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. 2023 Aug 15:14:1224969.
doi: 10.3389/fimmu.2023.1224969. eCollection 2023.

Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features

Affiliations

Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features

Dmitrii S Shcherbinin et al. Front Immunol. .

Abstract

Introduction: T-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.

Methods: Identification 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.

Results: 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 - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues.

Conclusion: Our results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.

Keywords: T-cell receptor; TCR-peptide-MHC complex; antigen recognition; database; structural modeling.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
(A) Pipeline used in the stepwise amino acid mutation modeling approach applied in the present study. (B) Mutation path in one of the studied CDR3 clusters starting from a known 2NX5 PDB structure (36) carrying CAVQASGGSYIPTF CDR3α loop. (C) CDR3 sequence similarity map of VDJdb, layout of a graph with edges connecting CDR3 sequences that differ by no more than a single amino acid mismatch. CDR3 sequences having PDB templates are shown with labels. Orange points show VDJdb entries that were classified as belonging to a sequence homology motif (described in (37)), red points are connected components built around PDB structure templates, TCR:pMHC records connected by no more than 3 subsequent amino acid substitutions to a PDB that were modeled in present study are shown with black crosses. (D) An example of the mutation pathways in which the same CDR3 loops could be modeled using different intermediate sequences. The corresponding pathways are shown with bold red arrows.
Figure 2
Figure 2
Modeled amino acid substitutions in CDR3 loops in TCR:peptide:MHC complexes containing SARS-CoV-2 spike epitopes YLQPRTFLL. Contacting residues are represented as sticks, modeled substitutions are represented as lines and close contacts are illustrated as dashed sticks. CDR3 loop of TCRα is colored green, TCRβ is colored cyan, and the peptide residues are colored magenta.
Figure 3
Figure 3
Correlation of BLOSUM62 matrix indices and absolute values of dEnergy, calculated for modeled structures. Corresponding values for TCRα and TCRβ are colored red and blue.
Figure 4
Figure 4
Correlation between the remoteness of contacting residues in CDR3-peptide interface and absolute dEnergy values. Overall correlation values are presented in black, and linear fittings calculated for TCR-alpha and TCR-beta chains independently are presented in red and blue respectively.
Figure 5
Figure 5
Correlations of calculated descriptors between contacting residues of CDR3 loops and epitopes. The presented correlation analysis was performed for the closest contacting residues, interacting through at least one side-chain group in (A) minimized and (B) repacked models. Paired correlation values are represented as waffle plots, and their distribution in groups is shown as histograms. Statistical significance of correlation is labeled with “*”, “**” and “***” for P-values< 0.05, 0.01 and 0.001 respectively.

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