1
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Yadav S, Vora DS, Sundar D, Dhanjal JK. TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding. Comput Struct Biotechnol J 2024; 23:165-173. [PMID: 38146434 PMCID: PMC10749252 DOI: 10.1016/j.csbj.2023.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 12/27/2023] Open
Abstract
Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.
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Affiliation(s)
- Shashank Yadav
- Department of Biomedical Engineering, University of Arizona, Tucson 85721, AZ, USA
| | - Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Jaspreet Kaur Dhanjal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
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2
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Meynard-Piganeau B, Feinauer C, Weigt M, Walczak AM, Mora T. TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes. Proc Natl Acad Sci U S A 2024; 121:e2316401121. [PMID: 38838016 PMCID: PMC11181096 DOI: 10.1073/pnas.2316401121] [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: 09/20/2023] [Accepted: 04/29/2024] [Indexed: 06/07/2024] Open
Abstract
The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.
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Affiliation(s)
- Barthelemy Meynard-Piganeau
- Laboratory of Computational and Quantitative Biology, Institut de Biologie Paris Seine, CNRS, Sorbonne Université, Paris75005, France
- Department of Computing Sciences, Bocconi University, Milan20100, Italy
| | | | - Martin Weigt
- Laboratory of Computational and Quantitative Biology, Institut de Biologie Paris Seine, CNRS, Sorbonne Université, Paris75005, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, Université Paris Sciences et Lettres, CNRS, Sorbonne Université, Université de Paris Cité, Paris75005, France
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, Université Paris Sciences et Lettres, CNRS, Sorbonne Université, Université de Paris Cité, Paris75005, France
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3
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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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4
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Jiang F, Guo Y, Ma H, Na S, Zhong W, Han Y, Wang T, Huang J. GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity. Brief Bioinform 2024; 25:bbae343. [PMID: 39007599 PMCID: PMC11247411 DOI: 10.1093/bib/bbae343] [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: 01/29/2024] [Revised: 05/15/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.
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Affiliation(s)
- Feng Jiang
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Yuzhi Guo
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Hehuan Ma
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Saiyang Na
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Wenliang Zhong
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Yi Han
- Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, TX 75390, United States
| | - Tao Wang
- Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, TX 75390, United States
| | - Junzhou Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
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5
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McMaster B, Thorpe C, Ogg G, Deane CM, Koohy H. Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nat Methods 2024; 21:766-776. [PMID: 38654083 DOI: 10.1038/s41592-024-02240-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 03/08/2024] [Indexed: 04/25/2024]
Abstract
T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.
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Affiliation(s)
- Benjamin McMaster
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Christopher Thorpe
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Graham Ogg
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Hashem Koohy
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Alan Turning Fellow in Health and Medicine, University of Oxford, Oxford, UK.
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6
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Croce G, Bobisse S, Moreno DL, Schmidt J, Guillame P, Harari A, Gfeller D. Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. Nat Commun 2024; 15:3211. [PMID: 38615042 PMCID: PMC11016097 DOI: 10.1038/s41467-024-47461-8] [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: 09/15/2023] [Accepted: 04/03/2024] [Indexed: 04/15/2024] Open
Abstract
T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.
