Towards the accurate modelling of antibody−antigen complexes from sequence using machine learning and information-driven docking
Open accessPublished version of a preprint
Abstract
Motivation: Antibody−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. Results: In 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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1× · see citing works
“We report results for three confidence metrics: the AlphaFold2 average predicted local distance difference test (pLDDT; Jumper et al. 2021, Tunyasuvunakool et al. 2021), averaged separately over paratope and epitope residues, and the ABodyBuilder2 root mean squared predicted error (RMSPE; Abanades…”
MethodZDOCK: An initial‐stage protein‐docking algorithm
Chen, R., Li, L., & Weng, Z. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10389 · Cited by 1,393 · see citing works
CAPRI: A Critical Assessment of PRedicted Interactions
Janin, J., Henrick, K., Moult, J., Eyck, L. T., Sternberg, M. J. E., Vajda, S., Vakser, I., & Wodak, S. J. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10381 · Cited by 703 · see citing works
Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424 · see citing works
Self-citationComputational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., & Ma, J. (2022) · doi:10.1101/2022.07.10.499510 · Cited by 151 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Linear antibody epitope prediction using AlphaFold2
DeRoo, J., Terry, J. S., Zhao, N., Stasevich, T. J., Snow, C., & Geiss, B. J. (2026) · eLife · doi:10.7554/elife.98369 · Cited by 1 · see citing works
- Authors
- Giulini, M., Schneider, C., Cutting, D., Desai, N., Deane, C. M., & Bonvin, A. M. J. J.
- Affiliations
- Utrecht University (NL)
- Exscientia Plc , Oxford OX4 4GE,
- Corresponding author
- Bonvin, Alexandre M J J
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-09-30
- Volume
- 40
- Issue
- 10
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC11483107
- OpenAlex
- W4402984101
- Cited by
- 41
- References
- 9
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 12
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- European Union Horizon 2020 (823830, 101093290)
- EGI-ACE (101017567)
- Netherlands e-Science Center (027.020.G13)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- immune complexes, protein-protein docking, AlphaFold2, haddock, therapeutic antibody design, hypervariable region, epitope targeting, ZDOCK
- MeSH terms
- Machine Learning, Antibodies, Antigen-Antibody Complex, Epitopes, Antigens, Software, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
