Biology subjects

Shcherbinin, D. S.

Publications and source records attributed to Shcherbinin, D. S..

4 recordsLinked to original sources

bioRxiv2023

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

T-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 TCR 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: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.

bioinformatics↗

bioRxiv2022

Predicting TCR-peptide recognition based on residue-level pairwise statistical potential

Prediction of TCR-peptide interactions has great importance for therapy of cancer, infectious and autoimmune diseases, but remains a major challenge, particularly for unseen epitopes. We present a structure-based method that enables scoring of TCR-peptide interactions using an energy potential (TCRen) derived from statistics of TCR-peptide contacts in existing crystal structures. We show that TCRen has high performance in discriminating cognate/unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

immunology↗

bioRxiv2020

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells, is of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the centre of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology, has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as SARS-CoV-2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in training data of the models seem to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen vs. cancer peptides. Overall, we demonstrate that accurate and reliable prediction of immunogenic CD8+ T cell targets remains unsolved, thus we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

immunology↗

bioRxiv2019

Comprehensive analysis of structural and sequencing data reveals almost unconstrained chain pairing in TCRαβ complex

Antigen recognition by T-cells is guided by the T-cell receptor (TCR) heterodimer formed by and {beta} chains. A huge diversity of TCR sequences should be maintained by the immune system in order to be able to mount an effective response towards foreign pathogens, so, due to cooperative binding of and {beta} chains to the pathogen, any constraints on chain pairing can have a profound effect on immune repertoire structure, diversity and antigen specificity. By integrating available structural data and paired chain sequencing results we were able to show that there are almost no constraints on pairing in TCR{beta} complexes, allowing naive T-cell repertoire to reach the highest possible diversity. Additional analysis reveals that the specific choice of contacting amino acids can still have a profound effect on complex conformation. Moreover, antigen-driven selection can distort the uniform landscape of chain pairing, while small, yet significant, differences in the pairing can be attributed to various specialized T-cell subsets such as MAIT and iNKT T-cells, as well as other putative invariant TCRs.

bioinformatics↗