ArticleBioinformatics (Oxford, England)2026
Evaluating deep learning based structure prediction methods on antibody-antigen complexes.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- ABAG-Rank: improving model selection of AlphaFold antibody-antigen complexes by learning to rank.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- AbTune: layer-wise selective fine-tuning of protein language models for antibodies.Briefings in bioinformatics · 2026Article
- DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning.Communications biology · 2026Article
- Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Confidence scoring for deep learning-predicted antibody-antigen complexes: AntiConf as a precision-driven metric.Briefings in bioinformatics · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- Assessing structural prediction accuracy for nanobody-small molecule complexes.Protein engineering, design & selection : PEDS · 2026Article
- AlphaFold3 at CASP16.Proteins · 2026Article
- AI-driven drug reposition for pathogens: a new paradigm in pandemic preparedness.Frontiers in chemistry · 2026Review
- What does AlphaFold3 learn about antibody and nanobody docking, and what remains unsolved?mAbs · 2025Article
- Improving B-cell epitope prediction.Drug discovery today · 2025Review
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Authors and funding
3 authors.
Funding
Abstract
motivationAlphaFold2 significantly improved the prediction of protein complex structures. However, its accuracy is lower for interactions without coevolutionary signals, such as host-pathogen and antibody-antigen interactions. Two strategies have been developed to address this limitation: massive sampling and replacing the evoformer with the pairformer, which does not rely on coevolution, as introduced in AlphaFold3, thereby enabling more structural reasoning by the network.
resultsIn this study, we benchmark structure prediction methods on unseen antibody-antigen complexes. We found that increased sampling improves the chances of generating a correct protein model, roughly in a log-linear manner. However, the internal quality estimates by AlphaFold often cannot identify the best predicted structures for each target, resulting in a significant loss of performance for the top-ranked protein model compared with the best model. For all methods, a significant challenge remains the identification of the best model. We also show that AlphaFold3 outperforms AlphaFold2, Boltz-1, and Chai-1. Furthermore, AlphaFold3 performance declines significantly for complexes that lack structural similarity to the training set, indicating that it has to some extent learned to detect remote structural similarities. AVAILABILITY AND IMPLEMENTATION: All code is available from github.com/samuelfromm/abag-benchmark-set/ and all data from DOI: 10.5281/zenodo.17978681. The latter repository also contains the code.
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