Optimizing ligand conformations in flexible protein targets: amulti-objective strategy

Finding the orientation of a ligand (small molecule) with the lowest binding energy to the macromolecule (receptor) is a complex optimization problem, commonly called ligand–protein docking. This problem has been usually approached by minimizing a single objective that corresponds to the final free...

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Detalles Bibliográficos
Autores: López Camacho, Esteban, García Godoy, María Jesús, García Nieto, José Manuel, Nebro, Antonio J., Aldana Montes, José F.
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/108857
Acceso en línea:https://hdl.handle.net/11441/108857
https://doi.org/10.1007/s00500-019-04575-2
Access Level:acceso abierto
Palabra clave:Molecular Docking
Multi-objective optimization
Metaheuristics
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spelling Optimizing ligand conformations in flexible protein targets: amulti-objective strategyLópez Camacho, EstebanGarcía Godoy, María JesúsGarcía Nieto, José ManuelNebro, Antonio J.Aldana Montes, José F.Molecular DockingMulti-objective optimizationMetaheuristicsFinding the orientation of a ligand (small molecule) with the lowest binding energy to the macromolecule (receptor) is a complex optimization problem, commonly called ligand–protein docking. This problem has been usually approached by minimizing a single objective that corresponds to the final free energy of binding. In this work, we propose a new multiobjective strategy focused on minimizing: (1) the root mean square deviation (RMSD) between the co-crystallized and predicted ligand atomic coordinates, and (2) the ligand–receptor intermolecular energy. This multi-objective strategy provides the molecular biologists with a range of solutions computing different RMSD scores and intermolecular energies. A set of representative multi-objective algorithms, namely NSGA-II, SMPSO, GDE3 and MOEA/D, have been evaluated in the scope of an extensive set of docking problems, which are featured by including HIV-proteases with flexible ARG8 side chains and their inhibitors. As use cases for biological validation, we have included a set of instances based on new retroviral inhibitors to HIV-proteases. The proposed multi-objective approach shows that the predictions of ligand’s pose can be promising in cases in which studies in silico are necessary to test new candidate drugs (or analogue drugs) to a given therapeutic target.Ministerio de Educación y Ciencia TIN2017-86049-RSpringerCiencias de la Computación e Inteligencia ArtificialMinisterio de Educación y Ciencia (MEC). España2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/108857https://doi.org/10.1007/s00500-019-04575-2reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSoft Computing, 24 (July 2020), 10705-10719.TIN2017-86049-Rhttps://link.springer.com/article/10.1007/s00500-019-04575-2info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1088572026-06-17T12:51:07Z
dc.title.none.fl_str_mv Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
title Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
spellingShingle Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
López Camacho, Esteban
Molecular Docking
Multi-objective optimization
Metaheuristics
title_short Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
title_full Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
title_fullStr Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
title_full_unstemmed Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
title_sort Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
dc.creator.none.fl_str_mv López Camacho, Esteban
García Godoy, María Jesús
García Nieto, José Manuel
Nebro, Antonio J.
Aldana Montes, José F.
author López Camacho, Esteban
author_facet López Camacho, Esteban
García Godoy, María Jesús
García Nieto, José Manuel
Nebro, Antonio J.
Aldana Montes, José F.
author_role author
author2 García Godoy, María Jesús
García Nieto, José Manuel
Nebro, Antonio J.
Aldana Montes, José F.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
Ministerio de Educación y Ciencia (MEC). España
dc.subject.none.fl_str_mv Molecular Docking
Multi-objective optimization
Metaheuristics
topic Molecular Docking
Multi-objective optimization
Metaheuristics
description Finding the orientation of a ligand (small molecule) with the lowest binding energy to the macromolecule (receptor) is a complex optimization problem, commonly called ligand–protein docking. This problem has been usually approached by minimizing a single objective that corresponds to the final free energy of binding. In this work, we propose a new multiobjective strategy focused on minimizing: (1) the root mean square deviation (RMSD) between the co-crystallized and predicted ligand atomic coordinates, and (2) the ligand–receptor intermolecular energy. This multi-objective strategy provides the molecular biologists with a range of solutions computing different RMSD scores and intermolecular energies. A set of representative multi-objective algorithms, namely NSGA-II, SMPSO, GDE3 and MOEA/D, have been evaluated in the scope of an extensive set of docking problems, which are featured by including HIV-proteases with flexible ARG8 side chains and their inhibitors. As use cases for biological validation, we have included a set of instances based on new retroviral inhibitors to HIV-proteases. The proposed multi-objective approach shows that the predictions of ligand’s pose can be promising in cases in which studies in silico are necessary to test new candidate drugs (or analogue drugs) to a given therapeutic target.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/108857
https://doi.org/10.1007/s00500-019-04575-2
url https://hdl.handle.net/11441/108857
https://doi.org/10.1007/s00500-019-04575-2
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Soft Computing, 24 (July 2020), 10705-10719.
TIN2017-86049-R
https://link.springer.com/article/10.1007/s00500-019-04575-2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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