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Affiliation(s)
- Giancarlo Croce
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Sara Bobisse
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Dana Léa Moreno
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Schmidt
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Philippe Guillame
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Alexandre Harari
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
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7
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Antunes DA, Baker BM, Cornberg M, Selin LK. Editorial: Quantification and prediction of T-cell cross-reactivity through experimental and computational methods. Front Immunol 2024; 15:1377259. [PMID: 38444853 PMCID: PMC10912571 DOI: 10.3389/fimmu.2024.1377259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 02/05/2024] [Indexed: 03/07/2024] Open
Affiliation(s)
- Dinler A. Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
| | - Brian M. Baker
- Department of Chemistry and Biochemistry, and Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
| | - Markus Cornberg
- Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School, Hannover, Germany
- Centre for Individualized Infection Medicine (CiiM), c/o CRC Hannover, Hannover, Germany
- German Center for Infection Research (DZIF), Partner-site Hannover-Braunschweig, Hannover, Germany
| | - Liisa K. Selin
- Department of Pathology, University of Massachusetts Medical School, Worcester, MA, United States
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8
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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9
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Yang M, Huang ZA, Zhou W, Ji J, Zhang J, He S, Zhu Z. MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations. Bioinformatics 2023; 39:btad475. [PMID: 37527015 PMCID: PMC10423027 DOI: 10.1093/bioinformatics/btad475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/05/2023] [Accepted: 07/29/2023] [Indexed: 08/03/2023] Open
Abstract
MOTIVATION The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs. RESULTS To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset. AVAILABILITY AND IMPLEMENTATION The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.
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Affiliation(s)
- Minghao Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Zhi-An Huang
- Research Office, City University of Hong Kong (Dongguan), Dongguan 523000, China
| | - Wei Zhou
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Junkai Ji
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Jun Zhang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Shan He
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, China
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10
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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: 32] [Impact Index Per Article: 32.0] [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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11
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Xu AM, Chour W, DeLucia DC, Su Y, Pavlovitch-Bedzyk AJ, Ng R, Rasheed Y, Davis MM, Lee JK, Heath JR. Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities. Cell Syst 2023; 14:273-284.e5. [PMID: 37001518 PMCID: PMC10355346 DOI: 10.1016/j.cels.2023.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 09/01/2022] [Accepted: 03/01/2023] [Indexed: 04/22/2023]
Abstract
Antigen-specific T cell receptor (TCR) sequences can have prognostic, predictive, and therapeutic value, but decoding the specificity of TCR recognition remains challenging. Unlike DNA strands that base pair, TCRs bind to their targets with different orientations and different lengths, which complicates comparisons. We present scanning parametrized by normalized TCR length (SPAN-TCR) to analyze antigen-specific TCR CDR3 sequences and identify patterns driving TCR-pMHC specificity. Using entropic analysis, SPAN-TCR identifies 2-mer motifs that decrease the diversity (entropy) of CDR3s. These motifs are the most common patterns that can predict CDR3 composition, and we identify "essential" motifs that decrease entropy in the same CDR3 α or β chain containing the 2-mer, and "super-essential" motifs that decrease entropy in both chains. Molecular dynamics analysis further suggests that these motifs may play important roles in binding. We then employ SPAN-TCR to resolve similarities in TCR repertoires against different antigens using public databases of TCR sequences.
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Affiliation(s)
- Alexander M Xu
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - William Chour
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 91125, USA
| | - Diana C DeLucia
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yapeng Su
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Rachel Ng
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Yusuf Rasheed
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Mark M Davis
- Computational and Systems Immunology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John K Lee
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - James R Heath
- Institute for Systems Biology, Seattle, WA 98109, USA.
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12
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Zhang L, Li H, Zhang Z, Wang J, Chen G, Chen D, Shi W, Jia G, Liu M. Hybrid gMLP model for interaction prediction of MHC-peptide and TCR. Front Genet 2023; 13:1092822. [PMID: 36685858 PMCID: PMC9845249 DOI: 10.3389/fgene.2022.1092822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023] Open
Abstract
Understanding the interaction of T-cell receptor (TCR) with major histocompatibility-peptide (MHC-peptide) complex is extremely important in human immunotherapy and vaccine development. However, due to the limited available data, the performance of existing models for predicting the interaction of T-cell receptors (TCR) with major histocompatibility-peptide complexes is still unsatisfactory. Deep learning models have been applied to prediction tasks in various fields and have achieved better results compared with other traditional models. In this study, we leverage the gMLP model combined with attention mechanism to predict the interaction of MHC-peptide and TCR. Experiments show that our model can predict TCR-peptide interactions accurately and can handle the problems caused by different TCR lengths. Moreover, we demonstrate that the models trained with paired CDR3β-chain and CDR3α-chain data are better than those trained with only CDR3β-chain or with CDR3α-chain data. We also demonstrate that the hybrid model has greater potential than the traditional convolutional neural network.
