How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelli...

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Detalles Bibliográficos
Autores: Scardino, Valeria, Di Filippo, Juan Ignacio, Cavasotto, Claudio Norberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/229198
Acceso en línea:http://hdl.handle.net/11336/229198
Access Level:acceso abierto
Palabra clave:ARTIFICIAL INTELLIGENCE
COMPUTATIONAL CHEMISTRY
PROTEIN
PROTEIN FOLDING
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
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oai_identifier_str oai:ri.conicet.gov.ar:11336/229198
network_acronym_str AR
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repository_id_str
spelling How good are AlphaFold models for docking-based virtual screening?Scardino, ValeriaDi Filippo, Juan IgnacioCavasotto, Claudio NorbertoARTIFICIAL INTELLIGENCECOMPUTATIONAL CHEMISTRYPROTEINPROTEIN FOLDINGhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.Fil: Scardino, Valeria. Universidad Austral; ArgentinaFil: Di Filippo, Juan Ignacio. 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: 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. Universidad Austral; ArgentinaCell Press2023-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/229198Scardino, Valeria; Di Filippo, Juan Ignacio; Cavasotto, Claudio Norberto; How good are AlphaFold models for docking-based virtual screening?; Cell Press; iScience; 26; 1; 1-2023; 1-182589-0042CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2589004222021939info:eu-repo/semantics/altIdentifier/doi/10.1016/j.isci.2022.105920info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T14:15:04Zoai:ri.conicet.gov.ar:11336/229198instacron: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:34982024-05-08 14:15:05.139CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv How good are AlphaFold models for docking-based virtual screening?
title How good are AlphaFold models for docking-based virtual screening?
spellingShingle How good are AlphaFold models for docking-based virtual screening?
Scardino, Valeria
ARTIFICIAL INTELLIGENCE
COMPUTATIONAL CHEMISTRY
PROTEIN
PROTEIN FOLDING
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
title_short How good are AlphaFold models for docking-based virtual screening?
title_full How good are AlphaFold models for docking-based virtual screening?
title_fullStr How good are AlphaFold models for docking-based virtual screening?
title_full_unstemmed How good are AlphaFold models for docking-based virtual screening?
title_sort How good are AlphaFold models for docking-based virtual screening?
dc.creator.none.fl_str_mv Scardino, Valeria
Di Filippo, Juan Ignacio
Cavasotto, Claudio Norberto
author Scardino, Valeria
author_facet Scardino, Valeria
Di Filippo, Juan Ignacio
Cavasotto, Claudio Norberto
author_role author
author2 Di Filippo, Juan Ignacio
Cavasotto, Claudio Norberto
author2_role author
author
dc.subject.none.fl_str_mv ARTIFICIAL INTELLIGENCE
COMPUTATIONAL CHEMISTRY
PROTEIN
PROTEIN FOLDING
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
topic ARTIFICIAL INTELLIGENCE
COMPUTATIONAL CHEMISTRY
PROTEIN
PROTEIN FOLDING
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
description A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.
publishDate 2023
dc.date.none.fl_str_mv 2023-01
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/11336/229198
Scardino, Valeria; Di Filippo, Juan Ignacio; Cavasotto, Claudio Norberto; How good are AlphaFold models for docking-based virtual screening?; Cell Press; iScience; 26; 1; 1-2023; 1-18
2589-0042
CONICET Digital
CONICET
url http://hdl.handle.net/11336/229198
identifier_str_mv Scardino, Valeria; Di Filippo, Juan Ignacio; Cavasotto, Claudio Norberto; How good are AlphaFold models for docking-based virtual screening?; Cell Press; iScience; 26; 1; 1-2023; 1-18
2589-0042
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.sciencedirect.com/science/article/pii/S2589004222021939
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.isci.2022.105920
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cell Press
publisher.none.fl_str_mv Cell Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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 15,812429

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