Multi-objective ligand-protein docking with particle swarm optimizers

In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the a...

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Detalhes bibliográficos
Autores: García Nieto, José Manuel, López Camacho, Esteban, García Godoy, María Jesús, Nebro, Antonio J., Aldana Montes, José F.
Formato: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2019
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/108832
Acesso em linha:https://hdl.handle.net/11441/108832
https://doi.org/10.1016/j.swevo.2018.05.007
Access Level:acceso abierto
Palavra-chave:Multi-objective optimization
Particle Swarm Optimization
Molecular Docking
Archiving Strategies
Algorithm Comparison
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spelling Multi-objective ligand-protein docking with particle swarm optimizersGarcía Nieto, José ManuelLópez Camacho, EstebanGarcía Godoy, María JesúsNebro, Antonio J.Aldana Montes, José F.Multi-objective optimizationParticle Swarm OptimizationMolecular DockingArchiving StrategiesAlgorithm ComparisonIn the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view.Ministerio de Ciencia e Innovación TIN2017-86049-RMinisterio de Ciencia e Innovación TIN2014- 58304Junta de Andalucía P12-TIC-1519ElsevierCiencias de la Computación e Inteligencia ArtificialMinisterio de Ciencia e Innovación (MICIN). EspañaJunta de Andalucía2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/108832https://doi.org/10.1016/j.swevo.2018.05.007reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSwarm and Evolutionary Computation, 44 (February 2019), 439-452.TIN2017-86049-RTIN2014- 58304P12-TIC-1519https://www.sciencedirect.com/science/article/pii/S2210650217304467info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1088322026-06-17T12:51:07Z
dc.title.none.fl_str_mv Multi-objective ligand-protein docking with particle swarm optimizers
title Multi-objective ligand-protein docking with particle swarm optimizers
spellingShingle Multi-objective ligand-protein docking with particle swarm optimizers
García Nieto, José Manuel
Multi-objective optimization
Particle Swarm Optimization
Molecular Docking
Archiving Strategies
Algorithm Comparison
title_short Multi-objective ligand-protein docking with particle swarm optimizers
title_full Multi-objective ligand-protein docking with particle swarm optimizers
title_fullStr Multi-objective ligand-protein docking with particle swarm optimizers
title_full_unstemmed Multi-objective ligand-protein docking with particle swarm optimizers
title_sort Multi-objective ligand-protein docking with particle swarm optimizers
dc.creator.none.fl_str_mv García Nieto, José Manuel
López Camacho, Esteban
García Godoy, María Jesús
Nebro, Antonio J.
Aldana Montes, José F.
author García Nieto, José Manuel
author_facet García Nieto, José Manuel
López Camacho, Esteban
García Godoy, María Jesús
Nebro, Antonio J.
Aldana Montes, José F.
author_role author
author2 López Camacho, Esteban
García Godoy, María Jesús
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 Ciencia e Innovación (MICIN). España
Junta de Andalucía
dc.subject.none.fl_str_mv Multi-objective optimization
Particle Swarm Optimization
Molecular Docking
Archiving Strategies
Algorithm Comparison
topic Multi-objective optimization
Particle Swarm Optimization
Molecular Docking
Archiving Strategies
Algorithm Comparison
description In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view.
publishDate 2019
dc.date.none.fl_str_mv 2019
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/11441/108832
https://doi.org/10.1016/j.swevo.2018.05.007
url https://hdl.handle.net/11441/108832
https://doi.org/10.1016/j.swevo.2018.05.007
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Swarm and Evolutionary Computation, 44 (February 2019), 439-452.
TIN2017-86049-R
TIN2014- 58304
P12-TIC-1519
https://www.sciencedirect.com/science/article/pii/S2210650217304467
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 Elsevier
publisher.none.fl_str_mv Elsevier
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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score 15,300719

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