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Affiliation(s)
- Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China,*Correspondence: Lichao Zhang, ; Mingjun Liu,
| | - Haojin Li
- School of Software, Shandong University, Jinan, China
| | - Zhenjiu Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Jinjin Wang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | | | | | - Wentao Shi
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Gaozhi Jia
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Mingjun Liu
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China,*Correspondence: Lichao Zhang, ; Mingjun Liu,
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13
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Grazioli F, Machart P, Mösch A, Li K, Castorina LV, Pfeifer N, Min MR. Attentive Variational Information Bottleneck for TCR-peptide interaction prediction. Bioinformatics 2022; 39:6960920. [PMID: 36571499 PMCID: PMC9825246 DOI: 10.1093/bioinformatics/btac820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/18/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides. RESULTS Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences. AVAILABILITY AND IMPLEMENTATION The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg 69115, Germany
| | - Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg 69115, Germany
| | - Kai Li
- Machine Learning Department, NEC Laboratories America, Princeton, NJ 08540, USA
| | | | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen 72076, Germany
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14
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Montemurro A, Jessen LE, Nielsen M. NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions. Front Immunol 2022; 13:1055151. [PMID: 36561755 PMCID: PMC9763291 DOI: 10.3389/fimmu.2022.1055151] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.
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Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina,*Correspondence: Morten Nielsen,
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15
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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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16
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Liu M, Goo J, Liu Y, Sun W, Wu MC, Hsu L, He Q. TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes. BMC Bioinformatics 2022; 23:152. [PMID: 35484495 PMCID: PMC9052542 DOI: 10.1186/s12859-022-04690-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed. Methods We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined. Results Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients. Conclusion The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.
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Affiliation(s)
- Meiling Liu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Juna Goo
- Department of Mathematics, Boise State University, Boise, USA
| | - Yang Liu
- Department of Mathematics and Statistics, Wright State University, Dayton, USA
| | - Wei Sun
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Michael C Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
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17
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Lang F, Schrörs B, Löwer M, Türeci Ö, Sahin U. Identification of neoantigens for individualized therapeutic cancer vaccines. Nat Rev Drug Discov 2022; 21:261-282. [PMID: 35105974 PMCID: PMC7612664 DOI: 10.1038/s41573-021-00387-y] [Citation(s) in RCA: 167] [Impact Index Per Article: 83.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2021] [Indexed: 02/07/2023]
Abstract
Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.
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Affiliation(s)
- Franziska Lang
- TRON Translational Oncology, Mainz, Germany
- Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany
| | | | | | | | - Ugur Sahin
- BioNTech, Mainz, Germany.
- University Medical Center, Johannes Gutenberg University, Mainz, Germany.
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18
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Heather JM, Spindler MJ, Alonso M, Shui Y, Millar DG, Johnson D, Cobbold M, Hata A. OUP accepted manuscript. Nucleic Acids Res 2022; 50:e68. [PMID: 35325179 PMCID: PMC9262623 DOI: 10.1093/nar/gkac190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/18/2022] [Accepted: 03/09/2022] [Indexed: 11/17/2022] Open
Abstract
The study and manipulation of T cell receptors (TCRs) is central to multiple fields across basic and translational immunology research. Produced by V(D)J recombination, TCRs are often only recorded in the literature and data repositories as a combination of their V and J gene symbols, plus their hypervariable CDR3 amino acid sequence. However, numerous applications require full-length coding nucleotide sequences. Here we present Stitchr, a software tool developed to specifically address this limitation. Given minimal V/J/CDR3 information, Stitchr produces complete coding sequences representing a fully spliced TCR cDNA. Due to its modular design, Stitchr can be used for TCR engineering using either published germline or novel/modified variable and constant region sequences. Sequences produced by Stitchr were validated by synthesizing and transducing TCR sequences into Jurkat cells, recapitulating the expected antigen specificity of the parental TCR. Using a companion script, Thimble, we demonstrate that Stitchr can process a million TCRs in under ten minutes using a standard desktop personal computer. By systematizing the production and modification of TCR sequences, we propose that Stitchr will increase the speed, repeatability, and reproducibility of TCR research. Stitchr is available on GitHub.
