ArticleeLife2023
Structure-based prediction of T cell receptor:peptide-MHC interactions.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
110 citing papers in PubMed, 1 synthesis or guideline pooled it, 155 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Entering the Age of Specificity in T Cell Immunity.Immunological reviews · 2026Review
- Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.Cell systems · 2026Article
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- T Cell Thoughts.Immunological reviews · 2026Review
- Machine learning-based prediction of cross-immunity.Briefings in bioinformatics · 2026Article
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- Structure-based TCR-pMHC binding prediction and generalization to unseen peptides.npj drug discovery · 2026Article
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- What Does the Shape Space of T Cell Epitopes Look Like?Immunological reviews · 2026Review
- Structural Remodeling of TCR-HLA-DQ8 Recognition by a β-Cell Stress-Associated C19S Insulin Neoepitope in Type 1 Diabetes.International journal of molecular sciences · 2026Article
- Benchmarking sequence-based and AlphaFold-based methods for pMHC-II binding core prediction: distinct strengths and consensus approaches.BMC medical genomics · 2026Article
- TCR-FramePose: a local-frame representation for decomposing global docking and CDR3 loop geometry in TCR-pMHC recognition.bioRxiv : the preprint server for biology · 2026Article
- DTCR: generating realistic, diverse, and epitope-specific T cell receptor sequences via a discrete diffusion model.Briefings in bioinformatics · 2026Article
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.Journal for immunotherapy of cancer · 2026Review
- General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3.bioRxiv : the preprint server for biology · 2026Article
- TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity.PLoS computational biology · 2026Article
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
The regulatory and effector functions of T cells are initiated by the binding of their cell-surface T cell receptor (TCR) to peptides presented by major histocompatibility complex (MHC) proteins on other cells. The specificity of TCR:peptide-MHC interactions, thus, underlies nearly all adaptive immune responses. Despite intense interest, generalizable predictive models of TCR:peptide-MHC specificity remain out of reach; two key barriers are the diversity of TCR recognition modes and the paucity of training data. Inspired by recent breakthroughs in protein structure prediction achieved by deep neural networks, we evaluated structural modeling as a potential avenue for prediction of TCR epitope specificity. We show that a specialized version of the neural network predictor AlphaFold can generate models of TCR:peptide-MHC interactions that can be used to discriminate correct from incorrect peptide epitopes with substantial accuracy. Although much work remains to be done for these predictions to have widespread practical utility, we are optimistic that deep learning-based structural modeling represents a path to generalizable prediction of TCR:peptide-MHC interaction specificity.
Indexed as
Identifiers
What OpenQuestion holds
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.