ArticleNucleic acids research2023
SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A computational pipeline to discover potential cross-reactive antibodies: a case study on coronavirus.Frontiers in cellular and infection microbiology · 2026Article
- Article
- Improving B-cell epitope prediction.Drug discovery today · 2025Review
- AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.Science advances · 2025Article
- SITA: Predicting site-specific immunogenicity for therapeutic antibodies.Journal of pharmaceutical analysis · 2025Article
- A comprehensive antigen-antibody complex database unlocking insights into interaction interface.eLife · 2025Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- B cell epitope prediction by capturing spatial clustering property of the epitopes using graph attention network.Scientific reports · 2024Article
- EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information.NPJ systems biology and applications · 2024Article
- Potent and broadly neutralizing antibodies against sarbecoviruses induced by sequential COVID-19 vaccination.Cell discovery · 2024Article
- Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.Briefings in bioinformatics · 2024Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.
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