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Affiliation(s)
- James M Heather
- To whom correspondence should be addressed. Tel: +1 617 724 0104;
| | | | | | | | - David G Millar
- Massachusetts General Hospital Cancer Center, Charlestown, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Mark Cobbold
- Massachusetts General Hospital Cancer Center, Charlestown, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Aaron N Hata
- Correspondence may also be addressed to Aaron N. Hata. Tel: +1 617 724 3442;
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19
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Barbosa CRR, Barton J, Shepherd AJ, Mishto M. Mechanistic diversity in MHC class I antigen recognition. Biochem J 2021; 478:4187-4202. [PMID: 34940832 PMCID: PMC8786304 DOI: 10.1042/bcj20200910] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/16/2021] [Accepted: 11/18/2021] [Indexed: 12/20/2022]
Abstract
Throughout its evolution, the human immune system has developed a plethora of strategies to diversify the antigenic peptide sequences that can be targeted by the CD8+ T cell response against pathogens and aberrations of self. Here we provide a general overview of the mechanisms that lead to the diversity of antigens presented by MHC class I complexes and their recognition by CD8+ T cells, together with a more detailed analysis of recent progress in two important areas that are highly controversial: the prevalence and immunological relevance of unconventional antigen peptides; and cross-recognition of antigenic peptides by the T cell receptors of CD8+ T cells.
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Affiliation(s)
- Camila R. R. Barbosa
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL London, U.K
- Francis Crick Institute, NW1 1AT London, U.K
| | - Justin Barton
- Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, WC1E 7HX London, U.K
| | - Adrian J. Shepherd
- Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, WC1E 7HX London, U.K
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL London, U.K
- Francis Crick Institute, NW1 1AT London, U.K
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20
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Baulu E, Dougé A, Chuvin N, Bay JO, Depil S. [T cell-based immunotherapies in solid tumors]. Bull Cancer 2021; 108:S96-S108. [PMID: 34920813 DOI: 10.1016/j.bulcan.2021.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/07/2021] [Accepted: 06/11/2021] [Indexed: 12/08/2022]
Abstract
In solid tumors, adoptive T cell therapies based on ex vivo amplification of antitumor T cell are represented by three main complementary approaches : (i) tumor infiltrating lymphocytes (TILs) which are amplified in vitro before reinjection to the patient, (ii) chimeric antigen receptor (CAR) engineered T cells and (iii) T cell receptor (TCR) engineered T cells. Despite encouraging results, some obstacles remain, such as optimal target selection and tumor microenvironment. In this Review, we discuss pros and cons of these different therapeutic strategies that may open new perspectives in the treatment of solid tumors.
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Affiliation(s)
- Estelle Baulu
- Centre de recherche en cancérologie de Lyon, 28, rue Laennec, 69008 Lyon, France; ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France
| | - Aurore Dougé
- CHU Estaing, service d'hématologie, 1, rue Lucie et Raymond Aubrac, 63100 Clermont-Ferrand, France
| | - Nicolas Chuvin
- ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France
| | - Jacques-Olivier Bay
- CHU Estaing, service d'hématologie, 1, rue Lucie et Raymond Aubrac, 63100 Clermont-Ferrand, France; Faculté de médecine, 28, place Henri Dunant, 63000 Clermont-Ferrand, France
| | - Stéphane Depil
- Centre de recherche en cancérologie de Lyon, 28, rue Laennec, 69008 Lyon, France; ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France; Centre Léon Bérard, 28, Prom. Léa et Napoléon Bullukian, 69008 Lyon, France; Université Claude-Bernard Lyon 1, 43, boulevard du 11 novembre 1918, 69100 Villeurbanne, France.
