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Wang A, Lin X, Chau KN, Onuchic JN, Levine H, George JT. RACER-m leverages structural features for sparse T cell specificity prediction. SCIENCE ADVANCES 2024; 10:eadl0161. [PMID: 38748791 PMCID: PMC11095454 DOI: 10.1126/sciadv.adl0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
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Affiliation(s)
- Ailun Wang
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Xingcheng Lin
- Department of Physics, North Carolina State University, Raleigh, NC, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Kevin Ng Chau
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - José N. Onuchic
- Departments of Physics and Astronomy, Chemistry, and Biosciences, Rice University, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Jason T. George
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Biomedical Engineering, Texas A&M University, Houston, TX, USA
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2
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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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3
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Ribeiro-Filho HV, Jara GE, Guerra JVS, Cheung M, Felbinger NR, Pereira JGC, Pierce BG, Lopes-de-Oliveira PS. Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590222. [PMID: 38712216 PMCID: PMC11071404 DOI: 10.1101/2024.04.19.590222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively unexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.
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Affiliation(s)
- Helder V. Ribeiro-Filho
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil
| | - Gabriel E. Jara
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil
| | - João V. S. Guerra
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil
- Graduate Program in Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, University of Campinas, Campinas, São Paulo, 13083-871, Brazil
| | - Melyssa Cheung
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Nathaniel R. Felbinger
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - José G. C. Pereira
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil
| | - Brian G. Pierce
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Paulo S. Lopes-de-Oliveira
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil
- Graduate Program in Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, University of Campinas, Campinas, São Paulo, 13083-871, Brazil
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4
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Klebanoff CA, Chandran SS, Baker BM, Quezada SA, Ribas A. T cell receptor therapeutics: immunological targeting of the intracellular cancer proteome. Nat Rev Drug Discov 2023; 22:996-1017. [PMID: 37891435 PMCID: PMC10947610 DOI: 10.1038/s41573-023-00809-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2023] [Indexed: 10/29/2023]
Abstract
The T cell receptor (TCR) complex is a naturally occurring antigen sensor that detects, amplifies and coordinates cellular immune responses to epitopes derived from cell surface and intracellular proteins. Thus, TCRs enable the targeting of proteins selectively expressed by cancer cells, including neoantigens, cancer germline antigens and viral oncoproteins. As such, TCRs have provided the basis for an emerging class of oncology therapeutics. Herein, we review the current cancer treatment landscape using TCRs and TCR-like molecules. This includes adoptive cell transfer of T cells expressing endogenous or engineered TCRs, TCR bispecific engagers and antibodies specific for human leukocyte antigen (HLA)-bound peptides (TCR mimics). We discuss the unique complexities associated with the clinical development of these therapeutics, such as HLA restriction, TCR retrieval, potency assessment and the potential for cross-reactivity. In addition, we highlight emerging clinical data that establish the antitumour potential of TCR-based therapies, including tumour-infiltrating lymphocytes, for the treatment of diverse human malignancies. Finally, we explore the future of TCR therapeutics, including emerging genome editing methods to safely enhance potency and strategies to streamline patient identification.
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Affiliation(s)
- Christopher A Klebanoff
- Memorial Sloan Kettering Cancer Center (MSKCC), Human Oncology and Pathogenesis Program, New York, NY, USA.
| | - Smita S Chandran
- Memorial Sloan Kettering Cancer Center (MSKCC), Human Oncology and Pathogenesis Program, New York, NY, USA
- Parker Institute for Cancer Immunotherapy, New York, NY, USA
- Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Brian M Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, ID, USA
- The Harper Cancer Research Institute, University of Notre Dame, Notre Dame, ID, USA
| | - Sergio A Quezada
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Achilles Therapeutics, London, UK
| | - Antoni Ribas
- Jonsson Comprehensive Cancer Center at the University of California, Los Angeles (UCLA), Los Angeles, CA, USA
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Pang Z, Lu MM, Zhang Y, Gao Y, Bai JJ, Gu JY, Xie L, Wu WZ. Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges. Biomark Res 2023; 11:104. [PMID: 38037114 PMCID: PMC10690996 DOI: 10.1186/s40364-023-00534-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/22/2023] [Indexed: 12/02/2023] Open
Abstract
Adoptive cell therapy using T cell receptor-engineered T cells (TCR-T) is a promising approach for cancer therapy with an expectation of no significant side effects. In the human body, mature T cells are armed with an incredible diversity of T cell receptors (TCRs) that theoretically react to the variety of random mutations generated by tumor cells. The outcomes, however, of current clinical trials using TCR-T cell therapies are not very successful especially involving solid tumors. The therapy still faces numerous challenges in the efficient screening of tumor-specific antigens and their cognate TCRs. In this review, we first introduce TCR structure-based antigen recognition and signaling, then describe recent advances in neoantigens and their specific TCR screening technologies, and finally summarize ongoing clinical trials of TCR-T therapies against neoantigens. More importantly, we also present the current challenges of TCR-T cell-based immunotherapies, e.g., the safety of viral vectors, the mismatch of T cell receptor, the impediment of suppressive tumor microenvironment. Finally, we highlight new insights and directions for personalized TCR-T therapy.
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Affiliation(s)
- Zhi Pang
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Man-Man Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Yu Zhang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Yuan Gao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Jin-Jin Bai
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jian-Ying Gu
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China.
| | - Wei-Zhong Wu
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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6
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Nikam R, Yugandhar K, Gromiha MM. Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2023; 1871:140948. [PMID: 37567456 DOI: 10.1016/j.bbapap.2023.140948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/05/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.
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Affiliation(s)
- Rahul Nikam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Kumar Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computational Biology, Cornell University, New York, USA
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan; Department of Computer Science, National University of Singapore, Singapore.
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7
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Kumar A K, Rathore RS. Categorization of hotspots into three types - weak, moderate and strong to distinguish protein-protein versus protein-peptide interactions. J Biomol Struct Dyn 2023:1-13. [PMID: 37649387 DOI: 10.1080/07391102.2023.2252077] [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: 04/15/2023] [Accepted: 08/18/2023] [Indexed: 09/01/2023]
Abstract
Protein-protein and protein-peptide interactions (PPI and PPepI) belong to a similar category of interactions, yet seemingly subtle differences exist among them. To characterize differences between protein-protein (PP) and protein-peptide (PPep) interactions, we have focussed on two important classes of residues-hotspot and anchor residues. Using implicit solvation-based free energy calculations, a very large-scale alanine scanning has been performed on benchmark datasets, consisting of over 5700 interface residues. The differences in the two categories are more pronounced, if the data were divided into three distinct types, namely - weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG, ∼2-10 kcal/mol), moderate hotspots (ΔΔG, ∼10-20 kcal/mol) and strong hotspots (ΔΔG ≥ ∼20 kcal/mol). The analysis suggests that for PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate hotspot and charged residues (principally Arg) are overwhelmingly present in the strong hotspot. On the other hand, in the PPepI dataset, the distribution shifts from predominantly hydrophobic and polar (in the weak type) to almost similar preference for polar, hydrophobic and charged residues (in moderate type) and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. The preferred anchor residues in both categories are Arg, Tyr and Leu, possessing bulky side chain and which also strike a delicate balance between side chain flexibility and rigidity. The present knowledge should aid in effective design of biologics, by augmentation or disruption of PPIs with peptides or peptidomimetics.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Kiran Kumar A
- Department of Bioinformatics, School of Earth, Biological and Environmental Sciences, Central University of South Bihar, Gaya, India
| | - R S Rathore
- Department of Bioinformatics, School of Earth, Biological and Environmental Sciences, Central University of South Bihar, Gaya, India
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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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Shcherbinin DS, Karnaukhov VK, Zvyagin IV, Chudakov DM, Shugay M. Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features. Front Immunol 2023; 14:1224969. [PMID: 37649481 PMCID: PMC10464843 DOI: 10.3389/fimmu.2023.1224969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/27/2023] [Indexed: 09/01/2023] Open
Abstract
Introduction T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity. Methods Identification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R. Results Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues. Conclusion Our results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.
