Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen
Vadim K Karnaukhov1,2, Dmitrii S Shcherbinin3,4, Anton O Chugunov3,5
1Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia. vad.karnaukhov@gmail.com.
Abstract:
T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.
Insights
TCRen predicts novel T cell receptor (TCR) peptide interactions using a structure-based approach. This method aids in identifying potential cancer neoepitopes and has implications for treating various diseases.
Area of Science:
- Immunology
- Computational Biology
- Structural Biology
Background:
- T cell receptor (TCR) recognition of peptide-MHC complexes is crucial for adaptive immunity against pathogens and cancer.
- Predicting TCR-peptide interactions is vital for developing therapies for cancer, infectious, and autoimmune diseases.
- Identifying novel peptide epitopes recognized by TCRs remains a significant challenge.
Purpose of the Study:
- To present TCRen, a novel structure-based computational method for ranking unseen peptide epitopes for a given TCR.
- To improve the prediction accuracy of TCR-peptide interactions, especially for novel epitopes.
Main Methods:
- TCRen models the TCR-peptide-MHC structure.
- A TCR-peptide residue contact map is generated from the modeled structure.
- Candidate epitopes are ranked using an interaction score based on an energy potential derived from crystal structure statistics.
Main Results:
- TCRen demonstrates high performance in distinguishing cognate from unrelated peptides.
- The method effectively ranks candidate unseen epitopes for a given TCR.
- TCRen facilitates the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.
Conclusions:
- TCRen provides a robust structure-based method for predicting TCR-peptide interactions.
- This approach can significantly aid in the discovery of therapeutic targets for cancer and other immune-related diseases.
- The method's ability to identify novel epitopes enhances its utility in personalized medicine.
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