SHCHERBININ, Dmitrii S, Vadim K KARNAUKHOV, Ivan V ZVYAGIN, Dmitriy CHUDAKOV and Mikhail SHUGAY. Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features. Frontiers in immunology. LAUSANNE: Frontiers Media S.A., 2023, vol. 14, Aug, p. 1-14. ISSN 1664-3224. Available from: https://dx.doi.org/10.3389/fimmu.2023.1224969.
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Basic information
Original name Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features
Authors SHCHERBININ, Dmitrii S, Vadim K KARNAUKHOV, Ivan V ZVYAGIN, Dmitriy CHUDAKOV (643 Russian Federation, belonging to the institution) and Mikhail SHUGAY (guarantor).
Edition Frontiers in immunology, LAUSANNE, Frontiers Media S.A. 2023, 1664-3224.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 30102 Immunology
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 7.300 in 2022
RIV identification code RIV/00216224:14740/23:00133575
Organization unit Central European Institute of Technology
Doi http://dx.doi.org/10.3389/fimmu.2023.1224969
UT WoS 001119019700001
Keywords in English T-cell receptor; antigen recognition; TCR-peptide-MHC complex; structural modeling; database
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Eva Dubská, učo 77638. Changed: 7/3/2024 23:59.
Abstract
IntroductionT-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-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 these complexes using TCR sequences with known specificity.MethodsIdentification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R.ResultsUsing 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 TCR alpha or TCR beta 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 - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues.ConclusionOur results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.
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