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Affiliation(s)
- Dmitrii S. Shcherbinin
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Laboratory of Structural Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Vadim K. Karnaukhov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Ivan V. Zvyagin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Dmitriy M. Chudakov
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Center of Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
| | - Mikhail Shugay
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
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10
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Gouttefangeas C, Klein R, Maia A. The good and the bad of T cell cross-reactivity: challenges and opportunities for novel therapeutics in autoimmunity and cancer. Front Immunol 2023; 14:1212546. [PMID: 37409132 PMCID: PMC10319254 DOI: 10.3389/fimmu.2023.1212546] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/24/2023] [Indexed: 07/07/2023] Open
Abstract
T cells are main actors of the immune system with an essential role in protection against pathogens and cancer. The molecular key event involved in this absolutely central task is the interaction of membrane-bound specific T cell receptors with peptide-MHC complexes which initiates T cell priming, activation and recall, and thus controls a range of downstream functions. While textbooks teach us that the repertoire of mature T cells is highly diverse, it is clear that this diversity cannot possibly cover all potential foreign peptides that might be encountered during life. TCR cross-reactivity, i.e. the ability of a single TCR to recognise different peptides, offers the best solution to this biological challenge. Reports have shown that indeed, TCR cross-reactivity is surprisingly high. Hence, the T cell dilemma is the following: be as specific as possible to target foreign danger and spare self, while being able to react to a large spectrum of body-threatening situations. This has major consequences for both autoimmune diseases and cancer, and significant implications for the development of T cell-based therapies. In this review, we will present essential experimental evidence of T cell cross-reactivity, implications for two opposite immune conditions, i.e. autoimmunity vs cancer, and how this can be differently exploited for immunotherapy approaches. Finally, we will discuss the tools available for predicting cross-reactivity and how improvements in this field might boost translational approaches.
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Affiliation(s)
- Cécile Gouttefangeas
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) partner site Tübingen, Tübingen, Germany
| | - Reinhild Klein
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Ana Maia
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
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11
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Peng X, Lei Y, Feng P, Jia L, Ma J, Zhao D, Zeng J. Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00634-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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12
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Baulu E, Gardet C, Chuvin N, Depil S. TCR-engineered T cell therapy in solid tumors: State of the art and perspectives. SCIENCE ADVANCES 2023; 9:eadf3700. [PMID: 36791198 PMCID: PMC9931212 DOI: 10.1126/sciadv.adf3700] [Citation(s) in RCA: 59] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/06/2023] [Indexed: 05/25/2023]
Abstract
T cell engineering has changed the landscape of cancer immunotherapy. Chimeric antigen receptor T cells have demonstrated a remarkable efficacy in the treatment of B cell malignancies in hematology. However, their clinical impact on solid tumors has been modest so far. T cells expressing an engineered T cell receptor (TCR-T cells) represent a promising therapeutic alternative. The target repertoire is not limited to membrane proteins, and intrinsic features of TCRs such as high antigen sensitivity and near-to-physiological signaling may improve tumor cell detection and killing while improving T cell persistence. In this review, we present the clinical results obtained with TCR-T cells targeting different tumor antigen families. We detail the different methods that have been developed to identify and optimize a TCR candidate. We also discuss the challenges of TCR-T cell therapies, including toxicity assessment and resistance mechanisms. Last, we share some perspectives and highlight future directions in the field.
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Affiliation(s)
- Estelle Baulu
- Centre de Recherche en Cancérologie de Lyon, Lyon, France
- ErVaccine Technologies, Lyon, France
| | - Célia Gardet
- Centre de Recherche en Cancérologie de Lyon, Lyon, France
| | | | - Stéphane Depil
- Centre de Recherche en Cancérologie de Lyon, Lyon, France
- ErVaccine Technologies, Lyon, France
- Centre Léon Bérard, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
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13
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Papadaki GF, Ani O, Florio TJ, Young MC, Danon JN, Sun Y, Dersh D, Sgourakis NG. Decoupling peptide binding from T cell receptor recognition with engineered chimeric MHC-I molecules. Front Immunol 2023; 14:1116906. [PMID: 36761745 PMCID: PMC9905809 DOI: 10.3389/fimmu.2023.1116906] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/10/2023] [Indexed: 01/26/2023] Open
Abstract
Major Histocompatibility Complex class I (MHC-I) molecules display self, viral or aberrant epitopic peptides to T cell receptors (TCRs), which employ interactions between complementarity-determining regions with both peptide and MHC-I heavy chain 'framework' residues to recognize specific Human Leucocyte Antigens (HLAs). The highly polymorphic nature of the HLA peptide-binding groove suggests a malleability of interactions within a common structural scaffold. Here, using structural data from peptide:MHC-I and pMHC:TCR structures, we first identify residues important for peptide and/or TCR binding. We then outline a fixed-backbone computational design approach for engineering synthetic molecules that combine peptide binding and TCR recognition surfaces from existing HLA allotypes. X-ray crystallography demonstrates that chimeric molecules bridging divergent HLA alleles can bind selected peptide antigens in a specified backbone conformation. Finally, in vitro tetramer staining and biophysical binding experiments using chimeric pMHC-I molecules presenting established antigens further demonstrate the requirement of TCR recognition on interactions with HLA framework residues, as opposed to interactions with peptide-centric Chimeric Antigen Receptors (CARs). Our results underscore a novel, structure-guided platform for developing synthetic HLA molecules with desired properties as screening probes for peptide-centric interactions with TCRs and other therapeutic modalities.
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Affiliation(s)
- Georgia F. Papadaki
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Omar Ani
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Tyler J. Florio
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael C. Young
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Julia N. Danon
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Yi Sun
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Devin Dersh
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nikolaos G. Sgourakis
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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14
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Guo Z, Yamaguchi R. Machine learning methods for protein-protein binding affinity prediction in protein design. FRONTIERS IN BIOINFORMATICS 2022; 2:1065703. [PMID: 36591334 PMCID: PMC9800603 DOI: 10.3389/fbinf.2022.1065703] [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: 10/10/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.
