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MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory
Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificit...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5507473/ https://www.ncbi.nlm.nih.gov/pubmed/28650961 http://dx.doi.org/10.1371/journal.pcbi.1005614 |
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author | Rubenstein, Aliza B. Pethe, Manasi A. Khare, Sagar D. |
author_facet | Rubenstein, Aliza B. Pethe, Manasi A. Khare, Sagar D. |
author_sort | Rubenstein, Aliza B. |
collection | PubMed |
description | Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities. |
format | Online Article Text |
id | pubmed-5507473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-55074732017-07-25 MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory Rubenstein, Aliza B. Pethe, Manasi A. Khare, Sagar D. PLoS Comput Biol Research Article Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities. Public Library of Science 2017-06-26 /pmc/articles/PMC5507473/ /pubmed/28650961 http://dx.doi.org/10.1371/journal.pcbi.1005614 Text en © 2017 Rubenstein et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rubenstein, Aliza B. Pethe, Manasi A. Khare, Sagar D. MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory |
title | MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory |
title_full | MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory |
title_fullStr | MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory |
title_full_unstemmed | MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory |
title_short | MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory |
title_sort | mfpred: rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5507473/ https://www.ncbi.nlm.nih.gov/pubmed/28650961 http://dx.doi.org/10.1371/journal.pcbi.1005614 |
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