T cell receptor sequence clustering and antigen specificity

Vujović, Milena; Degn, Kristine; Marin, Frederikke Isa; Schaap‐Johansen, Anna‐Lisa; Chain, Benny; Andresen, Thomas Lars; Kaplinsky, Joseph; Marcatili, Paolo

doi:10.1016/j.csbj.2020.06.041

Cited by 16 publications

(12 citation statements)

References 51 publications

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“…A number of algorithms to cluster or classify TCR clonotypes have been developed. Each algorithm has advantages and disadvantages as reviewed by others ( 115 , 116 ), but in respect to TCR biomarker development for T1D, the algorithms can be divided to two groups. First, those that clusters TCRs by assessing similarities of TCR sequences with each other in datasets.…”

Section: Use Of Tcr Clonotypes As Surrogates To Quantify Antigen-specific T Cellsmentioning

confidence: 99%

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Nakayama

Michels

2021

Front. Immunol.

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T cell receptors (TCRs) are unique markers that define antigen specificity for a given T cell. With the evolution of sequencing and computational analysis technologies, TCRs are now prime candidates for the development of next-generation non-cell based T cell biomarkers, which provide a surrogate measure to assess the presence of antigen-specific T cells. Type 1 diabetes (T1D), the immune-mediated form of diabetes, is a prototypical organ specific autoimmune disease in which T cells play a pivotal role in targeting pancreatic insulin-producing beta cells. While the disease is now predictable by measuring autoantibodies in the peripheral blood directed to beta cell proteins, there is an urgent need to develop T cell markers that recapitulate T cell activity in the pancreas and can be a measure of disease activity. This review focuses on the potential and challenges of developing TCR biomarkers for T1D. We summarize current knowledge about TCR repertoires and clonotypes specific for T1D and discuss challenges that are unique for autoimmune diabetes. Ultimately, the integration of large TCR datasets produced from individuals with and without T1D along with computational ‘big data’ analysis will facilitate the development of TCRs as potentially powerful biomarkers in the development of T1D.

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Section: Use Of Tcr Clonotypes As Surrogates To Quantify Antigen-specific T Cellsmentioning

confidence: 99%

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Nakayama

Michels

2021

Front. Immunol.

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“…Multiple sequence alignment and sequence clustering methods are widely used to understand TCR specificity 68 . In order to probe characteristic sequence features of TCRs bound to nonameric peptides presented by MHC, we analyzed the variable domain ( V α, V β) sequences of 46 unique TCRs available from the structure.…”

Section: Resultsmentioning

confidence: 99%

“…67 Multiple sequence alignment and sequence clustering methods are widely used to understand TCR specificity. 68 In order to probe loops permits the TCR to approach close to the pMHC surface to attain a favorable binding conformation. [69][70][71] Also, a reduction in TCR-binding affinity has been observed upon mutation of the central glycine residues.…”

Section: Resultsmentioning

confidence: 99%

Structural patterns in class 1 major histocompatibility complex‐restricted nonamer peptide binding to T‐cell receptors

Rajeshwar

Smith

2022

Proteins

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The startling diversity in αβ T-cell receptor (TCR) sequences and structures complicates molecular-level analyses of the specificity and sensitivity determining T-cell immunogenicity. A number of three-dimensional (3D) structures are now available of ternary complexes between TCRs and peptides: major histocompatibility complexes (pMHC). Here, to glean molecular-level insights we analyze structures of TCRs bound to human class I nonamer peptide-MHC complexes. Residues at peptide positions 4-8 are found to be particularly important for TCR binding. About 90% of the TCRs hydrogen bond with one or both of the peptide residues at positions 4 and 8 presented by MHC allele HLA-A2, and this number is still ~79% for peptides presented by other MHC alleles. Residue 8, which lies outside the previously-identified central peptide region, is crucial for TCR recognition of class I MHC-presented nonamer peptides. The statistics of the interactions also sheds light on the MHC residues important for TCR binding. The present analysis will aid in the structural modeling of TCR: pMHC complexes and has implications for the rational design of peptide-based vaccines and T-cell-based immunotherapies.

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“…The current version of immuneML includes a set of components mainly focused on supervised ML, but the platform is also suitable for the community to extend it with components for settings such as unsupervised learning 67 or generative receptor modeling 14 , 19 , 68 . We also aim to improve the general support for model introspection, in particular in the direction of supporting causal interpretations for discovering and alleviating technical biases or challenges related to the study design 69 .…”

Section: Discussionmentioning

confidence: 99%

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Pavlović

Scheffer

Motwani

et al. 2021

Preprint

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Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

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T cell receptor sequence clustering and antigen specificity

Cited by 16 publications

References 51 publications

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Structural patterns in class 1 major histocompatibility complex‐restricted nonamer peptide binding to T‐cell receptors

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

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