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Affiliation(s)
- Zhongliang Guo
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan,Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan,*Correspondence: Rui Yamaguchi,
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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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Perez MAS, Cuendet MA, Röhrig UF, Michielin O, Zoete V. Structural Prediction of Peptide-MHC Binding Modes. Methods Mol Biol 2022; 2405:245-282. [PMID: 35298818 DOI: 10.1007/978-1-0716-1855-4_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.
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Affiliation(s)
- Marta A S Perez
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne, Switzerland
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michel A Cuendet
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland
| | - Ute F Röhrig
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Olivier Michielin
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland.
| | - Vincent Zoete
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, Lausanne, Switzerland.
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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17
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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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18
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Dhusia K, Su Z, Wu Y. A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes. Mol Immunol 2021; 139:76-86. [PMID: 34455212 PMCID: PMC10811653 DOI: 10.1016/j.molimm.2021.07.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/13/2021] [Accepted: 07/25/2021] [Indexed: 11/27/2022]
Abstract
The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can only recognize a specific subset from a large repertoire of peptide-MHC (pMHC) complexes, it is very often that peptides in this subset share little sequence similarity. This is known as the specificity and cross-reactivity of T cells, respectively. The binding affinities between different types of TCRs and pMHC are the major driving force to shape this specificity and cross-reactivity in T cell recognition. The binding affinities, furthermore, are determined by the sequence and structural properties at the interfaces between TCRs and pMHC. Fortunately, a wealth of data on binding and structures of TCR-pMHC interactions becomes publicly accessible in online resources, which offers us the opportunity to develop a random forest classifier for predicting the binding affinities between TCR and pMHC based on the structure of their complexes. Specifically, the structure and sequence of a given complex were projected onto a high-dimensional feature space as the input of the classifier, which was then trained by a large-scale benchmark dataset. Based on the cross-validation results, we found that our machine learning model can predict if the binding affinity of a given TCR-pMHC complex is stronger or weaker than a predefined threshold with an overall accuracy approximately around 75 %. The significance of our prediction was estimated by statistical analysis. Moreover, more than 60 % of binding affinities in the ATLAS database can be successfully classified into groups within the range of 2 kcal/mol. Additionally, we show that TCR-pMHC complexes with strong binding affinity prefer hydrophobic interactions between amino acids with large aromatic rings instead of electrostatic interactions. Our results therefore provide insights to design engineered TCRs which enhance the specificity for their targeted epitopes. Taken together, this method can serve as a useful addition to a suite of existing approaches which study binding between TCR and pMHC.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States.
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19
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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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20
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Padariya M, Kote S, Mayordomo M, Dapic I, Alfaro J, Hupp T, Fahraeus R, Kalathiya U. Structural determinants of peptide-dependent TAP1-TAP2 transit passage targeted by viral proteins and altered by cancer-associated mutations. Comput Struct Biotechnol J 2021; 19:5072-5091. [PMID: 34589184 PMCID: PMC8453138 DOI: 10.1016/j.csbj.2021.09.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/06/2021] [Accepted: 09/06/2021] [Indexed: 01/20/2023] Open
Abstract
The TAP1-TAP2 complex transports antigenic peptide substrates into the endoplasmic reticulum (ER). In ER, the peptides are further processed and loaded on the major histocompatibility class (MHC) I molecules by the peptide loading complex (PLC). The TAP transporters are linked with the PLC; a target for cancers and viral immune evasion. But the mechanisms whereby the cancer-derived mutations in TAP1-TAP2 or viral factors targeting the PLC, interfere peptide transport are only emerging. This study describes that transit of peptides through TAP can take place via two different channels (4 or 8 helices) depending on peptide length and sequence. Molecular dynamics and binding affinity predictions of peptide-transporters demonstrated that smaller peptides (8-10 mers; e.g. AAGIGILTV, SIINFEKL) can transport quickly through the transport tunnel compared to longer peptides (15-mer; e.g. ENPVVHFFKNIVTPR). In line with a regulated and selective peptide transport by TAPs, the immunopeptidome upon IFN-γ treatment in melanoma cells induced the shorter length (9-mer) peptide presentation over MHC-I that exhibit a relatively weak binding affinity with TAP. A conserved distance between N and C terminus residues of the studied peptides in the transport tunnel were reported. Furthermore, by adversely interacting with the TAP transport passage or affecting TAPNBD domains tilt movement, the viral proteins and cancer-derived mutations in TAP1-TAP2 may induce allosteric effects in TAP that block conformation of the tunnel (closed towards ER lumen). Interestingly, some cancer-associated mutations (e.g. TAP1R372Q and TAP2R373H) can specifically interfere with selective transport channels (i.e. for longer-peptides). These results provide a model for how viruses and cancer-associated mutations targeting TAP interfaces can affect MHC-I antigen presentation, and how the IFN-γ pathway alters MHC-I antigen presentation via the kinetics of peptide transport.
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Affiliation(s)
- Monikaben Padariya
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
| | - Sachin Kote
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
| | - Marcos Mayordomo
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
| | - Irena Dapic
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
| | - Javier Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland EH4 2XR, United Kingdom
| | - Ted Hupp
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland EH4 2XR, United Kingdom
| | - Robin Fahraeus
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
- Inserm UMRS1131, Institut de Génétique Moléculaire, Université Paris 7, Hôpital St. Louis, F-75010 Paris, France
- Department of Medical Biosciences, Building 6M, Umeå University, 901 85 Umeå, Sweden
- RECAMO, Masaryk Memorial Cancer Institute, Zlutykopec 7, 65653 Brno, Czech Republic
| | - Umesh Kalathiya
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland
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21
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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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22
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Kniga AE, Polyakov IV, Nemukhin AV. [In silico specificity determination of neoantigen-reactive T-lymphocytes]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2021; 67:251-258. [PMID: 34142532 DOI: 10.18097/pbmc20216703251] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Effective personalized immunotherapies of the future will need to capture not only the peculiarities of the patient's tumor but also of his immune response to it. In this study, using results of in vitro high-throughput specificity assays, and combining comparative models of pMHCs and TCRs using molecular docking, we have constructed all-atom models for the putative complexes of all their possible pairwise TCR-pMHC combinations. For the models obtained we have calculated a dataset of physics-based scores and have trained binary classifiers that perform better compared to their solely sequence-based counterparts. These structure-based classifiers pinpoint the most prominent energetic terms and structural features characterizing the type of protein-protein interactions that underlies the immune recognition of tumors by T cells.
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Affiliation(s)
- A E Kniga
- M.V. Lomonosov Moscow State University, Moscow, Russia; N.M. Emanuel Institute of Biochemical Physics RAS, Moscow, Russia
| | - I V Polyakov
- M.V. Lomonosov Moscow State University, Moscow, Russia; N.M. Emanuel Institute of Biochemical Physics RAS, Moscow, Russia
| | - A V Nemukhin
- M.V. Lomonosov Moscow State University, Moscow, Russia; N.M. Emanuel Institute of Biochemical Physics RAS, Moscow, Russia
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23
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Guest JD, Vreven T, Zhou J, Moal I, Jeliazkov JR, Gray JJ, Weng Z, Pierce BG. An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure 2021; 29:606-621.e5. [PMID: 33539768 DOI: 10.1016/j.str.2021.01.005] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 11/15/2020] [Accepted: 01/11/2021] [Indexed: 01/04/2023]
Abstract
Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.
