ArticleProtein science : a publication of the Protein Society2024
A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.
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 18 papers.
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18 citing papers in PubMed.
- Article
- A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation.iScience · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- High-Affinity Nanobody Against the LEDGF PWWP Domain Inhibits Chromatin Binding In Vitro.Biomolecules · 2026Article
- Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights.Briefings in bioinformatics · 2026Article
- Evaluating deep learning based structure prediction methods on antibody-antigen complexes.Bioinformatics (Oxford, England) · 2026Article
- Improving B-cell epitope prediction.Drug discovery today · 2025Review
- Evaluation of AlphaFold modeling for elucidation of nanobody-peptide epitope interactions.The Journal of biological chemistry · 2025Article
- Evaluation of Alphafold modeling for elucidation of nanobody-peptide epitope interactions.bioRxiv : the preprint server for biology · 2025Article
- Unveiling Interaction Signatures Across Viral Pathogens through VASCO: Viral Antigen-Antibody Structural COmplex dataset.bioRxiv : the preprint server for biology · 2025Article
- Improved prediction of antibody and their complexes with clustered generative modelling ensembles.Bioinformatics advances · 2025Article
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens.Science advances · 2024Article
- Improved deep learning prediction of antigen-antibody interactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.Protein science : a publication of the Protein Society · 2024Article
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3 authors.
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Abstract
The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer and RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower quality display significant structural biases at the level of tertiary motifs (TERMs) toward having fewer structural matches in non-antibody-containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that the scarcity of interfacial geometry data in the structural database may currently limit the application of ML to the prediction of antibody-antigen interactions.
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