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SAbPred: a structure-based antibody prediction server

SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containin...

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Autores principales: Dunbar, James, Krawczyk, Konrad, Leem, Jinwoo, Marks, Claire, Nowak, Jaroslaw, Regep, Cristian, Georges, Guy, Kelm, Sebastian, Popovic, Bojana, Deane, Charlotte M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987913/
https://www.ncbi.nlm.nih.gov/pubmed/27131379
http://dx.doi.org/10.1093/nar/gkw361
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author Dunbar, James
Krawczyk, Konrad
Leem, Jinwoo
Marks, Claire
Nowak, Jaroslaw
Regep, Cristian
Georges, Guy
Kelm, Sebastian
Popovic, Bojana
Deane, Charlotte M.
author_facet Dunbar, James
Krawczyk, Konrad
Leem, Jinwoo
Marks, Claire
Nowak, Jaroslaw
Regep, Cristian
Georges, Guy
Kelm, Sebastian
Popovic, Bojana
Deane, Charlotte M.
author_sort Dunbar, James
collection PubMed
description SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred.
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spelling pubmed-49879132016-08-22 SAbPred: a structure-based antibody prediction server Dunbar, James Krawczyk, Konrad Leem, Jinwoo Marks, Claire Nowak, Jaroslaw Regep, Cristian Georges, Guy Kelm, Sebastian Popovic, Bojana Deane, Charlotte M. Nucleic Acids Res Web Server issue SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred. Oxford University Press 2016-07-08 2016-04-29 /pmc/articles/PMC4987913/ /pubmed/27131379 http://dx.doi.org/10.1093/nar/gkw361 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server issue
Dunbar, James
Krawczyk, Konrad
Leem, Jinwoo
Marks, Claire
Nowak, Jaroslaw
Regep, Cristian
Georges, Guy
Kelm, Sebastian
Popovic, Bojana
Deane, Charlotte M.
SAbPred: a structure-based antibody prediction server
title SAbPred: a structure-based antibody prediction server
title_full SAbPred: a structure-based antibody prediction server
title_fullStr SAbPred: a structure-based antibody prediction server
title_full_unstemmed SAbPred: a structure-based antibody prediction server
title_short SAbPred: a structure-based antibody prediction server
title_sort sabpred: a structure-based antibody prediction server
topic Web Server issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987913/
https://www.ncbi.nlm.nih.gov/pubmed/27131379
http://dx.doi.org/10.1093/nar/gkw361
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