scholarly journals NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alessandro Montemurro ◽  
Viktoria Schuster ◽  
Helle Rus Povlsen ◽  
Amalie Kai Bentzen ◽  
Vanessa Jurtz ◽  
...  
Keyword(s):  
T Cell ◽  
Prediction Models ◽  
Sequence Data ◽  
Cell Receptor ◽  
Accurate Prediction ◽  
Problem Complexity ◽  
Cell Specificity ◽  
Binding Data ◽  
Peptide Complexes ◽  
Accuracy Data

AbstractPrediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that “shallow” convolutional neural network (CNN) architectures are adequate to deal with the problem complexity imposed by the length variations of TCRs. We demonstrate that current public bulk CDR3β-pMHC binding data overall is of low quality and that the development of accurate prediction models is contingent on paired α/β TCR sequence data corresponding to at least 150 distinct pairs for each investigated pMHC. In comparison, models trained on CDR3α or CDR3β data alone demonstrated a variable and pMHC specific relative performance drop. Together these findings support that T-cell specificity is predictable given the availability of accurate and sufficient paired TCR sequence data. NetTCR-2.0 is publicly available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.0.

Two-Level Parallel Alignment Based on Sequence Parallel Vectorization

2014 ◽  
Vol 490-491 ◽  
pp. 757-762
Author(s):  
Guo Li Ji ◽  
Long Teng Chen ◽  
Liang Liang Chen
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Sequence Alignment ◽  
High Throughput ◽  
Sequence Data ◽  
Cell Receptor ◽  
Alignment Accuracy ◽  
Gpu Acceleration ◽  
Alignment Algorithm ◽  
Alignment Result

This paper proposed a way of two-level parallel alignment based on sequence parallel vectorization with GPU acceleration on the Fermi architecture, which integrates sequence parallel vectorization, parallel k-means clustering approximate alignment and parallel Smith-Waterman algorithm. The method converts sequence alignment into vector alignment by first. Then it uses k-means alignment to divide sequences into several groups and reduce the size of sequence data. The expected accurate alignment result is achieved using parallel Smith-Waterman algorithm. The high-throughput mouse T-cell receptor (TCR) sequences were used to validate the proposed method. Under the same hardware condition, comparing to serial Smith-Waterman algorithm and CUDASW++2.0 algorithm, our method is the most efficient alignment algorithm with high alignment accuracy.

Anatomic location and T-cell stimulatory functions of mouse dendritic cell subsets defined by CD4 and CD8 expression

Blood ◽  
2002 ◽  
Vol 99 (6) ◽  
pp. 2084-2093 ◽  
Author(s):  
Alexander D. McLellan ◽  
Michaela Kapp ◽  
Andreas Eggert ◽  
Christian Linden ◽  
Ursula Bommhardt ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Cd8 T Cells ◽  
Antigen Expression ◽  
Cell Receptor ◽  
In Vitro Assays ◽  
Peptide Complexes ◽  
Marginal Zones

Abstract Mouse spleen contains CD4+, CD8α+, and CD4−/CD8α− dendritic cells (DCs) in a 2:1:1 ratio. An analysis of 70 surface and cytoplasmic antigens revealed several differences in antigen expression between the 3 subsets. Notably, the Birbeck granule–associated Langerin antigen, as well as CD103 (the mouse homologue of the rat DC marker OX62), were specifically expressed by the CD8α+ DC subset. All DC types were apparent in the T-cell areas as well as in the splenic marginal zones and showed similar migratory capacity in collagen lattices. The 3 DC subtypes stimulated allogeneic CD4+ T cells comparably. However, CD8α+ DCs were very weak stimulators of resting or activated allogeneic CD8+ T cells, even at high stimulator-to-responder ratios, although this defect could be overcome under optimal DC/T cell ratios and peptide concentrations using CD8+ F5 T-cell receptor (TCR)–transgenic T cells. CD8α− or CD8α+DCs presented alloantigens with the same efficiency for lysis by cytotoxic T lymphocytes (CTLs), and their turnover rate of class I–peptide complexes was similar, thus neither an inability to present, nor rapid loss of antigenic complexes from CD8α DCs was responsible for the low allostimulatory capacity of CD8α+ DCs in vitro. Surprisingly, both CD8α+ DCs and CD4−/CD8− DCs efficiently primed minor histocompatibility (H-Y male antigen) cytotoxicity following intravenous injection, whereas CD4+ DCs were weak inducers of CTLs. Thus, the inability of CD8α+ DCs to stimulate CD8+ T cells is limited to certain in vitro assays that must lack certain enhancing signals present during in vivo interaction between CD8α+ DCs and CD8+ T cells.

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 NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks

2018 ◽  
Author(s):  
Vanessa Isabell Jurtz ◽  
Leon Eyrich Jessen ◽  
Amalie Kai Bentzen ◽  
Martin Closter Jespersen ◽  
Swapnil Mahajan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Models ◽  
Cytotoxic T Cells ◽  
Cell Receptor ◽  
Training Data ◽  
Class I ◽  
Large Length ◽  
Peptide Complexes ◽  
Complex Target

Predicting epitopes recognized by cytotoxic T cells has been a long standing challenge within the field of immuno- and bioinformatics. While reliable predictions of peptide binding are available for most Major Histocompatibility Complex class I (MHCI) alleles, prediction models of T cell receptor (TCR) interactions with MHC class I-peptide complexes remain poor due to the limited amount of available training data. Recent next generation sequencing projects have however generated a considerable amount of data relating TCR sequences with their cognate HLA-peptide complex target. Here, we utilize such data to train a sequence-based predictor of the interaction between TCRs and peptides presented by the most common human MHCI allele, HLA-A*02:01. Our model is based on convolutional neural networks, which are especially designed to meet the challenges posed by the large length variations of TCRs. We show that such a sequence-based model allows for the identification of TCRs binding a given cognate peptide-MHC target out of a large pool of non-binding TCRs.

