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Modelling peptide–protein complexes: docking, simulations and machine learning
Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide–protein complexes remains more challenging than protein–protein or protein–small molecule interaction...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392694/ https://www.ncbi.nlm.nih.gov/pubmed/37529282 http://dx.doi.org/10.1017/qrd.2022.14 |
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author | Mondal, Arup Chang, Liwei Perez, Alberto |
author_facet | Mondal, Arup Chang, Liwei Perez, Alberto |
author_sort | Mondal, Arup |
collection | PubMed |
description | Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide–protein complexes remains more challenging than protein–protein or protein–small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions. |
format | Online Article Text |
id | pubmed-10392694 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103926942023-08-01 Modelling peptide–protein complexes: docking, simulations and machine learning Mondal, Arup Chang, Liwei Perez, Alberto QRB Discov Research Article Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide–protein complexes remains more challenging than protein–protein or protein–small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions. Cambridge University Press 2022-09-19 /pmc/articles/PMC10392694/ /pubmed/37529282 http://dx.doi.org/10.1017/qrd.2022.14 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
spellingShingle | Research Article Mondal, Arup Chang, Liwei Perez, Alberto Modelling peptide–protein complexes: docking, simulations and machine learning |
title | Modelling peptide–protein complexes: docking, simulations and machine learning |
title_full | Modelling peptide–protein complexes: docking, simulations and machine learning |
title_fullStr | Modelling peptide–protein complexes: docking, simulations and machine learning |
title_full_unstemmed | Modelling peptide–protein complexes: docking, simulations and machine learning |
title_short | Modelling peptide–protein complexes: docking, simulations and machine learning |
title_sort | modelling peptide–protein complexes: docking, simulations and machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392694/ https://www.ncbi.nlm.nih.gov/pubmed/37529282 http://dx.doi.org/10.1017/qrd.2022.14 |
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