A resource for researchers studying TCR antigen specificity. Welcome to make your contributions to this repo!
VDJdb is a curated database of T-cell receptor (TCR) sequences with known antigen specificities. The primary goal of VDJdb is to facilitate access to existing information on T-cell receptor antigen specificities, i.e. the ability to recognize certain epitopes in a certain MHC contexts.
The Immune Epitope Database (IEDB) is a freely available resource funded by NIAID. It catalogs experimental data on antibody and T cell epitopes studied in humans, non-human primates, and other animal species in the context of infectious disease, allergy, autoimmunity and transplantation. The IEDB also hosts tools to assist in the prediction and analysis of epitopes.
McPAS-TCR is a manually curated catalogue of T cell receptor (TCR) sequences that were found in T cells associated with various pathological conditions in humans and in mice. It is meant to link TCR sequences to their antigen target or to the pathology and organ with which they are associated. The database can be queried by disease condition, T cell type, tissue, epitope, source organism, MHC restriction, assay type and other criteria.
- Can we predict T cell specificity with digital biology and machine learning? [Nature Reviews Immunology, 2023] (Hashem Koohy*, University of Oxford)
- T Cell Epitope Prediction and Its Application to Immunotherapy [Frontiers in Immunology, 2021] (Paolo Marcatili*, Technical University of Denmark)
- Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors [Frontiers in Immunology, 2020] (Hashem Koohy*, University of Oxford)
- Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report [ImmunoInformatics, 2023] (Virag Sharma*, University of Limerick)
- PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder [Pacific Symposium on Biocomputing, 2023] (Heewook Lee*, Arizona State University)
- TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs [Bioinformatics, 2023] (Harri Lahdesmaki*, Aalto University)
- HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery [Immunity, 2023] (Ramnik Xavier*, Daniel B Graham*, Broad Institute of MIT and Harvard)
- A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design [Nature Machine Intelligence, 2022] (Dong-Qing Wei*, Shanghai Jiao Tong University)
- A large peptidome dataset improves HLA class I epitope prediction across most of the human population [Nature Biotechnology, 2020] (Derin B. Keskin*, Broad Institute of MIT and Harvard)
- TEINet: a deep learning framework for prediction of TCR–epitope binding specificity [Briefings in Bioinformatics, 2023] (Shuai Cheng Li*, City University of Hong Kong)
- Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning [nature machine intelligence, 2023] (Jianyang Zeng*, Tsinghua University)
- Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition [Nature Machine Intelligence, 2023] (Qi Liu*, Tongji University)
- ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model [Frontiers in Immunology, 2022] (Heewook Lee*, Arizona State University)
- Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy [Science Advance, 2022] (Tao Wang*, University of Texas Southwestern Medical Center)
- TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs [Bioinformatics, 2022] (Emmi Jokinen*, Aalto University)
- DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires [Nature Communications, 2021] (John-William Sidhom*, Johns Hopkins University School of Medicine)
- Deep learning-based prediction of the T cell receptor–antigen binding specificity [Nature Machine Intelligence, 2021] (Tao Wang*, University of Texas Southwestern Medical Center)
- TITAN: T-cell receptor specificity prediction with bimodal attention networks [ISMB, 2021] (Maria Rodriguez Martinez*, ETH Zurich)
- NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data [Communications Biology, 2021] (Morten Nielsen*, Technical University of Denmark)
- Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification [Briefings in Bioinformatics, 2021] (Pieter Meysman*, University of Antwerp)
- Predicting recognition between T cell receptors and epitopes with TCRGP [PLOS Computaional Biology, 2021]