Epi4Ab: a data-driven prediction model of conformational epitopes for specific antibody VH/VL families and CDRs sequences
Open accessPublished version of a preprint
Abstract
Antibodies recognize antigens via complementary and structurally dependent mechanisms. Therefore, inclusion of antibody inputs is crucial for accurate epitope prediction. Given the limited availability of antibody-antigen complex structures, any epitope prediction model will require minimal yet sufficient antibody inputs to ensure precise epitope identification. To address this need, we introduce Epi4Ab, an antibody-specific epitope prediction model that focuses on identifying unique in-contact antigen residues for a given antibody. Epi4Ab requires minimal antibody inputs, specifically VH/VL families and complementarity-determining region sequences.
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Cites
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
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Improving B-cell epitope prediction and its application to global antibody–antigen docking
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- Authors
- Tran, N. D., Subramani, K., & Su, C. T.-T.
- Affiliations
- Agency for Science, Technology and Research (SG)
- Corresponding author
- Su, Chinh Tran-To
- ORCID
- Su, Chinh Tran-To
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2025-07-10
- Volume
- 17
- Issue
- 1
- Pages
- 2531227-2531227
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC12247109
- OpenAlex
- W4412156380
- Cited by
- 0
- References
- 4
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- mAbs (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Medical Research Council (grant NMRC-OFYIRG (MOH-000661))
- Topics
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- antibody-antigen recognition, hypervariable region
- MeSH terms
- Epitopes, Humans, Immunoglobulin Variable Region, Immunoglobulin Heavy Chains, Immunoglobulin Light Chains, Models, Molecular, Complementarity Determining Regions
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