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.
Highlights
Antibody–antigen interactions are an important component of our immune response to pathogens[1], and understanding the structural basis of antibody–antigen interactions can help in designing more potent therapeutics
We superposed each antibody onto the native antibody in 16 other antibody–antigen complexes, generating 289 total pairs to discriminate
We have presented the first structure-based cross-docking study focused on discrimination of protein binders from non-binders by identifying native antibody–antigen interaction pairs among cognate and non-cognate complexes
Summary
Antibody–antigen interactions are an important component of our immune response to pathogens[1], and understanding the structural basis of antibody–antigen interactions can help in designing more potent therapeutics. We present a computational cross-docking study to discriminate binders from non-binders by identifying native antibody–antigen interaction pairs among cognate and non-cognate complexes. The ability to distinguish binding from non-binding interfaces was tested in Critical Assessment of PRediction of Interactions (CAPRI) through a challenge to predict successful high-affinity binders from a set of designed protein–protein interfaces and distinguishing natural interfaces from unsuccessful Rosetta-designed interfaces[14]. Both these challenges were difficult, and no computational method was able to identify the design responsible for the successful binder. Previous cross-docking attempts[15, 16] on a dataset of diverse protein–protein complexes found the prediction of antibody–antigen interaction pairs to be especially difficult
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