Exponential consensus ranking improves the outcome in docking and receptor ensemble docking

Autores
Palacio Rodríguez, Karen; Lans, Isaías; Cavasotto, Claudio Norberto; Cossio, Pilar
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.
Fil: Palacio Rodríguez, Karen. Universidad de Antioquia; Colombia
Fil: Lans, Isaías. Universidad de Antioquia; Colombia
Fil: Cavasotto, Claudio Norberto. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; Argentina
Fil: Cossio, Pilar. Universidad de Antioquia; Colombia
Materia
DOCKING
ENRICHMENT FACTOR
CONSENSUS SCORING
ENSEMBLE DOCKING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/163874
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Exponential consensus ranking improves the outcome in docking and receptor ensemble dockingPalacio Rodríguez, KarenLans, IsaíasCavasotto, Claudio NorbertoCossio, PilarDOCKINGENRICHMENT FACTORCONSENSUS SCORINGENSEMBLE DOCKINGhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.Fil: Palacio Rodríguez, Karen. Universidad de Antioquia; ColombiaFil: Lans, Isaías. Universidad de Antioquia; ColombiaFil: Cavasotto, Claudio Norberto. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; ArgentinaFil: Cossio, Pilar. Universidad de Antioquia; ColombiaNature Publishing Group2019-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/163874Palacio Rodríguez, Karen; Lans, Isaías; Cavasotto, Claudio Norberto; Cossio, Pilar; Exponential consensus ranking improves the outcome in docking and receptor ensemble docking; Nature Publishing Group; Scientific Reports; 9; 1; 3-2019; 5142-51422045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-019-41594-3info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-019-41594-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:04:24Zoai:ri.conicet.gov.ar:11336/163874instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:04:24.456CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
title Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
spellingShingle Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
Palacio Rodríguez, Karen
DOCKING
ENRICHMENT FACTOR
CONSENSUS SCORING
ENSEMBLE DOCKING
title_short Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
title_full Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
title_fullStr Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
title_full_unstemmed Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
title_sort Exponential consensus ranking improves the outcome in docking and receptor ensemble docking
dc.creator.none.fl_str_mv Palacio Rodríguez, Karen
Lans, Isaías
Cavasotto, Claudio Norberto
Cossio, Pilar
author Palacio Rodríguez, Karen
author_facet Palacio Rodríguez, Karen
Lans, Isaías
Cavasotto, Claudio Norberto
Cossio, Pilar
author_role author
author2 Lans, Isaías
Cavasotto, Claudio Norberto
Cossio, Pilar
author2_role author
author
author
dc.subject.none.fl_str_mv DOCKING
ENRICHMENT FACTOR
CONSENSUS SCORING
ENSEMBLE DOCKING
topic DOCKING
ENRICHMENT FACTOR
CONSENSUS SCORING
ENSEMBLE DOCKING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.
Fil: Palacio Rodríguez, Karen. Universidad de Antioquia; Colombia
Fil: Lans, Isaías. Universidad de Antioquia; Colombia
Fil: Cavasotto, Claudio Norberto. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; Argentina
Fil: Cossio, Pilar. Universidad de Antioquia; Colombia
description Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.
publishDate 2019
dc.date.none.fl_str_mv 2019-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/163874
Palacio Rodríguez, Karen; Lans, Isaías; Cavasotto, Claudio Norberto; Cossio, Pilar; Exponential consensus ranking improves the outcome in docking and receptor ensemble docking; Nature Publishing Group; Scientific Reports; 9; 1; 3-2019; 5142-5142
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/163874
identifier_str_mv Palacio Rodríguez, Karen; Lans, Isaías; Cavasotto, Claudio Norberto; Cossio, Pilar; Exponential consensus ranking improves the outcome in docking and receptor ensemble docking; Nature Publishing Group; Scientific Reports; 9; 1; 3-2019; 5142-5142
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-019-41594-3
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-019-41594-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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