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Affiliation(s)
- Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Jing Zhou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Iain Moal
- Computational Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, UK
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
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24
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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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25
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Nerli S, Sgourakis NG. Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens. FRONTIERS IN MEDICAL TECHNOLOGY 2020; 2:553478. [PMID: 35047875 PMCID: PMC8757863 DOI: 10.3389/fmedt.2020.553478] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 10/07/2020] [Indexed: 11/13/2022] Open
Abstract
SARS-CoV-2-specific CD4 and CD8 T cells have been shown to be present in individuals with acute, mild, and asymptomatic Coronavirus disease (COVID-19). Toward the development of diagnostic and therapeutic tools to fight COVID-19, it is important to predict and characterize T cell epitopes expressed by SARS-CoV-2. Here, we use RosettaMHC, a comparative modeling approach which leverages existing structures of peptide/MHC complexes available in the Protein Data Bank, to derive accurate 3D models for putative SARS-CoV-2 CD8 epitopes. We outline an application of our method to model 8-10 residue epitopic peptides predicted to bind to the common allele HLA-A*02:01, and we make our models publicly available through an online database (https://rosettamhc.chemistry.ucsc.edu). We further compare electrostatic surfaces with models of homologous peptide/HLA-A*02:01 complexes from human common cold coronavirus strains to identify epitopes which may be recognized by a shared pool of cross-reactive TCRs. As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models can be used to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.
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Affiliation(s)
- Santrupti Nerli
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Nikolaos G. Sgourakis
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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26
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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: 62] [Impact Index Per Article: 15.5] [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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27
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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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28
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Gowthaman R, Pierce BG. TCR3d: The T cell receptor structural repertoire database. Bioinformatics 2020; 35:5323-5325. [PMID: 31240309 PMCID: PMC6954642 DOI: 10.1093/bioinformatics/btz517] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 05/31/2019] [Accepted: 06/20/2019] [Indexed: 12/31/2022] Open
Abstract
Summary T cell receptors (TCRs) are critical molecules of the adaptive immune system, capable of recognizing diverse antigens, including peptides, lipids and small molecules, and represent a rapidly growing class of therapeutics. Determining the structural and mechanistic basis of TCR targeting of antigens is a major challenge, as each individual has a vast and diverse repertoire of TCRs. Despite shared general recognition modes, diversity in TCR sequence and recognition represents a challenge to predictive modeling and computational techniques being developed to predict antigen specificity and mechanistic basis of TCR targeting. To this end, we have developed the TCR3d database, a resource containing all known TCR structures, with a particular focus on antigen recognition. TCR3d provides key information on antigen binding mode, interface features, loop sequences and germline gene usage. Users can interactively view TCR complex structures, search sequences of interest against known structures and sequences, and download curated datasets of structurally characterized TCR complexes. This database is updated on a weekly basis, and can serve the community as a centralized resource for those studying T cell receptors and their recognition. Availability and implementation The TCR3d database is available at https://tcr3d.ibbr.umd.edu/.
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Affiliation(s)
- Ragul Gowthaman
- 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.,University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, 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.,University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA
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29
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Uncovering the Tumor Antigen Landscape: What to Know about the Discovery Process. Cancers (Basel) 2020; 12:cancers12061660. [PMID: 32585818 PMCID: PMC7352969 DOI: 10.3390/cancers12061660] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/11/2020] [Accepted: 06/20/2020] [Indexed: 12/14/2022] Open
Abstract
According to the latest available data, cancer is the second leading cause of death, highlighting the need for novel cancer therapeutic approaches. In this context, immunotherapy is emerging as a reliable first-line treatment for many cancers, particularly metastatic melanoma. Indeed, cancer immunotherapy has attracted great interest following the recent clinical approval of antibodies targeting immune checkpoint molecules, such as PD-1, PD-L1, and CTLA-4, that release the brakes of the immune system, thus reviving a field otherwise poorly explored. Cancer immunotherapy mainly relies on the generation and stimulation of cytotoxic CD8 T lymphocytes (CTLs) within the tumor microenvironment (TME), priming T cells and establishing efficient and durable anti-tumor immunity. Therefore, there is a clear need to define and identify immunogenic T cell epitopes to use in therapeutic cancer vaccines. Naturally presented antigens in the human leucocyte antigen-1 (HLA-I) complex on the tumor surface are the main protagonists in evocating a specific anti-tumor CD8+ T cell response. However, the methodologies for their identification have been a major bottleneck for their reliable characterization. Consequently, the field of antigen discovery has yet to improve. The current review is intended to define what are today known as tumor antigens, with a main focus on CTL antigenic peptides. We also review the techniques developed and employed to date for antigen discovery, exploring both the direct elution of HLA-I peptides and the in silico prediction of epitopes. Finally, the last part of the review analyses the future challenges and direction of the antigen discovery field.
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30
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High-affinity oligoclonal TCRs define effective adoptive T cell therapy targeting mutant KRAS-G12D. Proc Natl Acad Sci U S A 2020; 117:12826-12835. [PMID: 32461371 DOI: 10.1073/pnas.1921964117] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Complete cancer regression occurs in a subset of patients following adoptive T cell therapy (ACT) of ex vivo expanded tumor-infiltrating lymphocytes (TILs). However, the low success rate presents a great challenge to broader clinical application. To provide insight into TIL-based immunotherapy, we studied a successful case of ACT where regression was observed against tumors carrying the hotspot mutation G12D in the KRAS oncogene. Four T cell receptors (TCRs) made up the TIL infusion and recognized two KRAS-G12D neoantigens, a nonamer and a decamer, all restricted by human leukocyte antigen (HLA) C*08:02. Three of them (TCR9a, 9b, and 9c) were nonamer-specific, while one was decamer-specific (TCR10). We show that only mutant G12D but not the wild-type peptides stabilized HLA-C*08:02 due to the formation of a critical anchor salt bridge to HLA-C. Therapeutic TCRs exhibited high affinities, ranging from nanomolar to low micromolar. Intriguingly, TCR binding affinities to HLA-C inversely correlated with their persistence in vivo, suggesting the importance of antigenic affinity in the function of therapeutic T cells. Crystal structures of TCR-HLA-C complexes revealed that TCR9a to 9c recognized G12D nonamer with multiple conserved contacts through shared CDR2β and CDR3α. This allowed CDR3β variation to confer different affinities via a variable HLA-C contact, generating an oligoclonal response. TCR10 recognized an induced and distinct G12D decamer conformation. Thus, this successful case of ACT included oligoclonal TCRs of high affinity recognizing distinct conformations of neoantigens. Our study revealed the potential of a structural approach to inform clinical efforts in targeting KRAS-G12D tumors by immunotherapy and has general implications for T cell-based immunotherapies.
