scholarly journals SwarmTCR: a computational approach to predict the specificity of T Cell Receptors

2020 ◽  
Author(s):  
Ryan Ehrlich ◽  
Larisa Kamga ◽  
Anna Gil ◽  
Katherine Luzuriaga ◽  
Liisa Selin ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptors ◽  
Nearest Neighbor ◽  
Sequence Data ◽  
Cell Receptor ◽  
Classification Performance ◽  
Sequencing Data ◽  
Cell Receptors ◽  
The Individual ◽  
Cell Data

AbstractMotivationComputationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to tackle this problem is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance.ResultsWe compared the performance of SwarmTCR against a state-of-the-art method (TCRdist) and showed that SwarmTCR performed significantly better on epitopes EBV-BRLF1300, EBV-BRLF1109, NS4B214–222 with single cell data and epitopes EBV-BRLF1300, EBV-BRLF1109, IAV-M158 with bulk sequencing data (α and β chains). In addition, we show that the weights returned by SwarmTCR are biologically interpretable.AvailabilitySwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR)[email protected]

scholarly journals SwarmTCR: a computational approach to predict the specificity of T cell receptors

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan Ehrlich ◽  
Larisa Kamga ◽  
Anna Gil ◽  
Katherine Luzuriaga ◽  
Liisa K. Selin ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
T Cell Receptors ◽  
Nearest Neighbor ◽  
Sequence Data ◽  
Cell Receptor ◽  
Classification Performance ◽  
Sequencing Data ◽  
Cell Receptors ◽  
The Individual

Abstract Background With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to predict T cell receptor specificity is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance. Results We compared the performance of SwarmTCR against another nearest-neighbor method and showed that SwarmTCR performs well both with bulk sequencing data and with single cell data. In addition, we show that the weights returned by SwarmTCR are biologically interpretable. Conclusions Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR).

scholarly journals On the feasibility of mining CD8+ T-cell receptor patterns underlying immunogenic peptide recognition

2017 ◽  
Author(s):  
Nicolas De Neuter ◽  
Wout Bittremieux ◽  
Charlie Beirnaert ◽  
Bart Cuypers ◽  
Aida Mrzic ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
T Cell Receptors ◽  
Sequence Data ◽  
Cell Epitope ◽  
Cell Receptor ◽  
T Cell Epitope ◽  
T Cell Epitopes ◽  
Cell Receptors ◽  
Mhc Molecules

Abstract:Current T-cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T-cell receptor is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T-cell and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of T-cell receptors that each bind to a known peptide and (2) retrieving T-cell receptors that bind to a given peptide from a large pool of T-cell receptors. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particularly importance as they show that prediction of T-cell epitope and T-cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T-cell epitopes but also paves the way for more general and high performing models.

Developing and validating an approach for diagnosing and prognosticating cancer from biochemical motifs in T-cell receptors.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15260-e15260
Author(s):  
Jared L Ostmeyer ◽  
Lindsay G Cowell ◽  
Scott Christley
Keyword(s):  
Amino Acid ◽  
T Cell ◽  
T Cell Receptor ◽  
T Cell Receptors ◽  
Ovarian Tissue ◽  
Cell Receptor ◽  
Healthy Tissue ◽  
Cervical Lesions ◽  
Cell Receptors ◽  
Biochemical Features

e15260 Background: Immune repertoire deep sequencing allows profiling T-cell populations and enables novel approaches to diagnose and prognosticate cancer by identifying T-cell receptor sequence patterns associated with clinical phenotypes and outcomes. Methods: Our goal is to develop a method to diagnose and prognosticate cancer using sequenced T-cell receptors. To determine how to profile the specificity of a T-cell receptor, we analyze 3D X-ray crystallographic structures of T-cell receptors bound to antigen. We observe a contiguous strip typically 4 amino acid residues in length from the complimentary determining region 3 (CDR3) lying in direct contact with the antigen. Based on this observation, we extract 4 residue long snippets from every receptor’s CDR3 and represent each snippet using biochemical features encoded by its amino acid sequence. The biochemical features are combined with information about the abundance of the snippet or the receptor and scored using a machine learning based approach. Each predictive model is fitted and validated under the requirement that at least one positively labelled snippet appears per tumor and no positively labelled snippets appear in healthy tissue. Results: Using a patient-holdout cross-validation, we fit predictive models to distinguish: 1. colorectal tumors from healthy tissue matched controls with 93% accuracy, 2. breast tumors from healthy tissue matched controls with 94% accuracy, 3. ovarian tumors from non-cancer patient ovarian tissue with 95% accuracy (80% accuracy on a blinded follow-up cohort) 4. and regression of preneoplastic cervical lesions over 1 year in advance with 96% accuracy. Conclusions: Immune repertoires can be used to diagnose and prognosticate cancer.