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21
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Vakilian M. A review on the effect of prolyl isomerization on immune response aberration and hypersensitivity reactions: A unifying hypothesis. Clin Immunol 2021; 234:108896. [PMID: 34848356 DOI: 10.1016/j.clim.2021.108896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 11/19/2021] [Accepted: 11/21/2021] [Indexed: 12/01/2022]
Abstract
Little is known about the causes and mechanisms of ectopic immune responses, including different types of hypersensitivity, superantigens, and cytokine storms. Two of the most questionable phenomena observed in immunology are why the intensity and extent of immune responses to different antigens are different, and why some self-antigens are attacked as foreign. The secondary structure of the peptides involved in the immune system, such as the epitope-paratope interfaces plays a pivotal role in the resulting immune responses. Prolyl cis/trans isomerization plays a fundamental role in the form of the secondary structure and the folding of proteins. This review covers some of the emerging evidence indicating the impact of prolyl isomerization on protein conformation, aberration of immune responses, and the development of hypersensitivity reactions.
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Affiliation(s)
- Mehrdad Vakilian
- Department of Cell Biology, Genetics and Physiology, University of Malaga (UMA), The Institute of Biomedical Research in Malaga (IBIMA), Málaga, Spain.
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22
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Schaap-Johansen AL, Vujović M, Borch A, Hadrup SR, Marcatili P. T Cell Epitope Prediction and Its Application to Immunotherapy. Front Immunol 2021; 12:712488. [PMID: 34603286 PMCID: PMC8479193 DOI: 10.3389/fimmu.2021.712488] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/12/2021] [Indexed: 12/13/2022] Open
Abstract
T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.
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Affiliation(s)
| | - Milena Vujović
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Annie Borch
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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23
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Montemurro A, Schuster V, Povlsen HR, Bentzen AK, Jurtz V, Chronister WD, Crinklaw A, Hadrup SR, Winther O, Peters B, Jessen LE, Nielsen M. NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data. Commun Biol 2021; 4:1060. [PMID: 34508155 PMCID: PMC8433451 DOI: 10.1038/s42003-021-02610-3] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 08/27/2021] [Indexed: 12/17/2022] Open
Abstract
Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that "shallow" convolutional neural network (CNN) architectures are adequate to deal with the problem complexity imposed by the length variations of TCRs. We demonstrate that current public bulk CDR3β-pMHC binding data overall is of low quality and that the development of accurate prediction models is contingent on paired α/β TCR sequence data corresponding to at least 150 distinct pairs for each investigated pMHC. In comparison, models trained on CDR3α or CDR3β data alone demonstrated a variable and pMHC specific relative performance drop. Together these findings support that T-cell specificity is predictable given the availability of accurate and sufficient paired TCR sequence data. NetTCR-2.0 is publicly available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.0 .
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Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Viktoria Schuster
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Helle Rus Povlsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Amalie Kai Bentzen
- Department of Health Technology, Section for Experimental and Translational Immunology, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Vanessa Jurtz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - William D Chronister
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
| | - Austin Crinklaw
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
| | - Sine R Hadrup
- Department of Health Technology, Section for Experimental and Translational Immunology, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Ole Winther
- Department of Biology, Bioinformatics Centre, University of Copenhagen, 2200, Copenhagen, Denmark
- Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs., Lyngby, 2800, Denmark
- Centre for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, København, Ø 2100, Denmark
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
- Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA, 92037, USA
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark.
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina.
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24
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Milighetti M, Shawe-Taylor J, Chain B. Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes. Front Physiol 2021; 12:730908. [PMID: 34566692 PMCID: PMC8456106 DOI: 10.3389/fphys.2021.730908] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/02/2021] [Indexed: 11/13/2022] Open
Abstract
The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.