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31
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Hoffmann MM, Slansky JE. T-cell receptor affinity in the age of cancer immunotherapy. Mol Carcinog 2020; 59:862-870. [PMID: 32386086 DOI: 10.1002/mc.23212] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/30/2020] [Accepted: 04/30/2020] [Indexed: 12/13/2022]
Abstract
The strength of the interaction between T-cell receptors (TCRs) and their ligands, peptide/major histocompatibility complex complexes (pMHCs), is one of the most frequently discussed and investigated features of T cells in immuno-oncology today. Although there are many molecules on the surface of T cells that interact with ligands on other cells, the TCR/pMHC is the only receptor-ligand pair that offers antigen specificity and dictates the functional response of the T cell. The strength of the TCR/pMHC interaction, along with the environment in which this interaction takes place, is key to how the T cell will respond. The TCR repertoire of T cells that interact with tumor-associated antigens is vast, although typically of low affinity. Here, we focus on the low-affinity interactions between TCRs from CD8+ T cells and different models used in immuno-oncology.
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Affiliation(s)
- Michele M Hoffmann
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jill E Slansky
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, Colorado
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32
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Nerli S, Sgourakis NG. Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 32511353 DOI: 10.1101/2020.03.23.004176] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
As a first step toward the development of diagnostic and therapeutic tools to fight the Coronavirus disease (COVID-19), it is important to characterize CD8+ T cell epitopes in the SARS-CoV-2 peptidome that can trigger adaptive immune responses. Here, we use RosettaMHC, a comparative modeling approach which leverages existing high-resolution X-ray structures from peptide/MHC complexes available in the Protein Data Bank, to derive physically realistic 3D models for high-affinity SARS-CoV-2 epitopes. We outline an application of our method to model 439 9mer and 279 10mer predicted epitopes displayed by the common allele HLA-A*02:01, and we make our models publicly available through an online database ( https://rosettamhc.chemistry.ucsc.edu ). As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models of antigens from different strains and HLA alleles can be used as a basis to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.
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33
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Singh NK, Abualrous ET, Ayres CM, Noé F, Gowthaman R, Pierce BG, Baker BM. Geometrical characterization of T cell receptor binding modes reveals class-specific binding to maximize access to antigen. Proteins 2020; 88:503-513. [PMID: 31589793 PMCID: PMC6982585 DOI: 10.1002/prot.25829] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 08/08/2019] [Accepted: 09/17/2019] [Indexed: 11/11/2022]
Abstract
Recognition of antigenic peptides bound to major histocompatibility complex (MHC) proteins by αβ T cell receptors (TCRs) is a hallmark of T cell mediated immunity. Recent data suggest that variations in TCR binding geometry may influence T cell signaling, which could help explain outliers in relationships between physical parameters such as TCR-pMHC binding affinity and T cell function. Traditionally, TCR binding geometry has been described with simple descriptors such as the crossing angle, which quantifies what has become known as the TCR's diagonal binding mode. However, these descriptors often fail to reveal distinctions in binding geometry that are apparent through visual inspection. To provide a better framework for relating TCR structure to T cell function, we developed a comprehensive system for quantifying the geometries of how TCRs bind peptide/MHC complexes. We show that our system can discern differences not clearly revealed by more common methods. As an example of its potential to impact biology, we used it to reveal differences in how TCRs bind class I and class II peptide/MHC complexes, which we show allow the TCR to maximize access to and "read out" the peptide antigen. We anticipate our system will be of use in not only exploring these and other details of TCR-peptide/MHC binding interactions, but also addressing questions about how TCR binding geometry relates to T cell function, as well as modeling structural properties of class I and class II TCR-peptide/MHC complexes from sequence information. The system is available at https://tcr3d.ibbr.umd.edu/tcr_com or for download as a script.
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MESH Headings
- Binding Sites
- Crystallography, X-Ray
- Histocompatibility Antigens Class I/chemistry
- Histocompatibility Antigens Class I/immunology
- Histocompatibility Antigens Class I/metabolism
- Histocompatibility Antigens Class II/chemistry
- Histocompatibility Antigens Class II/immunology
- Histocompatibility Antigens Class II/metabolism
- Humans
- Models, Molecular
- Principal Component Analysis
- Protein Binding
- Protein Conformation, alpha-Helical
- Protein Conformation, beta-Strand
- Protein Interaction Domains and Motifs
- Receptors, Antigen, T-Cell, alpha-beta/chemistry
- Receptors, Antigen, T-Cell, alpha-beta/immunology
- Receptors, Antigen, T-Cell, alpha-beta/metabolism
- T-Lymphocytes/chemistry
- T-Lymphocytes/immunology
- T-Lymphocytes/metabolism
- Thermodynamics
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Affiliation(s)
- Nishant K. Singh
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, United States
- Harper Cancer Research Institute, University of Notre Dame, South Bend, IN, United States
| | - Esam T. Abualrous
- Molecular Biology Group, Institute for Mathematics, Freie Universität Berlin, Berlin, Germany
| | - Cory M. Ayres
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, United States
- Harper Cancer Research Institute, University of Notre Dame, South Bend, IN, United States
| | - Frank Noé
- Molecular Biology Group, Institute for Mathematics, Freie Universität Berlin, Berlin, Germany
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Brian M. Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, United States
- Harper Cancer Research Institute, University of Notre Dame, South Bend, IN, United States
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34
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Minervina AA, Pogorelyy MV, Komech EA, Karnaukhov VK, Bacher P, Rosati E, Franke A, Chudakov DM, Mamedov IZ, Lebedev YB, Mora T, Walczak AM. Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones. eLife 2020; 9:53704. [PMID: 32081129 PMCID: PMC7060039 DOI: 10.7554/elife.53704] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/21/2020] [Indexed: 11/16/2022] Open
Abstract
The diverse repertoire of T-cell receptors (TCR) plays a key role in the adaptive immune response to infections. Using TCR alpha and beta repertoire sequencing for T-cell subsets, as well as single-cell RNAseq and TCRseq, we track the concentrations and phenotypes of individual T-cell clones in response to primary and secondary yellow fever immunization — the model for acute infection in humans — showing their large diversity. We confirm the secondary response is an order of magnitude weaker, albeit ∼10 days faster than the primary one. Estimating the fraction of the T-cell response directed against the single immunodominant epitope, we identify the sequence features of TCRs that define the high precursor frequency of the two major TCR motifs specific for this particular epitope. We also show the consistency of clonal expansion dynamics between bulk alpha and beta repertoires, using a new methodology to reconstruct alpha-beta pairings from clonal trajectories.
