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Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features
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AbstractT-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. A large number of TCR sequences specific to different antigens are known to date, however, the structural data describing the conformation and contacting residues for TCR:antigen:MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of TCR:antigen:MHC complexes using TCR sequences with known specificity. Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR:antigen interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues. Our results provide a methodology for creating high-quality TCR:antigen:MHC models for antigens of interest that can be utilized to predict TCR specificity.
Cold Spring Harbor Laboratory
Title: Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features
Description:
AbstractT-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens.
A large number of TCR sequences specific to different antigens are known to date, however, the structural data describing the conformation and contacting residues for TCR:antigen:MHC complexes is relatively limited.
In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of TCR:antigen:MHC complexes using TCR sequences with known specificity.
Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure.
This database allowed us to analyze features of amino acid contacts in TCR:antigen interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues.
Our results provide a methodology for creating high-quality TCR:antigen:MHC models for antigens of interest that can be utilized to predict TCR specificity.
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