Can we predict T cell specificity with digital biology and machine learning?

Nguyen, A. T., Szeto, C. & Gras, S. The pockets guide to HLA class I molecules. Biochem. Soc. Trans. 49, 2319–2331 (2021).

CAS  Google Scholar 

de Jong, A. & Ogg, G. CD1a function in human skin disease. Mol. Immunol. 130, 14–19 (2021).

Google Scholar 

de Libero, G., Chancellor, A. & Mori, L. Antigen specificities and functional properties of MR1-restricted T cells. Mol. Immunol. 130, 148–153 (2021).

Google Scholar 

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).

CAS  Google Scholar 

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).

Google Scholar 

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).

CAS  Google Scholar 

Vita, R. et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 47, D339–D343 (2019).

CAS  Google Scholar 

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).

Google Scholar 

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).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Weber, A., Born, J. & Rodriguez Martínez, M. TITAN: T cell receptor specificity prediction with bimodal attention networks. Bioinformatics 37, I237–I244 (2021).

CAS  Google Scholar 

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).

Google Scholar 

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).

Google Scholar 

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).

Google Scholar 

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).

CAS  Google Scholar 

Altman, J. D. et al. Phenotypic analysis of antigen-specific T lymphocytes. Science 274, 94–96 (1996).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Joglekar, A. V. & Li, G. T cell antigen discovery. Nat. Methods 18, 873–880 (2021).

CAS  Google Scholar 

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).

Google Scholar 

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).

CAS  Google Scholar 

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).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Gascoigne, N. et al. Optimized peptide-MHC multimer protocols for detection and isolation of autoimmune T-cells. Front. Immunol. 9, 1378 (2018).

Google Scholar 

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).

Article  Google Scholar 

Dobson, C. S. et al. Antigen identification and high-throughput interaction mapping by reprogramming viral entry. Nat. Methods 19, 449–460 (2022).

CAS  Google Scholar 

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).

Google Scholar 

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).

CAS  Google Scholar 

Birnbaum, M. E. et al. Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).

CAS  Google Scholar 

Crawford, F. et al. Use of baculovirus MHC/peptide display libraries to characterize T-cell receptor ligands. Immunol. Rev. 210, 156–170 (2006).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Kula, T. et al. T-Scan: a genome-wide method for the systematic discovery of T cell epitopes. Cell 178, 1016 (2019).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Li, G. et al. T cell antigen discovery via trogocytosis. Nat. Methods 16, 183–190 (2019).

CAS  Google Scholar 

Joglekar, A. V. et al. T cell antigen discovery via signaling and antigen-presenting bifunctional receptors. Nat. Methods 16, 191–198 (2019).

CAS  Google Scholar 

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).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Lee, C. H. et al. Predicting cross-reactivity and antigen specificity of T cell receptors. Front. Immunol. 11, 2498 (2020).

Google Scholar 

Vujovic, M. et al. T cell receptor sequence clustering and antigen specificity. Comput. Struct. Biotechnol. J. 18, 2166–2173 (2020).

CAS  Google Scholar 

Katayama, Y., Yokota, R., Akiyama, T. & Kobayashi, T. J. Machine learning approaches to TCR repertoire analysis. Front. Immunol. 13, 858057 (2022).

CAS  Google Scholar 

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).

CAS  Google Scholar 

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).

CAS  Google Scholar 

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).

Article  Google Scholar 

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).

Google Scholar 

Lu, T. et al. Deep learning-based prediction of the T cell receptor–antigen binding specificity. Nat. Mach. Intell. 3, 864–875 (2021).

Google Scholar 

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).

Google Scholar 

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).

Article  Google Scholar 

Grazioli, F. et al. On TCR binding predictors failing to generalize to unseen peptides. Front. Immunol. 13, 1014256 (2022).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Chronister, W. D. et al. TCRMatch: predicting T-cell receptor specificity based on sequence similarity to previously characterized receptors. Front. Immunol. 12, 640725 (2021).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).

CAS  Google Scholar 

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).

CAS  Google Scholar 

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).

CAS  Google Scholar 

Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).

CAS  Google Scholar 

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).

CAS  Google Scholar 

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).

Article  Google Scholar 

Singh, N. K. et al. Emerging concepts in TCR specificity: rationalizing and (maybe) predicting outcomes. J. Immunol. 199, 2203–2213 (2017).

留言 (0)

沒有登入
gif