An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes

Autores
Blanco Capurro, Juan Ignacio; Di Paola, Matías Ezequiel; Gamarra, Marcelo Daniel; Marti, Marcelo Adrian; Modenutti, Carlos Pablo
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Unraveling the structure of lectin-carbohydrate complexes is vital for understanding key biological recognition processes and development of glycomimetic drugs. Molecular Docking application to predict them is challenging due to their low affinity, hydrophilic nature and ligand conformational diversity. In the last decade several strategies, such as the inclusion of glycan conformation specific scoring functions or our developed solvent-site biased method, have improved carbohydrate docking performance but significant challenges remain, in particular, those related to receptor conformational diversity. In the present work we have analyzed conventional and solvent-site biased autodock4 performance concerning receptor conformational diversity as derived from different crystal structures (apo and holo), Molecular Dynamics snapshots and Homology-based models, for 14 different lectin-monosaccharide complexes. Our results show that both conventional and biased docking yield accurate lectin-monosaccharide complexes, starting from either apo or homology-based structures, even when only moderate (45%) sequence identity templates are available. An essential element for success is a proper combination of a middle-sized (10-100 structures) conformational ensemble, derived either from Molecular dynamics or multiple homology model building. Consistent with our previous works, results show that solvent-site biased methods improve overall performance, but that results are still highly system dependent. Finally, our results also show that docking can select the correct receptor structure within the ensemble, underscoring the relevance of joint evaluation of both ligand pose and receptor conformation.
Fil: Blanco Capurro, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Di Paola, Matías Ezequiel. Universidad de Buenos Aires; Argentina
Fil: Gamarra, Marcelo Daniel. Universidad de Buenos Aires; Argentina
Fil: Marti, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Modenutti, Carlos Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
CARBOHYDRATES
DOCKING
HOMOLOGY-MODELING
LECTIN
MOLECULAR DYNAMICS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/97997
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/97997
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexesBlanco Capurro, Juan IgnacioDi Paola, Matías EzequielGamarra, Marcelo DanielMarti, Marcelo AdrianModenutti, Carlos PabloCARBOHYDRATESDOCKINGHOMOLOGY-MODELINGLECTINMOLECULAR DYNAMICShttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Unraveling the structure of lectin-carbohydrate complexes is vital for understanding key biological recognition processes and development of glycomimetic drugs. Molecular Docking application to predict them is challenging due to their low affinity, hydrophilic nature and ligand conformational diversity. In the last decade several strategies, such as the inclusion of glycan conformation specific scoring functions or our developed solvent-site biased method, have improved carbohydrate docking performance but significant challenges remain, in particular, those related to receptor conformational diversity. In the present work we have analyzed conventional and solvent-site biased autodock4 performance concerning receptor conformational diversity as derived from different crystal structures (apo and holo), Molecular Dynamics snapshots and Homology-based models, for 14 different lectin-monosaccharide complexes. Our results show that both conventional and biased docking yield accurate lectin-monosaccharide complexes, starting from either apo or homology-based structures, even when only moderate (45%) sequence identity templates are available. An essential element for success is a proper combination of a middle-sized (10-100 structures) conformational ensemble, derived either from Molecular dynamics or multiple homology model building. Consistent with our previous works, results show that solvent-site biased methods improve overall performance, but that results are still highly system dependent. Finally, our results also show that docking can select the correct receptor structure within the ensemble, underscoring the relevance of joint evaluation of both ligand pose and receptor conformation.Fil: Blanco Capurro, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Di Paola, Matías Ezequiel. Universidad de Buenos Aires; ArgentinaFil: Gamarra, Marcelo Daniel. Universidad de Buenos Aires; ArgentinaFil: Marti, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Modenutti, Carlos Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaOxford University Press2018-11info: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/11336/97997Blanco Capurro, Juan Ignacio; Di Paola, Matías Ezequiel; Gamarra, Marcelo Daniel; Marti, Marcelo Adrian; Modenutti, Carlos Pablo; An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes; Oxford University Press; Glycobiology; 29; 2; 11-2018; 124-1360959-6658CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/glycob/article/29/2/124/5165567info:eu-repo/semantics/altIdentifier/doi/10.1093/glycob/cwy102info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:32Zoai:ri.conicet.gov.ar:11336/97997instacron: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 09:33:32.329CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
title An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
spellingShingle An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
Blanco Capurro, Juan Ignacio
CARBOHYDRATES
DOCKING
HOMOLOGY-MODELING
LECTIN
MOLECULAR DYNAMICS
title_short An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
