ArticleBioinformatics (Oxford, England)2024
Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47-55.Proteins · 2026Article
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- Article
- Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes.Journal of chemical information and modeling · 2026Article
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Integrating AlphaFold2, RoseTTAFold2, and HADDOCK to refine Protein-Protein Interaction (PPI) candidate selection: A case study with ZWINT.Biochemistry and biophysics reports · 2026Article
- Article
- Improving B-cell epitope prediction.Drug discovery today · 2025Review
- Structure-based Predictions of Conformational B Cell Epitopes by Protein Language Model and Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.PLoS computational biology · 2025Article
- Epitope Mapping with Sidewinder: An XL-MS and Structural Modeling Approach.International journal of molecular sciences · 2025Article
- Nanobody engineering: computational modelling and design for biomedical and therapeutic applications.FEBS open bio · 2025Review
- Improved prediction of antibody and their complexes with clustered generative modelling ensembles.Bioinformatics advances · 2025Article
- Investigating and evaluating potential antigen binding sites for monoclonal anti-HER2 antibodies: The LightDock approach.Computational and structural biotechnology journal · 2025Article
- The Application of Machine Learning on Antibody Discovery and Optimization.Molecules (Basel, Switzerland) · 2024Review
- PD-1 Targeted Antibody Discovery Using AI Protein Diffusion.Technology in cancer research & treatmentArticle
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Authors and funding
6 authors.
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Abstract
motivationAntibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking.
resultsIn an information-driven scenario, we find that HADDOCK can generate accurate models of antibody-antigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high-quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline. AVAILABILITY AND IMPLEMENTATION: The source code of HADDOCK3 is freely available at github.com/haddocking/haddock3. The code to generate and analyse the data is available at github.com/haddocking/ai-antibodies. The full runs, including docking models from all modules of a workflow have been deposited in our lab collection (data.sbgrid.org/labs/32/1139) at the SBGRID data repository.
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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.