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Structure-based cross-docking analysis of antibody–antigen interactions

Antibody–antigen interactions are critical to our immune response, and understanding the structure-based biophysical determinants for their binding specificity and affinity is of fundamental importance. We present a computational structure-based cross-docking study to test the identification of nati...

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Autores principales: Kilambi, Krishna Praneeth, Gray, Jeffrey J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557897/
https://www.ncbi.nlm.nih.gov/pubmed/28811664
http://dx.doi.org/10.1038/s41598-017-08414-y
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author Kilambi, Krishna Praneeth
Gray, Jeffrey J.
author_facet Kilambi, Krishna Praneeth
Gray, Jeffrey J.
author_sort Kilambi, Krishna Praneeth
collection PubMed
description Antibody–antigen interactions are critical to our immune response, and understanding the structure-based biophysical determinants for their binding specificity and affinity is of fundamental importance. We present a computational structure-based cross-docking study to test the identification of native antibody–antigen interaction pairs among cognate and non-cognate complexes. We picked a dataset of 17 antibody–antigen complexes of which 11 have both bound and unbound structures available, and we generated a representative ensemble of cognate and non-cognate complexes. Using the Rosetta interface score as a classifier, the cognate pair was the top-ranked model in 80% (14/17) of the antigen targets using bound monomer structures in docking, 35% (6/17) when using unbound, and 12% (2/17) when using the homology-modeled backbones to generate the complexes. Increasing rigid-body diversity of the models using RosettaDock’s local dock routine lowers the discrimination accuracy with the cognate antibody–antigen pair ranking in bound and unbound models but recovers additional top-ranked cognate complexes when using homology models. The study is the first structure-based cross-docking attempt aimed at distinguishing antibody–antigen binders from non-binders and demonstrates the challenges to address for the methods to be widely applicable to supplement high-throughput experimental antibody sequencing workflows.
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spelling pubmed-55578972017-08-16 Structure-based cross-docking analysis of antibody–antigen interactions Kilambi, Krishna Praneeth Gray, Jeffrey J. Sci Rep Article Antibody–antigen interactions are critical to our immune response, and understanding the structure-based biophysical determinants for their binding specificity and affinity is of fundamental importance. We present a computational structure-based cross-docking study to test the identification of native antibody–antigen interaction pairs among cognate and non-cognate complexes. We picked a dataset of 17 antibody–antigen complexes of which 11 have both bound and unbound structures available, and we generated a representative ensemble of cognate and non-cognate complexes. Using the Rosetta interface score as a classifier, the cognate pair was the top-ranked model in 80% (14/17) of the antigen targets using bound monomer structures in docking, 35% (6/17) when using unbound, and 12% (2/17) when using the homology-modeled backbones to generate the complexes. Increasing rigid-body diversity of the models using RosettaDock’s local dock routine lowers the discrimination accuracy with the cognate antibody–antigen pair ranking in bound and unbound models but recovers additional top-ranked cognate complexes when using homology models. The study is the first structure-based cross-docking attempt aimed at distinguishing antibody–antigen binders from non-binders and demonstrates the challenges to address for the methods to be widely applicable to supplement high-throughput experimental antibody sequencing workflows. Nature Publishing Group UK 2017-08-15 /pmc/articles/PMC5557897/ /pubmed/28811664 http://dx.doi.org/10.1038/s41598-017-08414-y Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kilambi, Krishna Praneeth
Gray, Jeffrey J.
Structure-based cross-docking analysis of antibody–antigen interactions
title Structure-based cross-docking analysis of antibody–antigen interactions
title_full Structure-based cross-docking analysis of antibody–antigen interactions
title_fullStr Structure-based cross-docking analysis of antibody–antigen interactions
title_full_unstemmed Structure-based cross-docking analysis of antibody–antigen interactions
title_short Structure-based cross-docking analysis of antibody–antigen interactions
title_sort structure-based cross-docking analysis of antibody–antigen interactions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557897/
https://www.ncbi.nlm.nih.gov/pubmed/28811664
http://dx.doi.org/10.1038/s41598-017-08414-y
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