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Hudson D, Fernandes RA, Basham M, Ogg G, Koohy H. Can we predict T cell specificity with digital biology and machine learning? Nat Rev Immunol 2023; 23:511-521. [PMID: 36755161 PMCID: PMC9908307 DOI: 10.1038/s41577-023-00835-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 02/10/2023]
Abstract
Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.
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Affiliation(s)
- Dan Hudson
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- The Rosalind Franklin Institute, Didcot, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Graham Ogg
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
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Jiang N, Malone M, Chizari S. Antigen-specific and cross-reactive T cells in protection and disease. Immunol Rev 2023; 316:120-135. [PMID: 37209375 PMCID: PMC10524458 DOI: 10.1111/imr.13217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/22/2023]
Abstract
Human T cells have a diverse T-cell receptor (TCR) repertoire that endows them with the ability to identify and defend against a broad spectrum of antigens. The universe of possible antigens that T cells may encounter, however, is even larger. To effectively surveil such a vast universe, the T-cell repertoire must adopt a high degree of cross-reactivity. Likewise, antigen-specific and cross-reactive T-cell responses play pivotal roles in both protective and pathological immune responses in numerous diseases. In this review, we explore the implications of these antigen-driven T-cell responses, with a particular focus on CD8+ T cells, using infection, neurodegeneration, and cancer as examples. We also summarize recent technological advances that facilitate high-throughput profiling of antigen-specific and cross-reactive T-cell responses experimentally, as well as computational biology approaches that predict these interactions.
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Affiliation(s)
- Ning Jiang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
- Institute for Immunology, University of Pennsylvania, Philadelphia, PA, 19104
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA, 19104
- Institute for RNA Innovation, University of Pennsylvania, Philadelphia, PA, 19104
| | - Michael Malone
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
| | - Shahab Chizari
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
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Wu J, Qi M, Zhang F, Zheng Y. TPBTE: A model based on convolutional Transformer for predicting the binding of TCR to epitope. Mol Immunol 2023; 157:30-41. [PMID: 36966551 DOI: 10.1016/j.molimm.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/29/2023]
Abstract
T cell receptors (TCRs) selectively bind to antigens to fight pathogens with specific immunity. Current tools focus on the nature of amino acids within sequences and take less into account the nature of amino acids far apart and the relationship between sequences, leading to significant differences in the results from different datasets. We propose TPBTE, a model based on convolutional Transformer for Predicting the Binding of TCR to Epitope. It takes epitope sequences and the complementary decision region 3 (CDR3) sequences of TCRβ chain as inputs. And it uses a convolutional attention mechanism to learn amino acid representations between different positions of the sequences based on learning local features of the sequences. At the same time, it uses cross attention to learn the interaction information between TCR sequences and epitope sequences. A comprehensive evaluation of the TCR-epitope data shows that the average area under the curve of TPBTE outperforms the baseline model, and demonstrate an intentional performance. In addition, TPBTE can give the probability of binding TCR to epitopes, which can be used as the first step of epitope screening, narrowing the scope of epitope search and reducing the time of epitope search.
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T. RR, Smith JC. Structural patterns in class 1 major histocompatibility complex‐restricted nonamer peptide binding to T‐cell receptors. Proteins 2022; 90:1645-1654. [DOI: 10.1002/prot.26343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/12/2022] [Accepted: 03/27/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Rajitha Rajeshwar T.
- Department of Biochemistry and Cellular and Molecular Biology University of Tennessee Knoxville Tennessee USA
- UT/ORNL Center for Molecular Biophysics Oak Ridge National Laboratory Oak Ridge Tennessee USA
| | - Jeremy C. Smith
- Department of Biochemistry and Cellular and Molecular Biology University of Tennessee Knoxville Tennessee USA
- UT/ORNL Center for Molecular Biophysics Oak Ridge National Laboratory Oak Ridge Tennessee USA
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Bhattacharya M, Sharma AR, Mallick B, Lee SS, Seo EM, Chakraborty C. B.1.1.7 (Alpha) variant is the most antigenic compared to Wuhan strain, B.1.351, B.1.1.28/triple mutant and B.1.429 variants. Front Microbiol 2022; 13:895695. [PMID: 36033846 PMCID: PMC9411949 DOI: 10.3389/fmicb.2022.895695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
The rapid spread of the SARS-CoV-2 virus and its variants has created a catastrophic impact worldwide. Several variants have emerged, including B.1.351 (Beta), B.1.1.28/triple mutant (P.1), B.1.1.7 (Alpha), and B.1.429 (Epsilon). We performed comparative and comprehensive antigenicity mapping of the total S-glycoprotein using the Wuhan strain and the other variants and identified 9-mer, 15-mer, and 20-mer CTL epitopes through in silico analysis. The study found that 9-mer CTL epitope regions in the B.1.1.7 variant had the highest antigenicity and an average of the three epitope types. Cluster analysis of the 9-mer CTL epitopes depicted one significant cluster at the 70% level with two nodes (KGFNCYFPL and EGFNCYFPL). The phage-displayed peptides showed mimic 9-mer CTL epitopes with three clusters. CD spectra analysis showed the same band pattern of S-glycoprotein of Wuhan strain and all variants other than B.1.429. The developed 3D model of the superantigen (SAg)-like regions found an interaction pattern with the human TCR, indicating that the SAg-like component might interact with the TCR beta chain. The present study identified another partial SAg-like region (ANQFNSAIGKI) from the S-glycoprotein. Future research should examine the molecular mechanism of antigen processing for CD8+ T cells, especially all the variants’ antigens of S-glycoprotein.
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Affiliation(s)
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging and Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-do, South Korea
| | - Bidyut Mallick
- Department of Applied Science, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging and Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-do, South Korea
| | - Eun-Min Seo
- Institute for Skeletal Aging and Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-do, South Korea
- *Correspondence: Eun-Min Seo,
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, India
- Chiranjib Chakraborty,
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Nakayama M, Michels AW. Using the T Cell Receptor as a Biomarker in Type 1 Diabetes. Front Immunol 2021; 12:777788. [PMID: 34868047 PMCID: PMC8635517 DOI: 10.3389/fimmu.2021.777788] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 12/20/2022] Open
Abstract
T cell receptors (TCRs) are unique markers that define antigen specificity for a given T cell. With the evolution of sequencing and computational analysis technologies, TCRs are now prime candidates for the development of next-generation non-cell based T cell biomarkers, which provide a surrogate measure to assess the presence of antigen-specific T cells. Type 1 diabetes (T1D), the immune-mediated form of diabetes, is a prototypical organ specific autoimmune disease in which T cells play a pivotal role in targeting pancreatic insulin-producing beta cells. While the disease is now predictable by measuring autoantibodies in the peripheral blood directed to beta cell proteins, there is an urgent need to develop T cell markers that recapitulate T cell activity in the pancreas and can be a measure of disease activity. This review focuses on the potential and challenges of developing TCR biomarkers for T1D. We summarize current knowledge about TCR repertoires and clonotypes specific for T1D and discuss challenges that are unique for autoimmune diabetes. Ultimately, the integration of large TCR datasets produced from individuals with and without T1D along with computational 'big data' analysis will facilitate the development of TCRs as potentially powerful biomarkers in the development of T1D.
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Affiliation(s)
- Maki Nakayama
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Aaron W Michels
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Medicine, University of Colorado School of Medicine, Aurora, CO, United States
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The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00413-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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