Benchmark datasets of immune receptor-epitope structural complexes
- Autores
- Mahajan, Swapnil; Yan, Zhen; Jespersen, Martin Closter; Jensen, Kamilla Kjærgaard; Marcatili, Paolo; Nielsen, Morten; Sette, Alessandro; Peters, Bjoern
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Background: The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results: To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions: SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.
Fil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Yan, Zhen. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca
Fil: Jensen, Kamilla Kjærgaard. Technical University of Denmark; Dinamarca
Fil: Marcatili, Paolo. Technical University of Denmark; Dinamarca
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. 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
Fil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados Unidos
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos - Materia
-
ANTIBODY
EPITOPE
EPITOPE PREDICTION
IEDB
MHC
PROTEIN STRUCTURES
TCR - 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:11336/124580
Ver los metadatos del registro completo
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oai:ri.conicet.gov.ar:11336/124580 |
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3498 |
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CONICET Digital (CONICET) |
spelling |
Benchmark datasets of immune receptor-epitope structural complexesMahajan, SwapnilYan, ZhenJespersen, Martin ClosterJensen, Kamilla KjærgaardMarcatili, PaoloNielsen, MortenSette, AlessandroPeters, BjoernANTIBODYEPITOPEEPITOPE PREDICTIONIEDBMHCPROTEIN STRUCTURESTCRhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Background: The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results: To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions: SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.Fil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Yan, Zhen. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Jespersen, Martin Closter. Technical University of Denmark; DinamarcaFil: Jensen, Kamilla Kjærgaard. Technical University of Denmark; DinamarcaFil: Marcatili, Paolo. Technical University of Denmark; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. 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; ArgentinaFil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosBioMed Central2019-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/124580Mahajan, Swapnil; Yan, Zhen; Jespersen, Martin Closter; Jensen, Kamilla Kjærgaard; Marcatili, Paolo; et al.; Benchmark datasets of immune receptor-epitope structural complexes; BioMed Central; BMC Bioinformatics; 20; 1; 10-2019; 1-71471-2105CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3109-6info:eu-repo/semantics/altIdentifier/doi/10.1186/s12859-019-3109-6info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:14Zoai:ri.conicet.gov.ar:11336/124580instacron: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:34982025-09-03 10:03:14.319CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Benchmark datasets of immune receptor-epitope structural complexes |
title |
Benchmark datasets of immune receptor-epitope structural complexes |
spellingShingle |
Benchmark datasets of immune receptor-epitope structural complexes Mahajan, Swapnil ANTIBODY EPITOPE EPITOPE PREDICTION IEDB MHC PROTEIN STRUCTURES TCR |
title_short |
Benchmark datasets of immune receptor-epitope structural complexes |
title_full |
Benchmark datasets of immune receptor-epitope structural complexes |
title_fullStr |
Benchmark datasets of immune receptor-epitope structural complexes |
title_full_unstemmed |
Benchmark datasets of immune receptor-epitope structural complexes |
title_sort |
Benchmark datasets of immune receptor-epitope structural complexes |
dc.creator.none.fl_str_mv |
Mahajan, Swapnil Yan, Zhen Jespersen, Martin Closter Jensen, Kamilla Kjærgaard Marcatili, Paolo Nielsen, Morten Sette, Alessandro Peters, Bjoern |
author |
Mahajan, Swapnil |
author_facet |
Mahajan, Swapnil Yan, Zhen Jespersen, Martin Closter Jensen, Kamilla Kjærgaard Marcatili, Paolo Nielsen, Morten Sette, Alessandro Peters, Bjoern |
author_role |
author |
author2 |
Yan, Zhen Jespersen, Martin Closter Jensen, Kamilla Kjærgaard Marcatili, Paolo Nielsen, Morten Sette, Alessandro Peters, Bjoern |
author2_role |
author author author author author author author |
dc.subject.none.fl_str_mv |
ANTIBODY EPITOPE EPITOPE PREDICTION IEDB MHC PROTEIN STRUCTURES TCR |
topic |
ANTIBODY EPITOPE EPITOPE PREDICTION IEDB MHC PROTEIN STRUCTURES TCR |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3 |
dc.description.none.fl_txt_mv |
Background: The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results: To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions: SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre. Fil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Yan, Zhen. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca Fil: Jensen, Kamilla Kjærgaard. Technical University of Denmark; Dinamarca Fil: Marcatili, Paolo. Technical University of Denmark; Dinamarca Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. 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 Fil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos |
description |
Background: The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results: To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions: SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10 |
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/11336/124580 Mahajan, Swapnil; Yan, Zhen; Jespersen, Martin Closter; Jensen, Kamilla Kjærgaard; Marcatili, Paolo; et al.; Benchmark datasets of immune receptor-epitope structural complexes; BioMed Central; BMC Bioinformatics; 20; 1; 10-2019; 1-7 1471-2105 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/124580 |
identifier_str_mv |
Mahajan, Swapnil; Yan, Zhen; Jespersen, Martin Closter; Jensen, Kamilla Kjærgaard; Marcatili, Paolo; et al.; Benchmark datasets of immune receptor-epitope structural complexes; BioMed Central; BMC Bioinformatics; 20; 1; 10-2019; 1-7 1471-2105 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://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3109-6 info:eu-repo/semantics/altIdentifier/doi/10.1186/s12859-019-3109-6 |
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 |
BioMed Central |
publisher.none.fl_str_mv |
BioMed Central |
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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1842269789875077120 |
score |
13.13397 |