Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes

Autores
Jespersen, Martin Closter; Mahajan, Swapnil; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.
Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca
Fil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; Dinamarca
Fil: Marcatili, Paolo. Technical University of Denmark; Dinamarca
Materia
ANTIBODY
ANTIBODY SPECIFIC EPITOPE PREDICTION
ANTIGEN
B CELL EPITOPE
PARATOPE
PREDICTION
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:11336/120962

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network_name_str CONICET Digital (CONICET)
spelling Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexesJespersen, Martin ClosterMahajan, SwapnilPeters, BjoernNielsen, MortenMarcatili, PaoloANTIBODYANTIBODY SPECIFIC EPITOPE PREDICTIONANTIGENB CELL EPITOPEPARATOPEPREDICTIONhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.Fil: Jespersen, Martin Closter. Technical University of Denmark; DinamarcaFil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; DinamarcaFil: Marcatili, Paolo. Technical University of Denmark; DinamarcaFrontiers Media S.A.2019-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/120962Jespersen, Martin Closter; Mahajan, Swapnil; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo; Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes; Frontiers Media S.A.; Frontiers in Immunology; 10; FEB; 2-2019; 1-101664-3224CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2019.00298info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fimmu.2019.00298/fullinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:15:49Zoai:ri.conicet.gov.ar:11336/120962instacron: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 10:15:49.476CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
title Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
spellingShingle Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
Jespersen, Martin Closter
ANTIBODY
ANTIBODY SPECIFIC EPITOPE PREDICTION
ANTIGEN
B CELL EPITOPE
PARATOPE
PREDICTION
title_short Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
title_full Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
title_fullStr Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
title_full_unstemmed Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
title_sort Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes
dc.creator.none.fl_str_mv Jespersen, Martin Closter
Mahajan, Swapnil
Peters, Bjoern
Nielsen, Morten
Marcatili, Paolo
author Jespersen, Martin Closter
author_facet Jespersen, Martin Closter
Mahajan, Swapnil
Peters, Bjoern
Nielsen, Morten
Marcatili, Paolo
author_role author
author2 Mahajan, Swapnil
Peters, Bjoern
Nielsen, Morten
Marcatili, Paolo
author2_role author
author
author
author
dc.subject.none.fl_str_mv ANTIBODY
ANTIBODY SPECIFIC EPITOPE PREDICTION
ANTIGEN
B CELL EPITOPE
PARATOPE
PREDICTION
topic ANTIBODY
ANTIBODY SPECIFIC EPITOPE PREDICTION
ANTIGEN
B CELL EPITOPE
PARATOPE
PREDICTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.
Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca
Fil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; Dinamarca
Fil: Marcatili, Paolo. Technical University of Denmark; Dinamarca
description B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.
publishDate 2019
dc.date.none.fl_str_mv 2019-02
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/11336/120962
Jespersen, Martin Closter; Mahajan, Swapnil; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo; Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes; Frontiers Media S.A.; Frontiers in Immunology; 10; FEB; 2-2019; 1-10
1664-3224
CONICET Digital
CONICET
url http://hdl.handle.net/11336/120962
identifier_str_mv Jespersen, Martin Closter; Mahajan, Swapnil; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo; Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes; Frontiers Media S.A.; Frontiers in Immunology; 10; FEB; 2-2019; 1-10
1664-3224
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2019.00298
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fimmu.2019.00298/full
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 Frontiers Media S.A.
publisher.none.fl_str_mv Frontiers Media S.A.
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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