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Measures of epitope binding degeneracy from T cell receptor repertoires. Proc Natl Acad Sci U S A 2023; 120:e2213264120. [PMID: 36649423 PMCID: PMC9942805 DOI: 10.1073/pnas.2213264120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Adaptive immunity is driven by specific binding of hypervariable receptors to diverse molecular targets. The sequence diversity of receptors and targets are both individually known but because multiple receptors can recognize the same target, a measure of the effective "functional" diversity of the human immune system has remained elusive. Here, we show that sequence near-coincidences within T cell receptors that bind specific epitopes provide a new window into this problem and allow the quantification of how binding probability covaries with sequence. We find that near-coincidence statistics within epitope-specific repertoires imply a measure of binding degeneracy to amino acid changes in receptor sequence that is consistent across disparate experiments. Paired data on both chains of the heterodimeric receptor are particularly revealing since simultaneous near-coincidences are rare and we show how they can be exploited to estimate the number of epitope responses that created the memory compartment. In addition, we find that paired-chain coincidences are strongly suppressed across donors with different human leukocyte antigens, evidence for a central role of antigen-driven selection in making paired chain receptors public. These results demonstrate the power of coincidence analysis to reveal the sequence determinants of epitope binding in receptor repertoires.
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Valkiers S, Gielis S, Van Deuren VML, Laukens K, Meysman P. Clustering and Annotation of T Cell Receptor Repertoires. Methods Mol Biol 2023; 2673:33-51. [PMID: 37258905 DOI: 10.1007/978-1-0716-3239-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Immunological protection against a wide variety of pathogens is largely mediated by the diverse and dynamic T cell receptor (TCR) repertoire, a crucial component of the adaptive immune system. An encounter with infectious agents stimulates specific T cells to initiate a direct immune response to combat intruders. Hence, the TCR repertoire may conceal crucial information regarding current and past infections and might assist in the development and monitoring of vaccines. To unlock its knowledge, we describe a computational workflow involving both supervised and unsupervised machine learning techniques to analyze and annotate full TCR repertoire data. The method is explained using data from a published yellow fever virus (YFV) vaccination study in healthy individuals. The TCR repertoire of one individual is studied before and 2 weeks after vaccination, using an efficient clustering method and identification of YFV-specific TCRs.
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Affiliation(s)
- Sebastiaan Valkiers
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium
| | - Sofie Gielis
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium
| | - Vincent M L Van Deuren
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium
| | - Pieter Meysman
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
- AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium.
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Ralph DK, Matsen FA. Inference of B cell clonal families using heavy/light chain pairing information. PLoS Comput Biol 2022; 18:e1010723. [PMID: 36441808 PMCID: PMC9731466 DOI: 10.1371/journal.pcbi.1010723] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 12/08/2022] [Accepted: 11/09/2022] [Indexed: 11/29/2022] Open
Abstract
Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package.
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Affiliation(s)
- Duncan K. Ralph
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- * E-mail:
| | - Frederick A. Matsen
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Department of Statistics, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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Isacchini G, Walczak AM, Mora T, Nourmohammad A. Deep generative selection models of T and B cell receptor repertoires with soNNia. Proc Natl Acad Sci U S A 2021; 118:e2023141118. [PMID: 33795515 PMCID: PMC8040596 DOI: 10.1073/pnas.2023141118] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire-level sequence information, we classify CD4+ and CD8+ T cells, find correlations between receptor chains arising during selection, and identify T cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.
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Affiliation(s)
- Giulio Isacchini
- Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany
- Laboratoire de Physique de l'Ecole Normale Supérieure, Paris Sciences & Lettres (PSL) University, CNRS, Sorbonne Université and Université de Paris, 75005 Paris, France
| | - Aleksandra M Walczak
- Laboratoire de Physique de l'Ecole Normale Supérieure, Paris Sciences & Lettres (PSL) University, CNRS, Sorbonne Université and Université de Paris, 75005 Paris, France;
| | - Thierry Mora
- Laboratoire de Physique de l'Ecole Normale Supérieure, Paris Sciences & Lettres (PSL) University, CNRS, Sorbonne Université and Université de Paris, 75005 Paris, France;
| | - Armita Nourmohammad
- Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany;
- Department of Physics, University of Washington, Seattle, WA 98195
- Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109
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Robinson RA, McMurran C, McCully ML, Cole DK. Engineering soluble T-cell receptors for therapy. FEBS J 2021; 288:6159-6173. [PMID: 33624424 PMCID: PMC8596704 DOI: 10.1111/febs.15780] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/11/2021] [Accepted: 02/22/2021] [Indexed: 12/15/2022]
Abstract
Immunotherapy approaches that target peptide-human leukocyte antigen (pHLA) complexes are becoming highly attractive because of their potential to access virtually all foreign and cellular proteins. For this reason, there has been considerable interest in the development of the natural ligand for pHLA, the T-cell receptor (TCR), as a soluble drug to target disease-associated pHLA presented at the cell surface. However, native TCR stability is suboptimal for soluble drug development, and natural TCRs generally have weak affinities for pHLAs, limiting their potential to reach efficacious receptor occupancy levels as soluble drugs. To overcome these limitations and make full use of the TCR as a soluble drug platform, several protein engineering solutions have been applied to TCRs to enhance both their stability and affinity, with a focus on retaining target specificity and selectivity. Here, we review these advances and look to the future for the next generation of soluble TCR-based therapies that can target monomorphic HLA-like proteins presenting both peptide and nonpeptide antigens.
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Souter MNT, Eckle SBG. Biased MAIT TCR Usage Poised for Limited Antigen Diversity? Front Immunol 2020; 11:1845. [PMID: 33013835 PMCID: PMC7461848 DOI: 10.3389/fimmu.2020.01845] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/09/2020] [Indexed: 12/20/2022] Open
Abstract
Mucosal-associated invariant T (MAIT) cells are a subset of unconventional T cells that recognize the evolutionarily conserved major histocompatibility complex (MHC) class I-like antigen-presenting molecule known as MHC class I related protein 1 (MR1). Since their rise from obscurity in the early 1990s, the study of MAIT cells has grown substantially, accelerating our fundamental understanding of these cells and their possible roles in immunity. In the context of recent advances, we review here the relationship between MR1, antigen, and TCR usage among MAIT and other MR1-reactive T cells and provide a speculative discussion.
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Affiliation(s)
- Michael N T Souter
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Sidonia B G Eckle
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
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