ArticleJournal of immunological methods2016
CD4+ T-cell epitope prediction using antigen processing constraints.
Article in Journal of immunological methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Structural Basis for CD4+ T Cell Epitope Dominance in Arbo-Flavivirus Envelope Proteins: A Meta-Analysis.Viral immunology · 2017Pooled it
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- Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding.ArXiv · 2026Article
- Conformational bias in SARS-CoV-2 Spike CD4+ T-cell epitope dominance.Frontiers in immunology · 2026Article
- Cut or bind? Antigen-specific processing mechanisms define CD4Genome medicine · 2025Article
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- Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies.Vaccines · 2024Article
- XRCC1: a potential prognostic and immunological biomarker in LGG based on systematic pan-cancer analysis.Aging · 2024Article
- A cell-free antigen processing system informs HIV-1 epitope selection and vaccine design.The Journal of experimental medicine · 2023Article
- Diversity and evolution of computationally predicted T cell epitopes against human respiratory syncytial virus.PLoS computational biology · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Germline IgM predicts T cell immunity to Pneumocystis.JCI insight · 2022Article
- CD4+ T-Cell Epitope Prediction by Combined Analysis of Antigen Conformational Flexibility and Peptide-MHCII Binding Affinity.Biochemistry · 2022Article
- The Serpin-like Loop Insertion of Ovalbumin Increases the Stability and Decreases the OVA 323-339 Epitope Processing Efficiency.Biochemistry · 2021Article
- MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform.Journal of chemical information and modeling · 2021Article
- Deciphering and predicting CD4+ T cell immunodominance of influenza virus hemagglutinin.The Journal of experimental medicine · 2020Article
- PIRCHE-II: an algorithm to predict indirectly recognizable HLA epitopes in solid organ transplantation.Immunogenetics · 2020Review
- CD4 T Cell Determinants in West Nile Virus Disease and Asymptomatic Infection.Frontiers in immunology · 2020Article
- Peptide Epitope Hot Spots of CD4 T Cell Recognition Within Influenza Hemagglutinin During the Primary Response to Infection.Pathogens (Basel, Switzerland) · 2019Article
- Deimmunizing substitutions inThe Journal of biological chemistry · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
T-cell CD4+ epitopes are important targets of immunity against infectious diseases and cancer. State-of-the-art methods for MHC class II epitope prediction rely on supervised learning methods in which an implicit or explicit model of sequence specificity is constructed using a training set of peptides with experimentally tested MHC class II binding affinity. In this paper we present a novel method for CD4+ T-cell eptitope prediction based on modeling antigen-processing constraints. Previous work indicates that dominant CD4+ T-cell epitopes tend to occur adjacent to sites of initial proteolytic cleavage. Given an antigen with known three-dimensional structure, our algorithm first aggregates four types of conformational stability data in order to construct a profile of stability that allows us to identify regions of the protein that are most accessible to proteolysis. Using this profile, we then construct a profile of epitope likelihood based on the pattern of transitions from unstable to stable regions. We validate our method using 35 datasets of experimentally measured CD4+ T cell responses of mice bearing I-Ab or HLA-DR4 alleles as well as of human subjects. Overall, our results show that antigen processing constraints provide a significant source of predictive power. For epitope prediction in single-allele systems, our approach can be combined with sequence-based methods, or used in instances where little or no training data is available. In multiple-allele systems, sequence-based methods can only be used if the allele distribution of a population is known. In contrast, our approach does not make use of MHC binding prediction, and is thus agnostic to MHC class II genotypes.
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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.