On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition

Abstract

Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.

T‐cell receptor and B‐cell receptor repertoire profiling in adaptive immunity

“…These tools are used to assess the characteristics of different individual repertoires, including repertoire diversity and the usage of gene segments, in order to build evolutionary trees of hypermutated antibodies, and to compare individual repertoires to each other. Few programs have been developed that can predict TCR antigen specificity based on their sequence. Databases containing TCR sequences with known antigen specificities are also used to characterize repertoires.…”

“…However, it is still possible to identify specific TCR/BCR sequence features that are crucial for the recognition of antigens associated with the disease of interest. TCRs recognizing the same epitope often have highly similar sequences, and several sequence similarity measures have been recently proposed to cluster TCRs recognizing them . Shared TCR motifs arise by the same mechanism as described for public clonotype generation in the previous section: independent convergent recombination of the same sequence feature in many precursor cells, followed by clonal selection for the cognate antigen in the periphery.…”

“…CMV‐associated clonotypes were identified as predominantly shared between CMV‐positive patients using Fisher's exact test. The resulting collection of CMV‐associated clonotypes was afterwards used to create an accurate classifier of donor CMV status, which was also tested on an independent cohort . In another study, Faham et al .…”

TCRex: detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires

“…To determine the epitope specificity of TCRs, machine learning models have been developed to analyze TCR sequences and predict the probability that they recognize and bind specific epitopes. These methods are based on the principle that similar TCR sequences often target the same epitope (11) and that machine learning techniques can be used to learn the molecular underpinnings that are shared by these epitope-specific TCR sequences (12)(13)(14). While these methods have been shown to be performant on small targeted data sets, their application on full repertoire datasets remains challenging.…”

“…We built upon the epitope-specific TCR classifier described in (12) and developed an approach designed for the identification of various epitope-specific TCRs in full repertoire datasets. We have applied this new approach to three independent datasets (1,2,15), which have previously been analyzed with the traditional TCR repertoire data analyses described above.…”