ArticleiScience2022
Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of experimentally validated neo-epitopes: patterns, biases, and opportunities.Cancer immunology, immunotherapy : CII · 2025Pooled it
- Cancer epitope prediction tools and analysis pipelines in CEDAR.Nucleic acids research · 2026Article
- No evidence of immunosurveillance in mutation-hotspot-driven clonal hematopoiesis.Nature genetics · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- Vaccines for microbial eye diseases in the era of data science: opportunities and challenges.Frontiers in immunology · 2026Review
- Immunopeptidomics of cutaneous leishmaniasis patients reveals the natural antigenic landscape.Frontiers in immunology · 2026Article
- The Cancer Epitope Database and Analysis Resource (CEDAR): current capabilities and future directions.Frontiers in oncology · 2026Review
- Neoantigen-Driven Immunotherapy in Triple-Negative Breast Cancer: Emerging Strategies and Clinical Potential.Biomedicines · 2025Review
- Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation.Exploration of targeted anti-tumor therapy · 2025Review
- Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo.Nature communications · 2024Article
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
- A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes.NAR cancer · 2024Article
- Review
- VACCIMEL, an allogeneic melanoma vaccine, efficiently triggers T cell immune responses against neoantigens and alloantigens, as well as against tumor-associated antigens.Frontiers in immunology · 2024Article
- Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8Cell systems · 2023Article
- Estimating tissue-specific peptide abundance from public RNA-Seq data.Frontiers in genetics · 2023Article
- A Lean Reverse Vaccinology Pipeline with Publicly Available Bioinformatic Tools.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Peptide-based vaccine for cancer therapies.Frontiers in immunology · 2023Review
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Many steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions.
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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.