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Affiliation(s)
- Martina Milighetti
- Division of Infection and Immunity, University College London, London, United Kingdom
- Cancer Institute, University College London, London, United Kingdom
| | - John Shawe-Taylor
- Department of Computer Science, University College London, London, United Kingdom
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
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25
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Ferragut F, Acevedo GR, Gómez KA. T Cell Specificity: A Great Challenge in Chagas Disease. Front Immunol 2021; 12:674078. [PMID: 34267750 PMCID: PMC8276045 DOI: 10.3389/fimmu.2021.674078] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 06/09/2021] [Indexed: 11/13/2022] Open
Abstract
The CD4+ and CD8+ T cell immune response against T. cruzi, the parasite causing Chagas disease, are relevant for both parasite control and disease pathogenesis. Several studies have been focused on their phenotype and functionally, but only a few have drilled down to identify the parasite proteins that are processed and presented to these cells, especially to CD4+ T lymphocytes. Although approximately 10,000 proteins are encoded per haploid T. cruzi genome, fewer than 200 T cell epitopes from 49 T. cruzi proteins have been identified so far. In this context, a detailed knowledge of the specific targets of T cell memory response emerges as a prime tool for the conceptualization and development of prophylactic or therapeutic vaccines, an approach with great potential to prevent and treat this chronic disease. Here, we review the available information about this topic in a comprehensive manner and discuss the future challenges in the field.
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Affiliation(s)
- Fátima Ferragut
- Laboratorio de Inmunología de las Infecciones por Tripanosomátidos, Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Gonzalo R Acevedo
- Laboratorio de Inmunología de las Infecciones por Tripanosomátidos, Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Karina A Gómez
- Laboratorio de Inmunología de las Infecciones por Tripanosomátidos, Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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26
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Zhang W, Hawkins PG, He J, Gupta NT, Liu J, Choonoo G, Jeong SW, Chen CR, Dhanik A, Dillon M, Deering R, Macdonald LE, Thurston G, Atwal GS. A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity. SCIENCE ADVANCES 2021; 7:7/20/eabf5835. [PMID: 33990328 PMCID: PMC8121425 DOI: 10.1126/sciadv.abf5835] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/25/2021] [Indexed: 05/04/2023]
Abstract
T cell receptor (TCR) antigen-specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide-major histocompatibility complex) interactions and a neural network-based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen-specific interactions for basic immunological research and clinical immune monitoring.
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Affiliation(s)
- Wen Zhang
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA.
| | - Peter G Hawkins
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Jing He
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Namita T Gupta
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Jinrui Liu
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Gabrielle Choonoo
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Se W Jeong
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Calvin R Chen
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Ankur Dhanik
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Myles Dillon
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Raquel Deering
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Lynn E Macdonald
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Gavin Thurston
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA
| | - Gurinder S Atwal
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY 10591, USA.
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27
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Jones HF, Molvi Z, Klatt MG, Dao T, Scheinberg DA. Empirical and Rational Design of T Cell Receptor-Based Immunotherapies. Front Immunol 2021; 11:585385. [PMID: 33569049 PMCID: PMC7868419 DOI: 10.3389/fimmu.2020.585385] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/04/2020] [Indexed: 01/04/2023] Open
Abstract
The use of T cells reactive with intracellular tumor-associated or tumor-specific antigens has been a promising strategy for cancer immunotherapies in the past three decades, but the approach has been constrained by a limited understanding of the T cell receptor's (TCR) complex functions and specificities. Newer TCR and T cell-based approaches are in development, including engineered adoptive T cells with enhanced TCR affinities, TCR mimic antibodies, and T cell-redirecting bispecific agents. These new therapeutic modalities are exciting opportunities by which TCR recognition can be further exploited for therapeutic benefit. In this review we summarize the development of TCR-based therapeutic strategies and focus on balancing efficacy and potency versus specificity, and hence, possible toxicity, of these powerful therapeutic modalities.
