scholarly journals Predicting T cell receptor antigen specificity from structural features derived from homology models of receptor-peptide-major histocompatibility complexes.

2021 ◽  
Author(s):  
Martina Milighetti ◽  
John Shawe-Taylor ◽  
Benny Chain
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Homology Modelling ◽  
Cell Receptor ◽  
Structural Features ◽  
Physical Interaction ◽  
Major Histocompatibility ◽  
Antigen Specificity ◽  
Binding Interface ◽  
Homology Models

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

scholarly journals Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

2021 ◽  
Vol 12 ◽  
Author(s):  
Martina Milighetti ◽  
John Shawe-Taylor ◽  
Benny Chain
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Homology Modelling ◽  
Cell Receptor ◽  
Structural Features ◽  
Physical Interaction ◽  
Major Histocompatibility ◽  
Antigen Specificity ◽  
Binding Interface ◽  
Homology Models

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

scholarly journals GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hongyi Zhang ◽  
Xiaowei Zhan ◽  
Bo Li
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Large Scale ◽  
Cell Receptor ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Antigen Specificity ◽  
Non Invasive ◽  
Isometric Transformation ◽  
Specific Receptors

AbstractSimilarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

scholarly journals A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

2021 ◽  
Vol 7 (20) ◽  
pp. eabf5835
Author(s):  
Wen Zhang ◽  
Peter G. Hawkins ◽  
Jing He ◽  
Namita T. Gupta ◽  
Jinrui Liu ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Adaptive Immune System ◽  
Cell Receptor ◽  
Immune Monitoring ◽  
Binding Assays ◽  
Antigen Specificity ◽  
Interaction Map ◽  
Antigen Interaction ◽  
Context Specific

T cell receptor (TCR) antigen–specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide–major histocompatibility complex) interactions and a neural network–based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen–specific interactions for basic immunological research and clinical immune monitoring.

scholarly journals In Vivo Selection of T-Cell Receptor Junctional Region Sequences by HLA-A2 Human T-Cell Lymphotropic Virus Type 1 Tax11-19 Peptide Complexes

2001 ◽  
Vol 75 (2) ◽  
pp. 1065-1071 ◽  
Author(s):  
Mineki Saito ◽  
Graham P. Taylor ◽  
Akiko Saito ◽  
Yoshitaka Furukawa ◽  
Koichiro Usuku ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Virus Type ◽  
Cell Receptor ◽  
Antigen Specificity ◽  
Human T Cell ◽  
Cdr3 Region

ABSTRACT Using HLA-peptide tetrameric complexes, we isolated human T-cell lymphotrophic virus type 1 Tax peptide-specific CD8+ T cells ex vivo. Antigen-specific amino acid motifs were identified in the T-cell receptor Vβ CDR3 region of clonally expanded CD8+ T cells. This result directly confirms the importance of the CDR3 region in determining the antigen specificity in vivo.

scholarly journals A Framework to Identify Antigen-Expanded T Cell Receptor Clusters Within Complex Repertoires

2021 ◽  
Vol 12 ◽  
Author(s):  
Valentina Ceglia ◽  
Erin J. Kelley ◽  
Annalee S. Boyle ◽  
Sandra Zurawski ◽  
Heather L. Mead ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
T Cell Responses ◽  
Cell Receptor ◽  
Antigen Specificity ◽  
Protein Antigens ◽  
Cell Responses ◽  
Tcr Repertoire ◽  
Receptor Clusters ◽  
Theoretical Sensitivity

Common approaches for monitoring T cell responses are limited in their multiplexity and sensitivity. In contrast, deep sequencing of the T Cell Receptor (TCR) repertoire provides a global view that is limited only in terms of theoretical sensitivity due to the depth of available sampling; however, the assignment of antigen specificities within TCR repertoires has become a bottleneck. This study combines antigen-driven expansion, deep TCR sequencing, and a novel analysis framework to show that homologous ‘Clusters of Expanded TCRs (CETs)’ can be confidently identified without cell isolation, and assigned to antigen against a background of non-specific clones. We show that clonotypes within each CET respond to the same epitope, and that protein antigens stimulate multiple CETs reactive to constituent peptides. Finally, we demonstrate the personalized assignment of antigen-specificity to rare clones within fully-diverse uncultured repertoires. The method presented here may be used to monitor T cell responses to vaccination and immunotherapy with high fidelity.

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