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

Abstract 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 identificatio...

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Autores principales:	Krishna Praneeth Kilambi, Jeffrey J. Gray
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f7fd1839aab14bafae9116c755930e14
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spelling	oai:doaj.org-article:f7fd1839aab14bafae9116c755930e142021-12-02T15:05:27ZStructure-based cross-docking analysis of antibody–antigen interactions10.1038/s41598-017-08414-y2045-2322https://doaj.org/article/f7fd1839aab14bafae9116c755930e142017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08414-yhttps://doaj.org/toc/2045-2322Abstract 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.Krishna Praneeth KilambiJeffrey J. GrayNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Krishna Praneeth Kilambi Jeffrey J. Gray Structure-based cross-docking analysis of antibody–antigen interactions
description	Abstract 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.
format	article
author	Krishna Praneeth Kilambi Jeffrey J. Gray
author_facet	Krishna Praneeth Kilambi Jeffrey J. Gray
author_sort	Krishna Praneeth Kilambi
title	Structure-based cross-docking analysis of antibody–antigen interactions
title_short	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_sort	structure-based cross-docking analysis of antibody–antigen interactions
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f7fd1839aab14bafae9116c755930e14
work_keys_str_mv	AT krishnapraneethkilambi structurebasedcrossdockinganalysisofantibodyantigeninteractions AT jeffreyjgray structurebasedcrossdockinganalysisofantibodyantigeninteractions
_version_	1718388868753915904

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

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