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Affiliation(s)
- Heather F. Jones
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Weill Cornell Medicine, New York, NY, United States
| | - Zaki Molvi
- Weill Cornell Medicine, New York, NY, United States
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Martin G. Klatt
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Tao Dao
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - David A. Scheinberg
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Weill Cornell Medicine, New York, NY, United States
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28
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Borrman T, Pierce BG, Vreven T, Baker BM, Weng Z. High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides. Bioinformatics 2020; 36:5377-5385. [PMID: 33355667 PMCID: PMC8016493 DOI: 10.1093/bioinformatics/btaa1050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 11/23/2020] [Accepted: 12/08/2020] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides. RESULTS Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides. AVAILABILITY AND IMPLEMENTATION Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tyler Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Brian M Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA.,Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
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29
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Lee CH, Salio M, Napolitani G, Ogg G, Simmons A, Koohy H. Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors. Front Immunol 2020; 11:565096. [PMID: 33193332 PMCID: PMC7642207 DOI: 10.3389/fimmu.2020.565096] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 09/07/2020] [Indexed: 12/13/2022] Open
Abstract
Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.
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Affiliation(s)
- Chloe H. Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Mariolina Salio
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Giorgio Napolitani
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Graham Ogg
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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30
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T-cell repertoire analysis and metrics of diversity and clonality. Curr Opin Biotechnol 2020; 65:284-295. [PMID: 32889231 DOI: 10.1016/j.copbio.2020.07.010] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 07/22/2020] [Indexed: 12/12/2022]
Abstract
The recent developments of high-throughput bulk and single-cell sequencing technologies accelerated the understanding of the complexity of immune repertoire dynamics combined to transcriptomics. Also, profiling of cellular repertoires in health or disease requires statistical metrics to capture clonal diversity characterized by clones frequency, repertoire richness and convergence. Here we present the common technologies of bulk and single-cell sequencing of T-cell receptors (TCRs), discuss current knowledge regarding computational tools clustering and predicting specificity of TCR repertoires based on shared structural motifs and review main indices for repertoire diversity and convergence analyses. These tools represent potential biomarkers to decipher the fitness of immune repertoires in diseased or treated patients but also the presages and promises of computational approaches to revolutionize personalized immunotherapy.
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31
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Vujovic M, Degn KF, Marin FI, Schaap-Johansen AL, Chain B, Andresen TL, Kaplinsky J, Marcatili P. T cell receptor sequence clustering and antigen specificity. Comput Struct Biotechnol J 2020; 18:2166-2173. [PMID: 32952933 PMCID: PMC7473833 DOI: 10.1016/j.csbj.2020.06.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/25/2020] [Accepted: 06/27/2020] [Indexed: 11/17/2022] Open
Abstract
There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.
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Affiliation(s)
- Milena Vujovic
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Kristine Fredlund Degn
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Frederikke Isa Marin
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Anna-Lisa Schaap-Johansen
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Benny Chain
- UCL Division of Infection and Immunity, University College London, Wing 3.2, Cruciform Building, Gower Street, London WC1E 6BT, United Kingdom
| | - Thomas Lars Andresen
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Joseph Kaplinsky
- Ludwig Institute for Cancer Research Ltd, University of Oxford, Nuffield Department of Medicine, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Paolo Marcatili
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
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32
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Fischer DS, Wu Y, Schubert B, Theis FJ. Predicting antigen specificity of single T cells based on TCR CDR3 regions. Mol Syst Biol 2020; 16:e9416. [PMID: 32779888 PMCID: PMC7418512 DOI: 10.15252/msb.20199416] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 07/13/2020] [Accepted: 07/22/2020] [Indexed: 11/12/2022] Open
Abstract
It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining.
