1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
Collapse
Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
| |
Collapse
|
3
|
Han Y, Yang Y, Tian Y, Fattah FJ, von Itzstein MS, Hu Y, Zhang M, Kang X, Yang DM, Liu J, Xue Y, Liang C, Raman I, Zhu C, Xiao O, Dowell JE, Homsi J, Rashdan S, Yang S, Gwin ME, Hsiehchen D, Gloria-McCutchen Y, Pan K, Wu F, Gibbons D, Wang X, Yee C, Huang J, Reuben A, Cheng C, Zhang J, Gerber DE, Wang T. pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569599. [PMID: 38105939 PMCID: PMC10723300 DOI: 10.1101/2023.12.01.569599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.
Collapse
|
4
|
Boughter CT, Meier-Schellersheim M. An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator. PLoS Comput Biol 2023; 19:e1011577. [PMID: 37862356 PMCID: PMC10619816 DOI: 10.1371/journal.pcbi.1011577] [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: 12/19/2022] [Revised: 11/01/2023] [Accepted: 10/06/2023] [Indexed: 10/22/2023] Open
Abstract
The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.
Collapse
Affiliation(s)
- Christopher T. Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| |
Collapse
|
5
|
Bravi B, Di Gioacchino A, Fernandez-de-Cossio-Diaz J, Walczak AM, Mora T, Cocco S, Monasson R. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife 2023; 12:e85126. [PMID: 37681658 PMCID: PMC10522340 DOI: 10.7554/elife.85126] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 09/07/2023] [Indexed: 09/09/2023] Open
Abstract
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
Collapse
Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Andrea Di Gioacchino
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Jorge Fernandez-de-Cossio-Diaz
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Aleksandra M Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Simona Cocco
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| |
Collapse
|
6
|
Zhang Y, Jian X, Xu L, Zhao J, Lu M, Lin Y, Xie L. iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features. Front Genet 2023; 14:1141535. [PMID: 37229205 PMCID: PMC10203616 DOI: 10.3389/fgene.2023.1141535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.
Collapse
Affiliation(s)
- Yu Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Bio-Diversity Science, School of Life Sciences, Fudan University, Shanghai, China
| | - Jingjing Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Manman Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yong Lin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| |
Collapse
|
7
|
Xu AM, Chour W, DeLucia DC, Su Y, Pavlovitch-Bedzyk AJ, Ng R, Rasheed Y, Davis MM, Lee JK, Heath JR. Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities. Cell Syst 2023; 14:273-284.e5. [PMID: 37001518 PMCID: PMC10355346 DOI: 10.1016/j.cels.2023.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 09/01/2022] [Accepted: 03/01/2023] [Indexed: 04/22/2023]
Abstract
Antigen-specific T cell receptor (TCR) sequences can have prognostic, predictive, and therapeutic value, but decoding the specificity of TCR recognition remains challenging. Unlike DNA strands that base pair, TCRs bind to their targets with different orientations and different lengths, which complicates comparisons. We present scanning parametrized by normalized TCR length (SPAN-TCR) to analyze antigen-specific TCR CDR3 sequences and identify patterns driving TCR-pMHC specificity. Using entropic analysis, SPAN-TCR identifies 2-mer motifs that decrease the diversity (entropy) of CDR3s. These motifs are the most common patterns that can predict CDR3 composition, and we identify "essential" motifs that decrease entropy in the same CDR3 α or β chain containing the 2-mer, and "super-essential" motifs that decrease entropy in both chains. Molecular dynamics analysis further suggests that these motifs may play important roles in binding. We then employ SPAN-TCR to resolve similarities in TCR repertoires against different antigens using public databases of TCR sequences.
Collapse
Affiliation(s)
- Alexander M Xu
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - William Chour
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 91125, USA
| | - Diana C DeLucia
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yapeng Su
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Rachel Ng
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Yusuf Rasheed
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Mark M Davis
- Computational and Systems Immunology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John K Lee
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - James R Heath
- Institute for Systems Biology, Seattle, WA 98109, USA.
| |
Collapse
|
8
|
Sanromán ÁF, Joshi K, Au L, Chain B, Turajlic S. TCR sequencing: applications in immuno-oncology research. IMMUNO-ONCOLOGY TECHNOLOGY 2023; 17:100373. [PMID: 36908996 PMCID: PMC9996383 DOI: 10.1016/j.iotech.2023.100373] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
•T-cell receptor (TCR) interaction with major histocompatibility complex-antigen complexes leads to antitumour responses.•TCR sequencing analysis allows characterisation of T cells that recognise tumour neoantigens.•T-cell clonal revival and clonal replacement potentially underpin immunotherapy responses.
Collapse
Affiliation(s)
- Á F Sanromán
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - K Joshi
- Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London, UK.,Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - L Au
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia.,Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Australia
| | - B Chain
- Division of Infection and Immunity, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - S Turajlic
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK.,Melanoma and Kidney Cancer Team, The Institute of Cancer Research, London, UK
| |
Collapse
|