Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds

Autores
Scardino, Valeria; Bollini, Mariela; Cavasotto, Claudio Norberto
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.
Fil: Scardino, Valeria. Universidad Austral; Argentina
Fil: Bollini, Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; Argentina
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
Materia
pose and rank consensus
virttual screening
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/173147
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/173147
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Combination of pose and rank consensus in docking-based virtual screening: The best of both worldsScardino, ValeriaBollini, MarielaCavasotto, Claudio Norbertopose and rank consensusvirttual screeninghttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.Fil: Scardino, Valeria. Universidad Austral; ArgentinaFil: Bollini, Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: 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; ArgentinaRoyal Society of Chemistry2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/173147Scardino, Valeria; Bollini, Mariela; Cavasotto, Claudio Norberto; Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds; Royal Society of Chemistry; RSC Advances; 11; 56; 10-2021; 35383-353912046-2069CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1039/d1ra05785einfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:24:21Zoai:ri.conicet.gov.ar:11336/173147instacron: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:34982025-09-29 10:24:22.044CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
title Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
spellingShingle Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
Scardino, Valeria
pose and rank consensus
virttual screening
title_short Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
title_full Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
title_fullStr Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
title_full_unstemmed Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
title_sort Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds
dc.creator.none.fl_str_mv Scardino, Valeria
Bollini, Mariela
Cavasotto, Claudio Norberto
author Scardino, Valeria
author_facet Scardino, Valeria
Bollini, Mariela
Cavasotto, Claudio Norberto
author_role author
author2 Bollini, Mariela
Cavasotto, Claudio Norberto
author2_role author
author
dc.subject.none.fl_str_mv pose and rank consensus
virttual screening
topic pose and rank consensus
virttual screening
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.
Fil: Scardino, Valeria. Universidad Austral; Argentina
Fil: Bollini, Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; Argentina
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
description The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
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/11336/173147
Scardino, Valeria; Bollini, Mariela; Cavasotto, Claudio Norberto; Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds; Royal Society of Chemistry; RSC Advances; 11; 56; 10-2021; 35383-35391
2046-2069
CONICET Digital
CONICET
url http://hdl.handle.net/11336/173147
identifier_str_mv Scardino, Valeria; Bollini, Mariela; Cavasotto, Claudio Norberto; Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds; Royal Society of Chemistry; RSC Advances; 11; 56; 10-2021; 35383-35391
2046-2069
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1039/d1ra05785e
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Royal Society of Chemistry
publisher.none.fl_str_mv Royal Society of Chemistry
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 13.070432

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