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mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions
Protein–protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein–protein binding affinity have been propose...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262703/ https://www.ncbi.nlm.nih.gov/pubmed/33893812 http://dx.doi.org/10.1093/nar/gkab273 |
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author | Rodrigues, Carlos H M Pires, Douglas E V Ascher, David B |
author_facet | Rodrigues, Carlos H M Pires, Douglas E V Ascher, David B |
author_sort | Rodrigues, Carlos H M |
collection | PubMed |
description | Protein–protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein–protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein–protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi. |
format | Online Article Text |
id | pubmed-8262703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-82627032021-07-08 mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions Rodrigues, Carlos H M Pires, Douglas E V Ascher, David B Nucleic Acids Res Web Server Issue Protein–protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein–protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein–protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi. Oxford University Press 2021-04-24 /pmc/articles/PMC8262703/ /pubmed/33893812 http://dx.doi.org/10.1093/nar/gkab273 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://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/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Web Server Issue Rodrigues, Carlos H M Pires, Douglas E V Ascher, David B mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions |
title | mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions |
title_full | mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions |
title_fullStr | mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions |
title_full_unstemmed | mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions |
title_short | mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions |
title_sort | mmcsm-ppi: predicting the effects of multiple point mutations on protein–protein interactions |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262703/ https://www.ncbi.nlm.nih.gov/pubmed/33893812 http://dx.doi.org/10.1093/nar/gkab273 |
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