ArticleScience advances2024
RACER-m leverages structural features for sparse T cell specificity prediction.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Ensembles ofbioRxiv : the preprint server for biology · 2026Article
- Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.Nature communications · 2026Article
- A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences.bioRxiv : the preprint server for biology · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- STCRpy: a software suite for T-cell receptor structure parsing, interaction profiling, and machine learning dataset preparation.Bioinformatics (Oxford, England) · 2025Article
- Article
- T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity.Communications biology · 2025Article
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
- Integration of kinetic data into affinity-based models for improved T cell specificity prediction.Biophysical journal · 2024Article
- Strengths and limitations of web servers for the modeling of TCRpMHC complexes.Computational and structural biotechnology journal · 2024Article
- Limits on inferring T cell specificity from partial information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.bioRxiv : the preprint server for biology · 2024Article
- Machine learning meets physics: A two-way street.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Feature selection enhances peptide binding predictions for TCR-specific interactions.Frontiers in immunology · 2024Article
- Quantitative approaches for decoding the specificity of the human T cell repertoire.Frontiers in immunology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
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.