ArticleImmunogenetics2010
NetCTLpan: pan-specific MHC class I pathway epitope predictions.
Article in Immunogenetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 214 papers, 2 of them syntheses that pooled it.
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Who cites it
214 citing papers in PubMed, 2 syntheses or guidelines pooled it, 394 citations in OpenAlex.
- Landscape of epitopes targeted by T cells in 852 individuals recovered from COVID-19: Meta-analysis, immunoprevalence, and web platform.Cell reports. Medicine · 2021Pooled it
- Varicella-zoster virus-derived major histocompatibility complex class I-restricted peptide affinity is a determining factor in the HLA risk profile for the development of postherpetic neuralgia.Journal of virology · 2015Pooled it
- Toward next-generation therapeutic HPV vaccines: A review on expanding the antigen repertoire and an integrated bioinformatics selection framework.Human vaccines & immunotherapeutics · 2026Review
- Past achievements and future perspectives of personalized vaccines and the role of dendritic cells.Immunologic research · 2026Review
- Article
- Cancer epitope prediction tools and analysis pipelines in CEDAR.Nucleic acids research · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- Review
- Design of a multiepitope immunotherapy for fish allergy.Scientific reports · 2026Article
- AntiPan: a genome-informed in silico pipeline for advancing subunit vaccine discovery against Staphylococcus aureus.Scientific reports · 2026Article
- Vaccine Development T-cell (MHC-I) Epitopes Identification Against the Indian HCV Genotype: An Approach Based on Immunoinformatic.Molecular biotechnology · 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
- CNNeoPP: a large language model-enhanced deep learning pipeline for personalized neoantigen prediction and liquid biopsy applications.Frontiers in immunology · 2026Article
- HLA class I-specific nucleolin peptides induce therapeutic T cells in triple-negative breast cancer patients.International journal of biological sciences · 2026Article
- Computational Methods for Cancer Neoantigen Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Artificial intelligence in vaccine development: applications, implementation, and future directions.Frontiers in cellular and infection microbiology · 2026Review
- Bioinformatics-driven design and protective efficacy assessment of a multi-epitope vaccine forFrontiers in immunology · 2026Article
- Profiling immunogenic neoantigen peptides elicited by personalized neoantigen vaccine in cancer patients.Frontiers in immunology · 2026Article
- C/EBPβ-induced alternative splicing of RCAN1 generates a potent TCR-T target in mesenchymal glioblastoma.Cellular & molecular immunology · 2026Article
154 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/.
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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.