Antibody structure prediction using interpretable deep learning
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Abstract
Abstract Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. In recent years, deep learning methods have driven significant advances in general protein structure prediction. Here, we present DeepAb, a deep learning method for predicting accurate antibody F V structures from sequence. We evaluate DeepAb on two benchmark sets – one balanced for structural diversity and the other composed of clinical-stage therapeutic antibodies – and find that our method consistently outperforms the leading alternatives. Previous deep learning methods have operated as “black boxes” and offered few insights into their predictions. By introducing a directly interpretable attention mechanism, we show that our network attends to physically important residue pairs. For example, in prediction of one CDR H3 residue conformation, the network attends to proximal aromatics and a key hydrogen bonding interaction that constrain the loop conformation. Finally, we present a novel mutant scoring metric derived from network confidence and show that for a particular antibody, all eight of the top-ranked mutations improve binding affinity. These results suggest that this model will be useful for a broad range of antibody prediction and design tasks. Significance Accurate structure models are critical for understanding the properties of potential therapeutic antibodies. Conventional methods for protein structure determination require significant investments of time and resources and may fail. Although greatly improved, methods for general protein structure prediction still cannot consistently provide the accuracy necessary to understand or design antibodies. We present a deep learning method for antibody structure prediction and demonstrate improvement over alternatives on diverse, therapeutically relevant benchmarks. In addition to its improved accuracy, our method reveals interpretable outputs about specific amino acids and residue interactions that should facilitate design of novel therapeutic antibodies.
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tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence HomologsPreprint
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Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder CompetitionPreprint
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Self-citationLearned features of antibody-antigen binding affinity
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Self-citationStructure-based neural network protein–carbohydrate interaction predictions at the residue level
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VHH Structural Modelling Approaches: A Critical Review
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Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines
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Advances in antibody discovery from human BCR repertoires
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Innovations and trends in antibody repertoire analysis
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A comprehensive overview of recent advances in generative models for antibodies
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Learning the Language of Antibody HypervariabilityPreprint
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- Authors
- Ruffolo, J. A., Sulam, J., & Gray, J. J.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Gray, Jeffrey J.
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 3
- Issue
- 2
- Pages
- 100406
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC8848015
- OpenAlex
- W4226108318
- Cited by
- 216
- References
- 75
- Citations by year
- 2026: 26 · 2025: 42 · 2024: 50 · 2023: 58 · 2022: 37 · 2021: 3
- FWCI
- 11.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Maryland Advanced Research Computing Center
- National Institutes of Health (R01-GM078221, T32-GM008403)
- AstraZeneca plc
- 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, therapeutic antibodies, explainable deep learning, attention mechanism, binding affinity, rational antibody design, hydrogen bonding
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