Nguyen, A. T., Szeto, C. & Gras, S. The pockets guide to HLA class I molecules. Biochem. Soc. Trans. 49, 2319–2331 (2021).
de Jong, A. & Ogg, G. CD1a function in human skin disease. Mol. Immunol. 130, 14–19 (2021).
de Libero, G., Chancellor, A. & Mori, L. Antigen specificities and functional properties of MR1-restricted T cells. Mol. Immunol. 130, 148–153 (2021).
Sun, L., Middleton, D. R., Wantuch, P. L., Ozdilek, A. & Avci, F. Y. Carbohydrates as T-cell antigens with implications in health and disease. Glycobiology 26, 1029–1040 (2016).
Bagaev, D. V. et al. VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. Nucleic Acids Res. 48, D1057–D1062 (2020).
Tickotsky, N., Sagiv, T., Prilusky, J., Shifrut, E. & Friedman, N. McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics 33, 2924–2929 (2017).
Vita, R. et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 47, D339–D343 (2019).
Nolan, S. et al. A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2. Preprint at Res. Sq. https://www.researchsquare.com/article/rs-51964/v1 (2020).
Moris, P. et al. Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification. Brief. Bioinform. 22, bbaa318 (2021).
Huang, H., Wang, C., Rubelt, F., Scriba, T. J. & Davis, M. M. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat. Biotechnol. 38, 1194–1202 (2020).
Mayer-Blackwell, K. et al. TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs. eLife 10, e68605 (2021).
Weber, A., Born, J. & Rodriguez Martínez, M. TITAN: T cell receptor specificity prediction with bimodal attention networks. Bioinformatics 37, I237–I244 (2021).
Lee, C. H., Antanaviciute, A., Buckley, P. R., Simmons, A. & Koohy, H. To what extent does MHC binding translate to immunogenicity in humans? Immunoinformatics 3–4, 100006 (2021).
Buckley, P. R. et al. Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens. Brief. Bioinform. 23, bbac141 (2022).
Mösch, A., Raffegerst, S., Weis, M., Schendel, D. J. & Frishman, D. Machine learning for cancer immunotherapies based on epitope recognition by T cell receptors. Front. Genet. 10, 1141 (2019).
Wells, D. K. et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 183, 818–834.e13 (2020).
Altman, J. D. et al. Phenotypic analysis of antigen-specific T lymphocytes. Science 274, 94–96 (1996).
Yao, Y., Wyrozżemski, Ł., Lundin, K. E. A., Kjetil Sandve, G. & Qiao, S.-W. Differential expression profile of gluten-specific T cells identified by single-cell RNA-seq. PLoS ONE 16, e0258029 (2021).
Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).
Kurtulus, S. & Hildeman, D. Assessment of CD4+ and CD8+ T cell responses using MHC class I and II tetramers. Methods Mol. Biol. 979, 71–79 (2013).
Joglekar, A. V. & Li, G. T cell antigen discovery. Nat. Methods 18, 873–880 (2021).
Bosselut, R. et al. Single T cell sequencing demonstrates the functional role of αβ TCR pairing in cell lineage and antigen specificity. Front. Immunol. 1, 1516 (2019).
Emerson, R. O. et al. Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nat. Genet. 49, 659–665 (2017).
Wang, X., He, Y., Zhang, Q., Ren, X. & Zhang, Z. Direct comparative analyses of 10× genomics chromium and Smart-Seq2. Genomics Proteomics Bioinformatics 19, 253–266 (2021).
Zhang, W. et al. A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity. Sci. Adv. 7, eabf5835 (2021).
Gascoigne, N. et al. Optimized peptide-MHC multimer protocols for detection and isolation of autoimmune T-cells. Front. Immunol. 9, 1378 (2018).
Meysman, P. et al. Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report. Preprint at bioRxiv https://doi.org/10.1101/2022.10.27.514020 (2022).
Dobson, C. S. et al. Antigen identification and high-throughput interaction mapping by reprogramming viral entry. Nat. Methods 19, 449–460 (2022).
Guo, X. Z. J. & Elledge, S. J. V-CARMA: a tool for the detection and modification of antigen-specific T cells. Proc. Natl Acad. Sci. USA 119, e2116277119 (2022).
Brophy, S. E., Holler, P. D. & Kranz, D. M. A yeast display system for engineering functional peptide-MHC complexes. J. Immunol. Methods 272, 235–246 (2003).
Birnbaum, M. E. et al. Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).
