increasing binding affinity • Generation of the structural model of a mutant complex Protein Folding; Bioinformatics; 3D Reconstruction of Protein 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.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. |
| ArticleNumber | 100939 |
| Author | Panchenko, Anna R. Chen, Yuting Lu, Haoyu Zhao, Feiyang Goncearenco, Alexander Li, Minghui Zhang, Ning Alvarez, Roberto Vera |
| AuthorAffiliation | 2 National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA 1 Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China |
| AuthorAffiliation_xml | – name: 2 National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA – name: 1 Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China |
| Author_xml | – sequence: 1 givenname: Ning surname: Zhang fullname: Zhang, Ning organization: Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China – sequence: 2 givenname: Yuting surname: Chen fullname: Chen, Yuting organization: Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China – sequence: 3 givenname: Haoyu surname: Lu fullname: Lu, Haoyu organization: Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China – sequence: 4 givenname: Feiyang surname: Zhao fullname: Zhao, Feiyang organization: Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China – sequence: 5 givenname: Roberto Vera surname: Alvarez fullname: Alvarez, Roberto Vera organization: National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA – sequence: 6 givenname: Alexander surname: Goncearenco fullname: Goncearenco, Alexander organization: National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA – sequence: 7 givenname: Anna R. surname: Panchenko fullname: Panchenko, Anna R. email: panch@ncbi.nlm.nih.gov organization: National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA – sequence: 8 givenname: Minghui surname: Li fullname: Li, Minghui email: minghui.li@suda.edu.cn organization: Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32169820$$D View this record in MEDLINE/PubMed |
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