ArticleScientific reports2018
Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Computational immunology in venom research: a systematic review of epitope prediction and validation approaches.Briefings in bioinformatics · 2025Pooled it
- Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus.Biomedical journal · 2021Pooled it
- Immunoinformatics-driven multi-epitope vaccine design as a promising strategy against multidrug-resistant pathogens: a comprehensive review.Folia microbiologica · 2026Review
- Comparison of a recombinant endonuclease III protein and its synthetic peptides in ELISA for the diagnosis of tegumentary leishmaniasis using human serum and urine samples: A preliminary study.PLoS neglected tropical diseases · 2026Article
- PEPNet: a two-stage point cloud framework with hierarchical embedding and antigen-antibody interaction modeling for epitope prediction.Briefings in bioinformatics · 2026Article
- BLMPred: Predicting linear B-cell epitopes using pre-trained protein language models and machine learning.Computational and structural biotechnology journal · 2026Article
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- HAIRpred: Prediction of human antibody interacting residues in an antigen from its primary structure.Protein science : a publication of the Protein Society · 2025Article
- New mouse model based on adenocarcinoma 4T1 cells expressing HPV16 E6 and E7 applied to assess the efficacy of therapeutic and prophylactic E6/E7-based HPV16 vaccines.Infectious agents and cancer · 2025Article
- AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.Science advances · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.Briefings in bioinformatics · 2024Article
- Computer-Aided Multi-Epitope Based Vaccine Design Against Monkeypox Virus Surface Protein A30L: An Immunoinformatics Approach.The protein journal · 2023Article
- Multi-perspectives and challenges in identifying B-cell epitopes.Protein science : a publication of the Protein Society · 2023Review
- The ClusPro AbEMap web server for the prediction of antibody epitopes.Nature protocols · 2023Review
- BepiPred-3.0: Improved B-cell epitope prediction using protein language models.Protein science : a publication of the Protein Society · 2022Article
- Bioinformatics, Computational Informatics, and Modeling Approaches to the Design of mRNA COVID-19 Vaccine Candidates.Computation (Basel, Switzerland) · 2022Article
- Mapping immunogenic epitopes of an adhesin-like protein from Methanobrevibacter ruminantium M1 and comparison of empirical data with in silico prediction methods.Scientific reports · 2022Article
- EpitopeVec: linear epitope prediction using deep protein sequence embeddings.Bioinformatics (Oxford, England) · 2021Article
- Designing a multi-epitope vaccine against Mycobacteroides abscessus by pangenome-reverse vaccinology.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epitope identification is essential for developing effective antibodies that can detect and neutralize bioactive proteins. Computational prediction is a valuable and time-saving alternative for experimental identification. Current computational methods for epitope prediction are underused and undervalued due to their high false positive rate. In this work, we targeted common properties of linear B-cell epitopes identified in an individual protein class (metalloendopeptidases) and introduced an alternative method to reduce the false positive rate and increase accuracy, proposing to restrict predictive models to a single specific protein class. For this purpose, curated epitope sequences from metalloendopeptidases were transformed into frame-shifted Kmers (3 to 15 amino acid residues long). These Kmers were decomposed into a matrix of biochemical attributes and used to train a decision tree classifier. The resulting prediction model showed a lower false positive rate and greater area under the curve when compared to state-of-the-art methods. Our predictions were used for synthesizing peptides mimicking the predicted epitopes for immunization of mice. A predicted linear epitope that was previously undetected by an experimental immunoassay was able to induce neutralizing-antibody production in mice. Therefore, we present an improved prediction alternative and show that computationally identified epitopes can go undetected during experimental mapping.
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