ArticleScientific reports2017
Structure-based cross-docking analysis of antibody-antigen interactions.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Integrating deep learning with physics based modeling enables high precision antibody antigen interface prediction.Scientific reports · 2026Article
- AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.Science advances · 2025Article
- Unlocking the potential ofFrontiers in bioinformatics · 2025Review
- ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking.Briefings in bioinformatics · 2024Article
- Structural Modeling of Adaptive Immune Responses to Infection.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods.JMIR bioinformatics and biotechnology · 2022Article
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- Functional antibody characterization via direct structural analysis and information-driven protein-protein docking.Proteins · 2022Article
- DLAB: deep learning methods for structure-based virtual screening of antibodies.Bioinformatics (Oxford, England) · 2022Article
- From complete cross-docking to partners identification and binding sites predictions.PLoS computational biology · 2022Article
- An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants.Structure (London, England : 1993) · 2021Article
- Article
- Computational Design of a Novel VLP-Based Vaccine for Hepatitis B Virus.Frontiers in immunology · 2020Article
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- Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes.Frontiers in immunology · 2019Article
- A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.Scientific reports · 2018Article
- Homology modeling identified for purported drug targets to the neuroprotective effects of levodopa and asiaticoside-D in degenerated cerebral ganglions ofIndian journal of pharmacologyArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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