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Affiliation(s)
| | - Mikhail V Pogorelyy
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation.,Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Ekaterina A Komech
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation.,Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | | | - Petra Bacher
- Institute of Immunology, Kiel University, Kiel, Germany
| | - Elisa Rosati
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Dmitriy M Chudakov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation.,Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation.,Center of Life Sciences, Skoltech, Moscow, Russian Federation.,Masaryk University, Central European Institute of Technology, Brno, Czech Republic
| | - Ilgar Z Mamedov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation.,Masaryk University, Central European Institute of Technology, Brno, Czech Republic.,V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Moscow, Russian Federation
| | - Yuri B Lebedev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation.,Moscow State University, Moscow, Russian Federation
| | - Thierry Mora
- Laboratoire de physique de l'École normale supérieure, ENS, PSL, Sorbonne Université, Université de Paris, and CNRS, Paris, France
| | - Aleksandra M Walczak
- Laboratoire de physique de l'École normale supérieure, ENS, PSL, Sorbonne Université, Université de Paris, and CNRS, Paris, France
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35
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Vianna P, Mendes MF, Bragatte MA, Ferreira PS, Salzano FM, Bonamino MH, Vieira GF. pMHC Structural Comparisons as a Pivotal Element to Detect and Validate T-Cell Targets for Vaccine Development and Immunotherapy-A New Methodological Proposal. Cells 2019; 8:cells8121488. [PMID: 31766602 PMCID: PMC6952977 DOI: 10.3390/cells8121488] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/15/2019] [Accepted: 11/16/2019] [Indexed: 12/02/2022] Open
Abstract
The search for epitopes that will effectively trigger an immune response remains the “El Dorado” for immunologists. The development of promising immunotherapeutic approaches requires the appropriate targets to elicit a proper immune response. Considering the high degree of HLA/TCR diversity, as well as the heterogeneity of viral and tumor proteins, this number will invariably be higher than ideal to test. It is known that the recognition of a peptide-MHC (pMHC) by the T-cell receptor is performed entirely in a structural fashion, where the atomic interactions of both structures, pMHC and TCR, dictate the fate of the process. However, epitopes with a similar composition of amino acids can produce dissimilar surfaces. Conversely, sequences with no conspicuous similarities can exhibit similar TCR interaction surfaces. In the last decade, our group developed a database and in silico structural methods to extract molecular fingerprints that trigger T-cell immune responses, mainly referring to physicochemical similarities, which could explain the immunogenic differences presented by different pMHC-I complexes. Here, we propose an immunoinformatic approach that considers a structural level of information, combined with an experimental technology that simulates the presentation of epitopes for a T cell, to improve vaccine production and immunotherapy efficacy.
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Affiliation(s)
- Priscila Vianna
- Laboratory of Human Teratogenesis and Population Medical Genetics, Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil;
| | - Marcus F.A. Mendes
- Laboratory of Bioinformatics (NBLI), Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil (M.A.B.)
| | - Marcelo A. Bragatte
- Laboratory of Bioinformatics (NBLI), Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil (M.A.B.)
| | - Priscila S. Ferreira
- Program of Immunology and Tumor Biology, Division of Experimental and Translational Research, Brazilian National Cancer Institute, Rio de Janeiro 20231-050, Brazil; (P.S.F.); (M.H.B.)
| | - Francisco M. Salzano
- Laboratory of Molecular Evolution, Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil;
| | - Martin H. Bonamino
- Program of Immunology and Tumor Biology, Division of Experimental and Translational Research, Brazilian National Cancer Institute, Rio de Janeiro 20231-050, Brazil; (P.S.F.); (M.H.B.)
- Vice Presidency of Research and Biological Collections, Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil
| | - Gustavo F. Vieira
- Laboratory of Bioinformatics (NBLI), Department of Genetics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91.501-970, Brazil (M.A.B.)
- Laboratory of Health Bioinformatics, Post Graduate Program in Health and Human Development, La Salle University, Canoas 91.501-970, Brazil
- Correspondence: ; Tel.: +55-51-3308-99-38; Fax: +55-51-3308-73-11
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36
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Zvyagin IV, Tsvetkov VO, Chudakov DM, Shugay M. An overview of immunoinformatics approaches and databases linking T cell receptor repertoires to their antigen specificity. Immunogenetics 2019; 72:77-84. [PMID: 31741011 DOI: 10.1007/s00251-019-01139-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 10/16/2019] [Indexed: 11/26/2022]
Abstract
Recent advances in molecular and bioinformatic methods have greatly improved our ability to study the formation of an adaptive immune response towards foreign pathogens, self-antigens, and cancer neoantigens. T cell receptors (TCR) are the key players in this process that recognize peptides presented by major histocompatibility complex (MHC). Owing to the huge diversity of both TCR sequence variants and peptides they recognize, accumulation and complex analysis of large amounts of TCR-antigen specificity data is required for understanding the structure and features of adaptive immune responses towards pathogens, vaccines, cancer, as well as autoimmune responses. In the present review, we summarize recent efforts on gathering and interpreting TCR-antigen specificity data and outline the critical role of tighter integration with other immunoinformatics data sources that include epitope MHC restriction, TCR repertoire structure models, and TCR/peptide/MHC structural data. We suggest that such integration can lead to the ability to accurately annotate individual TCR repertoires, efficiently estimate epitope and neoantigen immunogenicity, and ultimately, in silico identify TCRs specific to yet unstudied antigens and predict self-peptides related to autoimmunity.
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Affiliation(s)
- Ivan V Zvyagin
- Pirogov Russian Medical State University, Moscow, Russia
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Vasily O Tsvetkov
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Dmitry M Chudakov
- Pirogov Russian Medical State University, Moscow, Russia
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Mikhail Shugay
- Pirogov Russian Medical State University, Moscow, Russia.
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.
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37
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Xu Y, Morales AJ, Cargill MJ, Towlerton AMH, Coffey DG, Warren EH, Tykodi SS. Preclinical development of T-cell receptor-engineered T-cell therapy targeting the 5T4 tumor antigen on renal cell carcinoma. Cancer Immunol Immunother 2019; 68:1979-1993. [PMID: 31686124 PMCID: PMC6877496 DOI: 10.1007/s00262-019-02419-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 10/18/2019] [Indexed: 12/19/2022]
Abstract
5T4 (trophoblast glycoprotein, TPBG) is a transmembrane tumor antigen expressed on more than 90% of primary renal cell carcinomas (RCC) and a wide range of human carcinomas but not on most somatic adult tissues. The favorable expression pattern has encouraged the development and clinical testing of 5T4-targeted antibody and vaccine therapies. 5T4 also represents a compelling and unexplored target for T-cell receptor (TCR)-engineered T-cell therapy. Our group has previously isolated high-avidity CD8+ T-cell clones specific for an HLA-A2-restricted 5T4 epitope (residues 17-25; 5T4p17). In this report, targeted single-cell RNA sequencing was performed on 5T4p17-specific T-cell clones to sequence the highly variable complementarity-determining region 3 (CDR3) of T-cell receptor α chain (TRA) and β chain (TRB) genes. Full-length TRA and TRB sequences were cloned into lentiviral vectors and transduced into CD8+ T-cells from healthy donors. Redirected effector T-cell function against 5T4p17 was measured by cytotoxicity and cytokine release assays. Seven unique TRA-TRB pairs were identified. All seven TCRs exhibited high expression on CD8+ T-cells with transduction efficiencies from 59 to 89%. TCR-transduced CD8+ T-cells demonstrated redirected cytotoxicity and cytokine release in response to 5T4p17 on target-cells and killed 5T4+/HLA-A2+ kidney-, breast-, and colorectal-tumor cell lines as well as primary RCC tumor cells in vitro. TCR-transduced CD8+ T-cells also detected presentation of 5T4p17 in TAP1/2-deficient T2 target-cells. TCR-transduced T-cells redirected to recognize the 5T4p17 epitope from a broadly shared tumor antigen are of interest for future testing as a cellular immunotherapy strategy for HLA-A2+ subjects with 5T4+ tumors.
