ArticleNucleic acids research2019
SEPPA 3.0-enhanced spatial epitope prediction enabling glycoprotein antigens.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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75 citing papers in PubMed, 141 citations in OpenAlex.
- PatchEpi: patch-aware equivariant learning improves structure-based epitope prediction.Bioinformatics (Oxford, England) · 2026Article
- Pocket restraints guided by B-cell epitope prediction improve Chai-1 antibody-antigen structure modeling.Protein science : a publication of the Protein Society · 2026Article
- Lineage Diversification and Evolutionary Dynamics of theMicroorganisms · 2026Article
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- Integrative Computational Prediction Strategy for Antibody-Antigen Binding: A Case Study on Interleukin-1 Beta.Journal of chemical information and modeling · 2026Article
- Recombinant Immunogens Designed by AI Epitope Prioritization Confer Protection AgainstVaccines · 2026Article
- MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C.PLoS computational biology · 2026Article
- Phylogenetic Analyses ofMicroorganisms · 2026Article
- Molecular Evolution of theMicroorganisms · 2026Article
- Consensus-based computational mapping of NS1 antigenic determinants in dengue and Zika viruses to improve diagnostic specificity.BMC infectious diseases · 2026Article
- B-cell epitope prediction in the age of machine learning: advancements and challenges.Journal of translational medicine · 2026Review
- RoBep: a region-oriented deep learning model for B-cell epitope prediction.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Article
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- Improving B-cell epitope prediction.Drug discovery today · 2025Review
- Structure-based Predictions of Conformational B Cell Epitopes by Protein Language Model and Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications.ACS omega · 2025Review
- Understanding the landscape of cross-species transmission, epidemiology, phylogenetics, and antigenicity of HPAI strain A(H5N1) causing a recent outbreak in the USA.Journal, genetic engineering & biotechnology · 2025Article
- AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.Science advances · 2025Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
B-cell epitope information is critical to immune therapy and vaccine design. Protein epitopes can be significantly affected by glycosylation, while no methods have considered this till now. Based on previous versions of Spatial Epitope Prediction of Protein Antigens (SEPPA), we here present an enhanced tool SEPPA 3.0, enabling glycoprotein antigens. Parameters were updated based on the latest and largest dataset. Then, additional micro-environmental features of glycosylation triangles and glycosylation-related amino acid indexes were added as important classifiers, coupled with final calibration based on neighboring antigenicity. Logistic regression model was retained as SEPPA 2.0. The AUC value of 0.794 was obtained through 10-fold cross-validation on internal validation. Independent testing on general protein antigens resulted in AUC of 0.740 with BA (balanced accuracy) of 0.657 as baseline of SEPPA 3.0. Most importantly, when tested on independent glycoprotein antigens only, SEPPA 3.0 gave an AUC of 0.749 and BA of 0.665, leading the top performance among peers. As the first server enabling accurate epitope prediction for glycoproteins, SEPPA 3.0 shows significant advantages over popular peers on both general protein and glycoprotein antigens. It can be accessed at http://bidd2.nus.edu.sg/SEPPA3/ or at http://www.badd-cao.net/seppa3/index.html. Batch query is supported.
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