ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation
Open accessAbstract
Computational modeling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modeling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modeling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard 4 steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction. ABodyBuilder then annotates the 'confidence' of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modeled within a root-mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on 4 separate datasets, including the 11 antibodies from the Antibody Modeling Assessment-II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub-Angstrom predictions for the 'canonical' CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision-making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder .
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ABodyBuilder is an automated antibody homology modeling pipeline that follows four steps: template selection, orientation prediction, CDR loop modeling, and side chain prediction.
“For example, ABodyBuilder [41], an automated antibody homology modeling pipeline, follows four steps: template selection, orientation prediction, CDR loop modeling, and side chain prediction.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [41]
DeepAb achieved a prediction accuracy for H3 loop at RMSD of 2.33 Å while ABlooper and ABodyBuilder achieved RMSD of 2.49 Å and 3.25 Å, respectively.
“In the end, DeepAb achieved a prediction accuracy for H3 loop at rootmean-square deviation (RMSD) of 2.33 Å while ABlooper and ABodyBuilder achieved RMSD of 2.49 Å and 3.25 Å, respectively [41].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [41]
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- Authors
- Leem, J., Dunbar, J., Georges, G., Shi, J., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
- UCB Pharma (United Kingdom) (GB)
- Corresponding author
- Deane, Charlotte M.
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2016-08-30
- Volume
- 8
- Issue
- 7
- Pages
- 1259-1268
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC5058620
- OpenAlex
- W2474661075
- Cited by
- 238
- References
- 47
- Citations by year
- 2026: 11 · 2025: 24 · 2024: 37 · 2023: 39 · 2022: 44 · 2021: 27 · 2020: 17 · 2019: 19 · 2018: 12 · 2017: 8
- FWCI
- 6.75
- Type
- journal article
- Language
- en
- Update notices
- correction (2018-03-29)
- Open access
- gold
- doi.org
- Licence
- Locations
- mAbs (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (1231608)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- antibody structure prediction, template selection, structural motifs
- MeSH terms
- Algorithms, Antibodies, Computer Simulation, Models, Molecular, Drug Design, Complementarity Determining Regions
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