title_full An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
title_fullStr An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
title_full_unstemmed An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
title_sort An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes
dc.creator.none.fl_str_mv Blanco Capurro, Juan Ignacio
Di Paola, Matías Ezequiel
Gamarra, Marcelo Daniel
Marti, Marcelo Adrian
Modenutti, Carlos Pablo
author Blanco Capurro, Juan Ignacio
author_facet Blanco Capurro, Juan Ignacio
Di Paola, Matías Ezequiel
Gamarra, Marcelo Daniel
Marti, Marcelo Adrian
Modenutti, Carlos Pablo
author_role author
author2 Di Paola, Matías Ezequiel
Gamarra, Marcelo Daniel
Marti, Marcelo Adrian
Modenutti, Carlos Pablo
author2_role author
author
author
author
dc.subject.none.fl_str_mv CARBOHYDRATES
DOCKING
HOMOLOGY-MODELING
LECTIN
MOLECULAR DYNAMICS
topic CARBOHYDRATES
DOCKING
HOMOLOGY-MODELING
LECTIN
MOLECULAR DYNAMICS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Unraveling the structure of lectin-carbohydrate complexes is vital for understanding key biological recognition processes and development of glycomimetic drugs. Molecular Docking application to predict them is challenging due to their low affinity, hydrophilic nature and ligand conformational diversity. In the last decade several strategies, such as the inclusion of glycan conformation specific scoring functions or our developed solvent-site biased method, have improved carbohydrate docking performance but significant challenges remain, in particular, those related to receptor conformational diversity. In the present work we have analyzed conventional and solvent-site biased autodock4 performance concerning receptor conformational diversity as derived from different crystal structures (apo and holo), Molecular Dynamics snapshots and Homology-based models, for 14 different lectin-monosaccharide complexes. Our results show that both conventional and biased docking yield accurate lectin-monosaccharide complexes, starting from either apo or homology-based structures, even when only moderate (45%) sequence identity templates are available. An essential element for success is a proper combination of a middle-sized (10-100 structures) conformational ensemble, derived either from Molecular dynamics or multiple homology model building. Consistent with our previous works, results show that solvent-site biased methods improve overall performance, but that results are still highly system dependent. Finally, our results also show that docking can select the correct receptor structure within the ensemble, underscoring the relevance of joint evaluation of both ligand pose and receptor conformation.
Fil: Blanco Capurro, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Di Paola, Matías Ezequiel. Universidad de Buenos Aires; Argentina
Fil: Gamarra, Marcelo Daniel. Universidad de Buenos Aires; Argentina
Fil: Marti, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Modenutti, Carlos Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Unraveling the structure of lectin-carbohydrate complexes is vital for understanding key biological recognition processes and development of glycomimetic drugs. Molecular Docking application to predict them is challenging due to their low affinity, hydrophilic nature and ligand conformational diversity. In the last decade several strategies, such as the inclusion of glycan conformation specific scoring functions or our developed solvent-site biased method, have improved carbohydrate docking performance but significant challenges remain, in particular, those related to receptor conformational diversity. In the present work we have analyzed conventional and solvent-site biased autodock4 performance concerning receptor conformational diversity as derived from different crystal structures (apo and holo), Molecular Dynamics snapshots and Homology-based models, for 14 different lectin-monosaccharide complexes. Our results show that both conventional and biased docking yield accurate lectin-monosaccharide complexes, starting from either apo or homology-based structures, even when only moderate (45%) sequence identity templates are available. An essential element for success is a proper combination of a middle-sized (10-100 structures) conformational ensemble, derived either from Molecular dynamics or multiple homology model building. Consistent with our previous works, results show that solvent-site biased methods improve overall performance, but that results are still highly system dependent. Finally, our results also show that docking can select the correct receptor structure within the ensemble, underscoring the relevance of joint evaluation of both ligand pose and receptor conformation.
publishDate 2018
dc.date.none.fl_str_mv 2018-11
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/11336/97997
Blanco Capurro, Juan Ignacio; Di Paola, Matías Ezequiel; Gamarra, Marcelo Daniel; Marti, Marcelo Adrian; Modenutti, Carlos Pablo; An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes; Oxford University Press; Glycobiology; 29; 2; 11-2018; 124-136
0959-6658
CONICET Digital
CONICET
url http://hdl.handle.net/11336/97997
identifier_str_mv Blanco Capurro, Juan Ignacio; Di Paola, Matías Ezequiel; Gamarra, Marcelo Daniel; Marti, Marcelo Adrian; Modenutti, Carlos Pablo; An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes; Oxford University Press; Glycobiology; 29; 2; 11-2018; 124-136
0959-6658
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://academic.oup.com/glycob/article/29/2/124/5165567
info:eu-repo/semantics/altIdentifier/doi/10.1093/glycob/cwy102
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
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 13.069144

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