Crawford, F. et al. Use of baculovirus MHC/peptide display libraries to characterize T-cell receptor ligands. Immunol. Rev. 210, 156–170 (2006).
Coles, C. H. et al. TCRs with distinct specificity profiles use different binding modes to engage an identical peptide–HLA complex. J. Immunol. 204, 1943–1953 (2020).
Kula, T. et al. T-Scan: a genome-wide method for the systematic discovery of T cell epitopes. Cell 178, 1016 (2019).
Pan, X. et al. Combinatorial HLA-peptide bead libraries for high throughput identification of CD8+ T cell specificity. J. Immunol. Methods 403, 72–78 (2014).
Li, G. et al. T cell antigen discovery via trogocytosis. Nat. Methods 16, 183–190 (2019).
Joglekar, A. V. et al. T cell antigen discovery via signaling and antigen-presenting bifunctional receptors. Nat. Methods 16, 191–198 (2019).
Schaap-Johansen, A.-L., Vujovic, M., Borch, A., Hadrup, S. R. & Marcatili, P. T cell epitope prediction and its application to immunotherapy. Front. Immunol. 12, 712488 (2021).
Valkiers, S. et al. Recent advances in T-cell receptor repertoire analysis: bridging the gap with multimodal single-cell RNA sequencing. Immunoinformatics 5, 100009 (2022).
Lee, C. H. et al. Predicting cross-reactivity and antigen specificity of T cell receptors. Front. Immunol. 11, 2498 (2020).
Vujovic, M. et al. T cell receptor sequence clustering and antigen specificity. Comput. Struct. Biotechnol. J. 18, 2166–2173 (2020).
Katayama, Y., Yokota, R., Akiyama, T. & Kobayashi, T. J. Machine learning approaches to TCR repertoire analysis. Front. Immunol. 13, 858057 (2022).
Luu, A. M., Leistico, J. R., Miller, T., Kim, S. & Song, J. S. Predicting TCR-epitope binding specificity using deep metric learning and multimodal learning. Genes 12, 572 (2021).
Montemurro, A. et al. NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data. Commun. Biol. 4, 1060 (2021).
Dens, C., Bittremieux, W., Affaticati, F., Laukens, K. & Meysman, P. Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions. Preprint at bioRxiv https://doi.org/10.1101/2022.05.02.490264 (2022).
Springer, I., Tickotsky, N. & Louzoun, Y. Contribution of T cell receptor alpha and beta CDR3, MHC typing, V and J genes to peptide binding prediction. Front. Immunol. 12, 1436 (2021).
Lu, T. et al. Deep learning-based prediction of the T cell receptor–antigen binding specificity. Nat. Mach. Intell. 3, 864–875 (2021).
Fischer, D. S., Wu, Y., Schubert, B. & Theis, F. J. Predicting antigen specificity of single T cells based on TCR CDR3 regions. Mol. Syst. Biol. 16, 9416 (2020).
Wu, K. et al. TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-binding analyses. Preprint at bioRxiv https://doi.org/10.1101/2021.11.18.469186 (2021).
Grazioli, F. et al. On TCR binding predictors failing to generalize to unseen peptides. Front. Immunol. 13, 1014256 (2022).
Zhang, H., Zhan, X. & Li, B. GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation. Nat. Commun. 12, 4699 (2021).
Chronister, W. D. et al. TCRMatch: predicting T-cell receptor specificity based on sequence similarity to previously characterized receptors. Front. Immunol. 12, 640725 (2021).
Sidhom, J. W., Larman, H. B., Pardoll, D. M. & Baras, A. S. DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires. Nat. Commun. 12, 1605 (2021).
Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).
Valkiers, S., van Houcke, M., Laukens, K. & Meysman, P. ClusTCR: a python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity. Bioinformatics 37, 4865–4867 (2021).
Corrie, B. D. et al. iReceptor: a platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories. Immunol. Rev. 284, 24–41 (2018).
Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).
Leem, J., de Oliveira, S. H. P., Krawczyk, K. & Deane, C. M. STCRDab: the structural T-cell receptor database. Nucleic Acids Res. 46, D406–D412 (2018).
Mayer, A. & Callan Jr, C. G. Measures of epitope binding degeneracy from T cell receptor repertoires. Preprint at bioRxiv https://doi.org/10.1101/2022.07.25.501373 (2022).
Singh, N. K. et al. Emerging concepts in TCR specificity: rationalizing and (maybe) predicting outcomes. J. Immunol. 199, 2203–2213 (2017).
留言 (0)