DLAB-deep learning methods for structure-based virtual screening of antibodies
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Abstract
MOTIVATION: Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders. RESULTS: We demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies. AVAILABILITY AND IMPLEMENTATION: The DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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DLAB localizes the interface of an antibody and antigen and uses a 3D convolution-based neural network to generate binding scores.
“For example, DLAB [52] localizes the interface of an antibody and antigen and uses a 3D convolution-based neural network to generate binding scores.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [52]
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Computational and artificial intelligence-based methods for antibody development
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“…still no generalizable solution that can reconstruct complexes from separated antibody and antigen structures, with the performance dropping significantly for structure prediction models (Schneider et al. 2021; Jin, Barzilay, and Jaakkola 17--23 Jul 2022; Sircar and Gray 2010; Krawczyk et al. 2014).”
- Authors
- Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- AstraZeneca (United Kingdom) (GB)
- Corresponding author
- Deane, Charlotte M
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-09-21
- Volume
- 38
- Issue
- 2
- Pages
- 377-383
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC8723137
- OpenAlex
- W3197596721
- Cited by
- 102
- References
- 41
- Citations by year
- 2026: 9 · 2025: 24 · 2024: 22 · 2023: 26 · 2022: 18 · 2021: 3
- FWCI
- 9.06
- 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
- Medical Research Council (EP/L016044/1)
- AstraZeneca
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Keywords
- structure-based virtual screening, antibody-antigen docking, deep learning, pose ranking
- MeSH terms
- Deep Learning, Antibodies, Antigens, Software
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