ArticleProtein science : a publication of the Protein Society2024
Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers.
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Who cites it
95 citing papers in PubMed.
- Article
- Article
- Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47-55.Proteins · 2026Article
- AlphaFold and Docking Approaches for Antibody-Antigen and Other Targets: Insights From CAPRI Rounds 47-55.Proteins · 2026Article
- Massive Sampling Strategy for Antibody-Antigen Targets in CAPRI Round 55 With MassiveFold.Proteins · 2026Article
- Evaluating AlphaFold Tools and Related Scoring Functions for Protein-peptide Complex Prediction.Genomics, proteomics & bioinformatics · 2026Article
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Antigen-Detected NMR for Minimal Epitope Engineering and Structure-Guided Selection of a NaAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AVIDbase: A biologically accurate structural dataset of nanobody-antigen complexes.Protein science : a publication of the Protein Society · 2026Article
- Efficient in vivo assembly of DNA encoded polyvalent BTEs for dual antigen targeting for broadening therapeutic impact in ovarian cancer.Molecular therapy : the journal of the American Society of Gene Therapy · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics.bioRxiv : the preprint server for biology · 2026Article
- AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Deep mining of the human antibody repertoire identifies frequent and genetically diverse CDRH3 topologies targetable by vaccination.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights.Briefings in bioinformatics · 2026Article
- In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer.Nature communications · 2026Article
- A high-speed attention network for MHC-bound peptide identification and 3D modeling.Cell reports methods · 2026Article
- Evaluating deep learning based structure prediction methods on antibody-antigen complexes.Bioinformatics (Oxford, England) · 2026Article
35 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.
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