Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mondal, Arup, Chang, Liwei, Perez, Alberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2022
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
_version_ 1785083016819769344
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
work_keys_str_mv AT mondalarup modellingpeptideproteincomplexesdockingsimulationsandmachinelearning
AT changliwei modellingpeptideproteincomplexesdockingsimulationsandmachinelearning
AT perezalberto modellingpeptideproteincomplexesdockingsimulationsandmachinelearning