ArticlePloS one2012
BEST: improved prediction of B-cell epitopes from antigen sequences.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications.ACS omega · 2025Review
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- Multi-perspectives and challenges in identifying B-cell epitopes.Protein science : a publication of the Protein Society · 2023Review
- B-Cell Epitope Predictions Using Computational Methods.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets.Pathogens (Basel, Switzerland) · 2022Review
- An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics.International journal of environmental research and public health · 2021Review
- Discovering the Ultimate Limits of Protein Secondary Structure Prediction.Biomolecules · 2021Article
- Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface.International journal of molecular sciences · 2021Review
- Advanced strategies for development of vaccines against human bacterial pathogens.World journal of microbiology & biotechnology · 2021Review
- Peptides-based vaccine against SARS-Bio-design and manufacturing · 2021Article
- The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.PloS one · 2021Article
- iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features.Genomics, proteomics & bioinformatics · 2020Article
- Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach.IET systems biology · 2020Article
- A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy.PloS one · 2020Article
- Deep learning methods improve linear B-cell epitope prediction.BioData mining · 2020Article
- Postgenomic Approaches and Bioinformatics Tools to Advance the Development of Vaccines against Bacteria of theVaccines · 2018Review
- DRREP: deep ridge regressed epitope predictor.BMC genomics · 2017Article
- DNA Vaccines Encoding Toxoplasma gondii Cathepsin C 1 Induce Protection against Toxoplasmosis in Mice.The Korean journal of parasitology · 2017Article
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5 authors.
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Abstract
Accurate identification of immunogenic regions in a given antigen chain is a difficult and actively pursued problem. Although accurate predictors for T-cell epitopes are already in place, the prediction of the B-cell epitopes requires further research. We overview the available approaches for the prediction of B-cell epitopes and propose a novel and accurate sequence-based solution. Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new architecture based on averaging selected scores generated from sliding 20-mers by a Support Vector Machine (SVM). The SVM predictor utilizes a comprehensive and custom designed set of inputs generated by combining information derived from the chain, sequence conservation, similarity to known (training) epitopes, and predicted secondary structure and relative solvent accessibility. Empirical evaluation on benchmark datasets demonstrates that BEST outperforms several modern sequence-based B-cell epitope predictors including ABCPred, method by Chen et al. (2007), BCPred, COBEpro, BayesB, and CBTOPE, when considering the predictions from antigen chains and from the chain fragments. Our method obtains a cross-validated area under the receiver operating characteristic curve (AUC) for the fragment-based prediction at 0.81 and 0.85, depending on the dataset. The AUCs of BEST on the benchmark sets of full antigen chains equal 0.57 and 0.6, which is significantly and slightly better than the next best method we tested. We also present case studies to contrast the propensity profiles generated by BEST and several other methods.
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