ArticleScience advances2025
AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Tumor-infiltrating plasma cell profiling after PD-1 blockade reveals tumor-specific antibodies.Cancer cell · 2026Article
- Pocket restraints guided by B-cell epitope prediction improve Chai-1 antibody-antigen structure modeling.Protein science : a publication of the Protein Society · 2026Article
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- IgPose: a generative data-augmented pipeline for robust immunoglobulin-antigen binding prediction.Bioinformatics (Oxford, England) · 2026Article
- Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025Article
- Experimental Study and Molecular Modeling of Antibody Interactions with Different Fluoroquinolones.International journal of molecular sciences · 2025Article
- Technologies for Monoclonal Antibody Discovery and Development.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
B cell epitope prediction tools are crucial for designing vaccines and disease diagnostics. However, predicting which antigens a specific antibody binds to and their exact binding sites (epitopes) remains challenging. Here, we present AbEpiTope-1.0, a tool for antibody-specific B cell epitope prediction, using AlphaFold for structural modeling and inverse folding for machine learning models. On a dataset of 1730 antibody-antigen complexes, AbEpiTope-1.0 outperforms AlphaFold in predicting modeled antibody-antigen interface accuracy. By creating swapped antibody-antigen complex structures for each antibody-antigen complex using incorrect antibodies, we show that predicted accuracies are sensitive to antibody input. Furthermore, a model variant optimized for antibody target prediction-differentiating true from swapped complexes-achieved an accuracy of 61.21% in correctly identifying antibody-antigen pairs. The tool evaluates hundreds of structures in minutes, providing researchers with a resource for screening antibodies targeting specific antigens. AbEpiTope-1.0 is freely available as a web server and software.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.