scholarly journals T Cell Receptor Transfection Shows Non-HLA-Restricted Recognition of Nickel by CD8+ Human T Cells to be Mediated by αβ T Cell Receptors

2003 ◽  
Vol 121 (3) ◽  
pp. 496-501 ◽  
Author(s):  
Corinne Moulon ◽  
Yoanna Choleva ◽  
Hermann-Josef Thierse ◽  
Doris Wild ◽  
Hans Ulrich Weltzien
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
T Cell Receptors ◽  
Cell Receptor ◽  
Cell Receptors ◽  
Human T Cells

T Cell Receptor CDR3 Sequences and Recombinant T Cell Receptors

1997 ◽  
Vol 113 (1-3) ◽  
pp. 170-172 ◽  
Author(s):  
Slawomir Sowka ◽  
Roswitha Friedl-Hajek ◽  
Ute Siemann ◽  
Christof Ebner ◽  
Otto Scheiner ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
T Cell Receptors ◽  
Cell Receptor ◽  
Cell Receptors

scholarly journals The somatically generated portion of T cell receptor CDR3α contributes to the MHC allele specificity of the T cell receptor

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Philippa Marrack ◽  
Sai Harsha Krovi ◽  
Daniel Silberman ◽  
Janice White ◽  
Eleanor Kushnir ◽  
...  
Keyword(s):  
Major Histocompatibility Complex ◽  
T Cell ◽  
Positive Selection ◽  
T Cell Receptor ◽  
T Cell Receptors ◽  
Cell Receptor ◽  
Structural Basis ◽  
Major Histocompatibility ◽  
Cell Receptors ◽  
Histocompatibility Complex

Mature T cells bearing αβ T cell receptors react with foreign antigens bound to alleles of major histocompatibility complex proteins (MHC) that they were exposed to during their development in the thymus, a phenomenon known as positive selection. The structural basis for positive selection has long been debated. Here, using mice expressing one of two different T cell receptor β chains and various MHC alleles, we show that positive selection-induced MHC bias of T cell receptors is affected both by the germline encoded elements of the T cell receptor α and β chain and, surprisingly, dramatically affected by the non germ line encoded portions of CDR3 of the T cell receptor α chain. Thus, in addition to determining specificity for antigen, the non germline encoded elements of T cell receptors may help the proteins cope with the extremely polymorphic nature of major histocompatibility complex products within the species.

scholarly journals Determining epitope specificity of T cell receptors with TCRGP

2019 ◽  
Author(s):  
Emmi Jokinen ◽  
Jani Huuhtanen ◽  
Satu Mustjoki ◽  
Markus Heinonen ◽  
Harri Lähdesmäki
Keyword(s):  
T Cell ◽  
Immune Responses ◽  
T Cell Receptors ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Sequencing Data ◽  
Specific Data ◽  
Cell Receptors ◽  
Epitope Specificity ◽  
Process Method

T cell receptors (TCRs) can recognize various pathogens and consequently start immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals’ immune status in different diseases. We have developed TCRGP, a novel Gaussian process method to predict if TCRs recognize certain epitopes. This method can utilize CDR sequences from TCRα and TCRβ chains and learn which CDRs are important in recognizing different epitopes. We have experimented with with epitope-specific data against 29 epitopes and performed a comprehensive evaluation with existing prediction methods. On this data, TCRGP outperforms other state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCRαβ (scRNA+TCRαβ) sequencing data by quantifying epitope-specific TCRs with TCRGP in phenotypes identified from scRNA-seq data. With this approach, we find HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.

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