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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/120962
Ver los metadatos del registro completo
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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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1844614097547558912 |
score |
13.070432 |