TCRex: a webtool for the prediction of T-cell receptor sequence epitope specificity

laserson · August 28, 2018, 9:27pm

This manuscript is slim. It looks like each possible epitope has its own random forest trained.

Full abstract:

Identification of T-cell receptor (TCR) repertoire epitope targets constitutes an important part of many TCR repertoire studies. To date, we are still relying on time consuming epitope binding experiments for the identification of epitope-specific TCR sequences. Recently, we showed that the prediction of epitope-TCR interaction is possible using a random forest model. We implemented this method in a webtool called TCRex. TCRex is the first tool that enables the prediction of TCR-epitope recognition. It allows users to upload TCR sequences and predict interaction with multiple known cancer or viral epitopes or train new prediction models for new epitopes. TCRex is freely available for academic use at tcrex.biodatamining.be

ematsen · September 19, 2018, 1:35pm

Yes, each epitope has its own random forest.

The real methods development and benchmarking (for two HIV epitopes) is done in

ncbi.nlm.nih.gov

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

N De Neuter, W Bittremieux, C Beirnaert, B Cuypers, A Mrzic, P Moris, A Suls, V Van Tendeloo, B Ogunjimi, K Laukens and P Meysman, Immunogenetics, 2018 03

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.