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Affiliation(s)
- Yuexin Xu
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
| | - Alicia J Morales
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Michael J Cargill
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Andrea M H Towlerton
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - David G Coffey
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Edus H Warren
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA.,Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Scott S Tykodi
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
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38
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Karapetyan AR, Chaipan C, Winkelbach K, Wimberger S, Jeong JS, Joshi B, Stein RB, Underwood D, Castle JC, van Dijk M, Seibert V. TCR Fingerprinting and Off-Target Peptide Identification. Front Immunol 2019; 10:2501. [PMID: 31695703 PMCID: PMC6817589 DOI: 10.3389/fimmu.2019.02501] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 10/07/2019] [Indexed: 01/06/2023] Open
Abstract
Adoptive T cell therapy using patient T cells redirected to recognize tumor-specific antigens by expressing genetically engineered high-affinity T-cell receptors (TCRs) has therapeutic potential for melanoma and other solid tumors. Clinical trials implementing genetically modified TCRs in melanoma patients have raised concerns regarding off-target toxicities resulting in lethal destruction of healthy tissue, highlighting the urgency of assessing which off-target peptides can be recognized by a TCR. As a model system we used the clinically efficacious NY-ESO-1-specific TCR C259, which recognizes the peptide epitope SLLMWITQC presented by HLA-A*02:01. We investigated which amino acids at each position enable a TCR interaction by sequentially replacing every amino acid position outside of anchor positions 2 and 9 with all 19 possible alternative amino acids, resulting in 134 peptides (133 altered peptides plus epitope peptide). Each peptide was individually evaluated using three different in vitro assays: binding of the NY-ESOc259 TCR to the peptide, peptide-dependent activation of TCR-expressing cells, and killing of peptide-presenting target cells. To represent the TCR recognition kernel, we defined Position Weight Matrices (PWMs) for each assay by assigning normalized measurements to each of the 20 amino acids in each position. To predict potential off-target peptides, we applied a novel algorithm projecting the PWM-defined kernel into the human proteome, scoring NY-ESOc259 TCR recognition of 336,921 predicted human HLA-A*02:01 binding 9-mer peptides. Of the 12 peptides with high predicted score, we confirmed 7 (including NY-ESO-1 antigen SLLMWITQC) strongly activate human primary NY-ESOc259-expressing T cells. These off-target peptides include peptides with up to 7 amino acid changes (of 9 possible), which could not be predicted using the recognition motif as determined by alanine scans. Thus, this replacement scan assay determines the “TCR fingerprint” and, when coupled with the algorithm applied to the database of human 9-mer peptides binding to HLA-A*02:01, enables the identification of potential off-target antigens and the tissues where they are expressed. This platform enables both screening of multiple TCRs to identify the best candidate for clinical development and identification of TCR-specific cross-reactive peptide recognition and constitutes an improved methodology for the identification of potential off-target peptides presented on MHC class I molecules.
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39
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Leem J, de Oliveira SHP, Krawczyk K, Deane CM. STCRDab: the structural T-cell receptor database. Nucleic Acids Res 2019; 46:D406-D412. [PMID: 29087479 PMCID: PMC5753249 DOI: 10.1093/nar/gkx971] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 10/09/2017] [Indexed: 01/16/2023] Open
Abstract
The Structural T–cell Receptor Database (STCRDab; http://opig.stats.ox.ac.uk/webapps/stcrdab) is an online resource that automatically collects and curates TCR structural data from the Protein Data Bank. For each entry, the database provides annotations, such as the α/β or γ/δ chain pairings, major histocompatibility complex details, and where available, antigen binding affinities. In addition, the orientation between the variable domains and the canonical forms of the complementarity-determining region loops are also provided. Users can select, view, and download individual or bulk sets of structures based on these criteria. Where available, STCRDab also finds antibody structures that are similar to TCRs, helping users explore the relationship between TCRs and antibodies.
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Affiliation(s)
- Jinwoo Leem
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
| | | | - Konrad Krawczyk
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
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40
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Bioinformatic methods for cancer neoantigen prediction. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 164:25-60. [PMID: 31383407 DOI: 10.1016/bs.pmbts.2019.06.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.
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41
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Ogishi M, Yotsuyanagi H. Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space. Front Immunol 2019; 10:827. [PMID: 31057550 PMCID: PMC6477061 DOI: 10.3389/fimmu.2019.00827] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 03/28/2019] [Indexed: 01/02/2023] Open
Abstract
Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this “convergence” of adaptive immunity among different individuals remain poorly understood. To quantitatively describe epitope immunogenicity, here we propose a supervised machine learning framework generating probabilistic estimates of immunogenicity, termed “immunogenicity scores,” based on the numerical features computed through sequence-based simulation approximating the molecular scanning process of peptides presented onto major histocompatibility complex (MHC) by the human T cell receptor (TCR) repertoire. Notably, overlapping sets of intermolecular interaction parameters were commonly utilized in MHC-I and MHC-II prediction. Moreover, a similar simulation of individual TCR-peptide interaction using the same set of interaction parameters yielded correlates of TCR affinity. Pathogen-derived epitopes and tumor-associated epitopes with positive T cell reactivity generally had higher immunogenicity scores than non-immunogenic counterparts, whereas thymically expressed self-epitopes were assigned relatively low scores regardless of their immunogenicity annotation. Immunogenicity score dynamics among single amino acid mutants delineated the landscape of position- and residue-specific mutational impacts. Simulation of position-specific immunogenicity score dynamics detected residues with high escape potential in multiple epitopes, consistent with known escape mutations in the literature. This study indicates that targeting of epitopes by human adaptive immunity is to some extent directed by defined thermodynamic principles. The proposed framework also has a practical implication in that it may enable to more efficiently prioritize epitope candidates highly prone to T cell recognition in multiple individuals, warranting prospective validation across different cohorts.