scholarly journals Breast cancer is marked by specific, Public T-cell receptor CDR3 regions shared by mice and humans

2021 ◽  
Vol 17 (1) ◽  
pp. e1008486
Author(s):  
Miri Gordin ◽  
Hagit Philip ◽  
Alona Zilberberg ◽  
Moriah Gidoni ◽  
Raanan Margalit ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Sequence Data ◽  
Breast Tumors ◽  
Cell Receptor ◽  
Beta Chain ◽  
Antigen Receptors ◽  
Partial Success

The partial success of tumor immunotherapy induced by checkpoint blockade, which is not antigen-specific, suggests that the immune system of some patients contain antigen receptors able to specifically identify tumor cells. Here we focused on T-cell receptor (TCR) repertoires associated with spontaneous breast cancer. We studied the alpha and beta chain CDR3 domains of TCR repertoires of CD4 T cells using deep sequencing of cell populations in mice and applied the results to published TCR sequence data obtained from human patients. We screened peripheral blood T cells obtained monthly from individual mice spontaneously developing breast tumors by 5 months. We then looked at identical TCR sequences in published human studies; we used TCGA data from tumors and healthy tissues of 1,256 breast cancer resections and from 4 focused studies including sequences from tumors, lymph nodes, blood and healthy tissues, and from single cell dataset of 3 breast cancer subjects. We now report that mice spontaneously developing breast cancer manifest shared, Public CDR3 regions in both their alpha and beta and that a significant number of women with early breast cancer manifest identical CDR3 sequences. These findings suggest that the development of breast cancer is associated, across species, with biomarker, exclusive TCR repertoires.

scholarly journals TRIP - T cell receptor/immunoglobulin profiler

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Maria Th. Kotouza ◽  
◽  
Katerina Gemenetzi ◽  
Chrysi Galigalidou ◽  
Elisavet Vlachonikola ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Web Application ◽  
Somatic Hypermutation ◽  
Sequence Data ◽  
Receptor Gene ◽  
Antigen Receptor ◽  
Cell Receptor ◽  
Nucleotide Composition ◽  
Depth Analysis

Abstract Background Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These provide detailed information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny. Results Two sets of experiments have been performed in order to evaluate the efficiency and performance of the TRIP tool. The first used a number of synthetic datasets, ranging from 250k to 1M sequences, and established the linear response time of the tool (about 6 h for 1M sequences processed through the entire BcR IG data pipeline). The reproducibility of the tool was tested comparing the results produced by the main TRIP workflow with the results from a previous pipeline used on the Galaxy platform. As expected, no significant differences were noted between the two tools; although the preselection process seems to be stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered out, with no impact on the final results. Conclusions TRIP is a software framework that provides analytical services on antigen receptor gene sequence data. It is accurate and contains functions for data wrangling, cleaning, analysis and visualization, enabling the user to build a pipeline tailored to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler.

Engineered T-cell receptor tetramers bind MHC-peptide complexes with high affinity

2004 ◽  
Vol 22 (11) ◽  
pp. 1429-1434 ◽  
Author(s):  
Ramu A Subbramanian ◽  
Chikaya Moriya ◽  
Kristi L Martin ◽  
Fred W Peyerl ◽  
Atsuhiko Hasegawa ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
High Affinity ◽  
Peptide Complexes

scholarly journals A High-Avidity T-cell Receptor Redirects Natural Killer T-cell Specificity and Outcompetes the Endogenous Invariant T-cell Receptor

2019 ◽  
Vol 8 (1) ◽  
pp. 57-69 ◽  
Author(s):  
Elisa Landoni ◽  
Christof C. Smith ◽  
Giovanni Fucá ◽  
Yuhui Chen ◽  
Chuang Sun ◽  
...  
Keyword(s):  
T Cell ◽  
Natural Killer ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
High Avidity ◽  
Natural Killer T Cell ◽  
Cell Specificity ◽  
Killer T Cell ◽  
Natural Killer T

scholarly journals The workings and failings of clustering T-cell receptor beta-chain sequences without a known epitope preference

2018 ◽  
Author(s):  
Pieter Meysman ◽  
Nicolas De Neuter ◽  
Sofie Gielis ◽  
Danh Bui Thi ◽  
Benson Ogunjimi ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Sequence Data ◽  
Cell Receptor ◽  
Data Sets ◽  
Cell Groups ◽  
Target Epitope ◽  
T Cell Receptor Beta ◽  
Receptor Beta

AbstractThe T-cell receptor is responsible for recognizing potentially harmful epitopes presented on cell surfaces. The binding rules that govern this recognition between receptor and epitope is currently an unsolved problem, yet one of great interest. Several methods have been proposed recently to perform supervised classification of T-cell receptor sequences, but this requires known examples of T-cell sequences for a given epitope. Here we study the viability of various methods to perform unsupervised clustering of distinct T-cell receptor sequences and how these clusters relate to their target epitope. The goal is to provide an overview of the performance of various distance metrics on two large independent T-cell receptor sequence data sets. Our results confirm the presence of structural distinct T-cell groups that target identical epitopes. In addition, we put forward several recommendations to perform T-cell receptor sequence clustering.

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