ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation
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Abstract
MOTIVATION: Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen-binding function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures. RESULTS: In this work, we present ABlooper, an end-to-end equivariant deep learning-based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49 Å, which drops to 2.05 Å when considering only its 75% most confident predictions. AVAILABILITY AND IMPLEMENTATION: https://github.com/oxpig/ABlooper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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ABlooper uses a graph neural network, which can directly work on 3D coordinate data from structure files, to predict the positions of all backbone atoms (Ca, N, C, and Cb) for the six CDR loops.
“ABlooper [42] uses a graph neural network, which can directly work on 3D coordinate data from structure files, to predict the positions of all backbone atoms (Ca, N, C, and Cb) for the six CDR loops.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [42]
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- Authors
- Abanades, B., Georges, G., Bujotzek, A., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Roche Pharma AG (Germany) (DE)
- Corresponding author
- Abanades, Brennan
- Deane, Charlotte M
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-01-31
- Volume
- 38
- Issue
- 7
- Pages
- 1877-1880
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC8963302
- OpenAlex
- W4210520848
- Cited by
- 157
- References
- 25
- Citations by year
- 2026: 23 · 2025: 30 · 2024: 37 · 2023: 46 · 2022: 21
- FWCI
- 10.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/S024093/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- antibody structure prediction, hypervariable region, confidence estimation, antibody-antigen binding, protein structure prediction
- MeSH terms
- Antibodies, Models, Molecular, Protein Conformation, Complementarity Determining Regions
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