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Affiliation(s)
- David S Fischer
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- TUM School of Life Sciences WeihenstephanTechnical University of MunichFreisingGermany
| | - Yihan Wu
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
| | - Benjamin Schubert
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- Department of MathematicsTechnical University of MunichGarching bei MünchenGermany
| | - Fabian J Theis
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- TUM School of Life Sciences WeihenstephanTechnical University of MunichFreisingGermany
- Department of MathematicsTechnical University of MunichGarching bei MünchenGermany
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33
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Teraguchi S, Saputri DS, Llamas-Covarrubias MA, Davila A, Diez D, Nazlica SA, Rozewicki J, Ismanto HS, Wilamowski J, Xie J, Xu Z, Loza-Lopez MDJ, van Eerden FJ, Li S, Standley DM. Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput Struct Biotechnol J 2020; 18:2000-2011. [PMID: 32802272 PMCID: PMC7366105 DOI: 10.1016/j.csbj.2020.07.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/08/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.
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Affiliation(s)
- Shunsuke Teraguchi
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Dianita S. Saputri
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Mara Anais Llamas-Covarrubias
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Mexico
| | - Ana Davila
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Diego Diez
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Sedat Aybars Nazlica
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - John Rozewicki
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Hendra S. Ismanto
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jiaqi Xie
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | | | - Floris J. van Eerden
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Daron M. Standley
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
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34
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Abstract
T cells respond to threats in an antigen-specific manner using T cell receptors (TCRs) that recognize short peptide antigens presented on major histocompatibility complex (MHC) proteins. The TCR-peptide-MHC interaction mediated between a T cell and its target cell dictates its function and thereby influences its role in disease. A lack of approaches for antigen discovery has limited the fundamental understanding of the antigenic landscape of the overall T cell response. Recent advances in high-throughput sequencing, mass cytometry, microfluidics and computational biology have led to a surge in approaches to address the challenge of T cell antigen discovery. Here, we summarize the scope of this challenge, discuss in depth the recent exciting work and highlight the outstanding questions and remaining technical hurdles in this field.
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35
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Abstract
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.
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Affiliation(s)
- Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Massimo Andreatta
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA
- Department of Medicine, University of California, San Diego, La Jolla, California 92093, USA
| | - Søren Buus
- Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
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36
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Croft NP. Peptide Presentation to T Cells: Solving the Immunogenic Puzzle: Systems Immunology Profiling of Antigen Presentation for Prediction of CD8 + T Cell Immunogenicity. Bioessays 2020; 42:e1900200. [PMID: 31958157 DOI: 10.1002/bies.201900200] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/18/2019] [Indexed: 02/02/2023]
Abstract
The vertebrate immune system uses an impressive arsenal of mechanisms to combat harmful cellular states such as infection. One way is via cells delivering real-time snapshots of their protein content to the cell surface in the form of short peptides. Specialized immune cells (T cells) sample these peptides and assess whether they are foreign, warranting an action such as destruction of the infected cell. The delivery of peptides to the cell surface is termed antigen processing and presentation, and decades of research have provided unprecedented understanding of this process. However, predicting the capacity for a given peptide to be immunogenic-to elicit a T cell response-has remained both enigmatic and a long sought-after goal. In the era of big data, a point is being approached where the steps of antigen processing and presentation can be quantified and assessed against peptide immunogenicity in order to build predictive models. This review presents new findings in this area and contemplates challenges ahead.
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Affiliation(s)
- Nathan P Croft
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, 3800, Australia
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37
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Mösch A, Raffegerst S, Weis M, Schendel DJ, Frishman D. Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors. Front Genet 2019; 10:1141. [PMID: 31798635 PMCID: PMC6878726 DOI: 10.3389/fgene.2019.01141] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/21/2019] [Indexed: 12/30/2022] Open
Abstract
In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.
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Affiliation(s)
- Anja Mösch
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Silke Raffegerst
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Manon Weis
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dolores J. Schendel
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
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