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Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction
Protein-protein interactions often involve a complex system of intermolecular interactions between residues and atoms at the binding site. A comprehensive exploration of these interactions can help reveal key residues involved in protein-protein recognition that are not obvious using other protein a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572624/ https://www.ncbi.nlm.nih.gov/pubmed/36234723 http://dx.doi.org/10.3390/molecules27196178 |
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author | Tam, Justin Z. Palumbo, Talulla Miwa, Julie M. Chen, Brian Y. |
author_facet | Tam, Justin Z. Palumbo, Talulla Miwa, Julie M. Chen, Brian Y. |
author_sort | Tam, Justin Z. |
collection | PubMed |
description | Protein-protein interactions often involve a complex system of intermolecular interactions between residues and atoms at the binding site. A comprehensive exploration of these interactions can help reveal key residues involved in protein-protein recognition that are not obvious using other protein analysis techniques. This paper presents and extends DiffBond, a novel method for identifying and classifying intermolecular bonds while applying standard definitions of bonds in chemical literature to explain protein interactions. DiffBond predicted intermolecular bonds from four protein complexes: Barnase-Barstar, Rap1a-raf, SMAD2-SMAD4, and a subset of complexes formed from three-finger toxins and nAChRs. Based on validation through manual literature search and through comparison of two protein complexes from the SKEMPI dataset, DiffBond was able to identify intermolecular ionic bonds and hydrogen bonds with high precision and recall, and identify salt bridges with high precision. DiffBond predictions on bond existence were also strongly correlated with observations of Gibbs free energy change and electrostatic complementarity in mutational experiments. DiffBond can be a powerful tool for predicting and characterizing influential residues in protein-protein interactions, and its predictions can support research in mutational experiments and drug design. |
format | Online Article Text |
id | pubmed-9572624 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95726242022-10-17 Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction Tam, Justin Z. Palumbo, Talulla Miwa, Julie M. Chen, Brian Y. Molecules Article Protein-protein interactions often involve a complex system of intermolecular interactions between residues and atoms at the binding site. A comprehensive exploration of these interactions can help reveal key residues involved in protein-protein recognition that are not obvious using other protein analysis techniques. This paper presents and extends DiffBond, a novel method for identifying and classifying intermolecular bonds while applying standard definitions of bonds in chemical literature to explain protein interactions. DiffBond predicted intermolecular bonds from four protein complexes: Barnase-Barstar, Rap1a-raf, SMAD2-SMAD4, and a subset of complexes formed from three-finger toxins and nAChRs. Based on validation through manual literature search and through comparison of two protein complexes from the SKEMPI dataset, DiffBond was able to identify intermolecular ionic bonds and hydrogen bonds with high precision and recall, and identify salt bridges with high precision. DiffBond predictions on bond existence were also strongly correlated with observations of Gibbs free energy change and electrostatic complementarity in mutational experiments. DiffBond can be a powerful tool for predicting and characterizing influential residues in protein-protein interactions, and its predictions can support research in mutational experiments and drug design. MDPI 2022-09-21 /pmc/articles/PMC9572624/ /pubmed/36234723 http://dx.doi.org/10.3390/molecules27196178 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tam, Justin Z. Palumbo, Talulla Miwa, Julie M. Chen, Brian Y. Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction |
title | Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction |
title_full | Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction |
title_fullStr | Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction |
title_full_unstemmed | Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction |
title_short | Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction |
title_sort | analysis of protein-protein interactions for intermolecular bond prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572624/ https://www.ncbi.nlm.nih.gov/pubmed/36234723 http://dx.doi.org/10.3390/molecules27196178 |
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