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Affiliation(s)
- Masato Ogishi
- Division of Infectious Diseases and Applied Immunology, The Institute of Medical Sciences Research Hospital, The University of Tokyo, Tokyo, Japan
| | - Hiroshi Yotsuyanagi
- Division of Infectious Diseases and Applied Immunology, The Institute of Medical Sciences Research Hospital, The University of Tokyo, Tokyo, Japan
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42
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Geng C, Xue LC, Roel‐Touris J, Bonvin AMJJ. Finding the ΔΔ
G
spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1410] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Li C. Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Jorge Roel‐Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
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43
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Mahajan S, Vita R, Shackelford D, Lane J, Schulten V, Zarebski L, Jespersen MC, Marcatili P, Nielsen M, Sette A, Peters B. Epitope Specific Antibodies and T Cell Receptors in the Immune Epitope Database. Front Immunol 2018; 9:2688. [PMID: 30515166 PMCID: PMC6255941 DOI: 10.3389/fimmu.2018.02688] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 10/31/2018] [Indexed: 11/13/2022] Open
Abstract
The Immune Epitope Database (IEDB) is a free public resource which catalogs experiments characterizing immune epitopes. To accommodate data from next generation repertoire sequencing experiments, we recently updated how we capture and query epitope specific antibodies and T cell receptors. Specifically, we are now storing partial receptor sequences sufficient to determine CDRs and VDJ gene usage which are commonly identified by repertoire sequencing. For previously captured full length receptor sequencing data, we have calculated the corresponding CDR sequences and gene usage information using IMGT numbering and VDJ gene nomenclature format. To integrate information from receptors defined at different levels of resolution, we grouped receptors based on their host species, receptor type and CDR3 sequence. As of August 2018, we have cataloged sequence information for more than 22,510 receptors in 18,292 receptor groups, shown to bind to more than 2,241 distinct epitopes. These data are accessible as full exports and through a new dedicated query interface. The later combines the new ability to search by receptor characteristics with previously existing capability to search by epitope characteristics such as the infectious agent the epitope is derived from, or the kind of immune response involved in its recognition. We expect that this comprehensive capture of epitope specific immune receptor information will provide new insights into receptor-epitope interactions, and facilitate the development of novel tools that help in the analysis of receptor repertoire data.
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Affiliation(s)
- Swapnil Mahajan
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Randi Vita
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Deborah Shackelford
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Jerome Lane
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Veronique Schulten
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Laura Zarebski
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Alessandro Sette
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,University of California San Diego, La Jolla, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,University of California San Diego, La Jolla, CA, United States
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44
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Venturi V, Thomas PG. The expanding role of systems immunology in decoding the T cell receptor repertoire. ACTA ACUST UNITED AC 2018; 12:37-45. [PMID: 31106281 DOI: 10.1016/j.coisb.2018.09.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
T cells play a crucial role in the immune system's defense against many infectious diseases, including persistent infections for which no effective vaccines currently exist. The T cell component of the adaptive immune system is highly complex involving a constantly evolving landscape of various inter-related T cell populations. These T cell populations are characterized by their phenotypic and functional properties as well as the collection, or repertoire, of T cell receptors (TCR) that mediate T cell recognition of antigenic peptides derived from pathogens. Understanding the various processes and factors that impact the development and evolution of the broader T cell repertoire available to recognize and respond to pathogens and the characteristics of antigen-experienced T cell repertoires associated with effective immune control of pathogens is critical to the rational design of T cell-based vaccines and therapies. In this article we discuss, using examples of recent research, the promise that systems immunology approaches, involving quantitative analysis and mathematical and computational modeling of immunological data, hold for decoding the complex TCR repertoire system in the current era of advancing technologies.
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Affiliation(s)
- Vanessa Venturi
- Infection Analytics Program, Kirby Institute for Infection and Immunity, UNSW Australia, Sydney, NSW, Australia
| | - Paul G Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
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45
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Antunes DA, Devaurs D, Moll M, Lizée G, Kavraki LE. General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept. Sci Rep 2018. [PMID: 29531253 PMCID: PMC5847594 DOI: 10.1038/s41598-018-22173-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.
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Affiliation(s)
- Dinler A Antunes
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Didier Devaurs
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Mark Moll
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Gregory Lizée
- Department of Melanoma Medical Oncology - Research, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston, TX, 77005, USA.
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46
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Singh NK, Riley TP, Baker SCB, Borrman T, Weng Z, Baker BM. Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes. THE JOURNAL OF IMMUNOLOGY 2017; 199:2203-2213. [PMID: 28923982 DOI: 10.4049/jimmunol.1700744] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 07/10/2017] [Indexed: 12/14/2022]
Abstract
T cell specificity emerges from a myriad of processes, ranging from the biological pathways that control T cell signaling to the structural and physical mechanisms that influence how TCRs bind peptides and MHC proteins. Of these processes, the binding specificity of the TCR is a key component. However, TCR specificity is enigmatic: TCRs are at once specific but also cross-reactive. Although long appreciated, this duality continues to puzzle immunologists and has implications for the development of TCR-based therapeutics. In this review, we discuss TCR specificity, emphasizing results that have emerged from structural and physical studies of TCR binding. We show how the TCR specificity/cross-reactivity duality can be rationalized from structural and biophysical principles. There is excellent agreement between predictions from these principles and classic predictions about the scope of TCR cross-reactivity. We demonstrate how these same principles can also explain amino acid preferences in immunogenic epitopes and highlight opportunities for structural considerations in predictive immunology.
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Affiliation(s)
- Nishant K Singh
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556.,Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556; and
| | - Timothy P Riley
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556.,Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556; and
| | - Sarah Catherine B Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556.,Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556; and
| | - Tyler Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605
| | - Brian M Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556; .,Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556; and
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47
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Antunes DA, Rigo MM, Freitas MV, Mendes MFA, Sinigaglia M, Lizée G, Kavraki LE, Selin LK, Cornberg M, Vieira GF. Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy. Front Immunol 2017; 8:1210. [PMID: 29046675 PMCID: PMC5632759 DOI: 10.3389/fimmu.2017.01210] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 09/12/2017] [Indexed: 12/16/2022] Open
Abstract
Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient’s own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide–ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide–MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC “hot-spots” for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.
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Affiliation(s)
- Dinler A Antunes
- Núcleo de Bioinformática do Laboratório de Imunogenética (NBLI), Department of Genetics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.,Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Maurício M Rigo
- Núcleo de Bioinformática do Laboratório de Imunogenética (NBLI), Department of Genetics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.,Laboratório de Imunologia Celular e Molecular, Instituto de Pesquisas Biomédicas (IPB), Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Martiela V Freitas
- Núcleo de Bioinformática do Laboratório de Imunogenética (NBLI), Department of Genetics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Marcus F A Mendes
- Núcleo de Bioinformática do Laboratório de Imunogenética (NBLI), Department of Genetics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Marialva Sinigaglia
- Núcleo de Bioinformática do Laboratório de Imunogenética (NBLI), Department of Genetics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Gregory Lizée
- Lizée Lab, Department of Melanoma Medical Oncology - Research, The University of Texas M. D. Anderson Cancer Center, Houston, TX, United States
| | - Lydia E Kavraki
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Liisa K Selin
- Selin Lab, Department of Pathology, University of Massachusetts Medical School, Worcester, MA, United States
| | - Markus Cornberg
- Cornberg Lab, Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany.,German Center for Infection Research (DZIF), Partner-Site Hannover-Braunschweig, Hannover, Germany
| | - Gustavo F Vieira
- Núcleo de Bioinformática do Laboratório de Imunogenética (NBLI), Department of Genetics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.,Programa de Pós-Graduação em Saúde e Desenvolvimento Humano, Universidade La Salle, Porto Alegre, Brazil
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