ArticleFrontiers in immunology2022
SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed, 74 citations in OpenAlex.
- Article
- Surf2Spot: a surface-informed geometry-aware model for predicting partner-independent binder and nanobody design hotspots.Briefings in bioinformatics · 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
- Design of a novel proteome-derived scaffold presenting epitopes of Pseudomonas aeruginosa in native conformation.Scientific reports · 2026Article
- Phylogenetic Analyses ofMicroorganisms · 2026Article
- Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid arthritis treatment stratification.Nature biomedical engineering · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Molecular Evolution of theMicroorganisms · 2026Article
- RoBep: a region-oriented deep learning model for B-cell epitope prediction.Bioinformatics (Oxford, England) · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Immunoinformatics-driven design of a conserved RNA-dependent RNA polymerase-based multi-epitope vaccine against avian infectious bronchitis virus.Veterinary world · 2026Article
- A computational pipeline to discover potential cross-reactive antibodies: a case study on coronavirus.Frontiers in cellular and infection microbiology · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- Article
- Mutation profiling, evolution analysis, molecular dynamics simulation, and functional characterization of Omicron sub-strains.Virus research · 2025Article
- Antibody-Drug Conjugates in Breast Cancer: Navigating Innovations, Overcoming Resistance, and Shaping Future Therapies.Biomedicines · 2025Review
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.