ArticleFrontiers in immunology2019
Structure Based Prediction of Neoantigen Immunogenicity.
Article in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
What it found
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Who cites it
59 citing papers in PubMed.
- Toward next-generation therapeutic HPV vaccines: A review on expanding the antigen repertoire and an integrated bioinformatics selection framework.Human vaccines & immunotherapeutics · 2026Review
- Article
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- 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
- Identifying T cell antigen at the atomic level with graph convolutional network.Nature communications · 2025Article
- A frameshift-generated cancer neoepitope that controls tumor burden in prophylaxis as well as therapy.Journal of immunology (Baltimore, Md. : 1950) · 2025Article
- Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer.Frontiers in immunology · 2025Article
- AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond.Frontiers in immunology · 2025Article
- RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models.Journal of chemical information and modeling · 2024Article
- Modi-2 a vaccine stimulating CD4 responses to homocitrullinated self epitopes as therapy for solid cancers.NPJ vaccines · 2024Article
- IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction.Briefings in bioinformatics · 2024Article
- NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.Bioinformatics (Oxford, England) · 2024Article
- APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries.Journal of chemical information and modeling · 2024Article
- Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines.The Journal of clinical investigation · 2024Review
- Accurate modeling of peptide-MHC structures with AlphaFold.Structure (London, England : 1993) · 2024Article
- Review
- Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8
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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.