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ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation
Open accessPreprint — published as 10.1093/bioinformatics/btac016 · Paper page
Abstract
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 is 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. 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 2.49Å, which drops to 2.05Å when considering only its 76% most confident predictions.
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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
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Research works printed but not linked: 16
- 10.1038/nrd.2017.227 · DOI
- 2019, Antibody structure and function: The basis for engineering therapeutics, Antibodies, 8, 55, 10.3390/antib8040055 · DOI
- 2016, Automated antibody structure prediction with data–driven accuracy estimation. In MAbs, 8, 1259
- Jeffrey A Ruffolo , Jeremias Sulam , and Jeffrey J Gray . Antibody structure prediction using interpretable deep learning. bioRxiv, 2021. · DOI
- Constantin Schneider , Andrew Buchanan , Bruck Taddese , and Charlotte M Deane . DLAB-deep learning methods for structure-based virtual screening of antibodies. bioRxiv, 2021. · DOI
- Sarah A Robinson , Matthew IJ Raybould , Claire Marks , Constantin Schneider , Wing Ki Wong , and Charlotte M Deane . Epitope profiling of coronavirus-binding antibodies using computational structural modelling. bioRxiv, 2021. · DOI
- 2014, Antibody modeling assessment ii. structures and models, Proteins: Structure, Function, and Bioinformatics, 82, 1563, 10.1002/prot.24554 · DOI · Paper page
- 2019, Critical assessment of methods of protein structure prediction (CASP)—Round XIII, Proteins: Structure, Function, and Bioinformatics, 87, 1011, 10.1002/prot.25823 · DOI · Paper page
- 10.1073/pnas.1914677117 · DOI · Paper page
- 2020, Improved protein structure prediction using potentials from deep learning, Nature, 577, 706, 10.1038/s41586-019-1923-7 · DOI · Paper page
- 2021, A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups, arXiv preprint
- 2021, E (n) equivariant graph neural networks, arXiv preprint
- 10.1093/nar/gkt1043 · DOI · Paper page
- 10.1093/nar/28.1.235 · DOI · Paper page
- 2003, IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains, Developmental & Comparative Immunology, 27, 55, 10.1016/S0145-305X(02)00039-3 · DOI
- 2015, ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB, Journal of chemical theory and computation, 11, 3696, 10.1021/acs.jctc.5b00255 · DOI
Metadata
- 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
- openRxiv
- Published
- 2021-07-26
- Volume
- 38
- Issue
- 7
- Pages
- 1877-1880
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC8963302
- OpenAlex
- W4210520848
- Cited by
- 157
- References
- 1
- 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
- cc-by
- 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, EP/S024093/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Immunology and Microbiology › Immunology › 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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