T Cell Epitope Prediction and Its Application to Immunotherapy

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications

The Protein-Templated Synthesis of Enzyme-Generated Aptamers

Inspired by the chemical synthesis of molecularly imprinted polymers, we demonstrated for the first time, the protein‐target mediated synthesis of enzyme‐generated aptamers (EGAs). We prepared pre‐polymerisation mixtures containing different ratios of nucleotides, an initiator sequence and protein template and incubated each mixture with terminal deoxynucleotidyl transferase (TdT). Upon purification and rebinding of the EGAs against the target, we observed an enhancement in binding of templated‐EGAs towards the target compared to a non‐templated control. These results demonstrate the presence of two primary mechanisms for the formation of EGAs, namely, the binding of random sequences to the target as observed in systematic evolution of ligands by exponential enrichment (SELEX) and the dynamic competition between TdT enzyme and the target protein for binding of EGAs during synthesis. The latter mechanism serves to increase the stringency of EGA‐based screening and represents a new way to develop aptamers that relies on rational design

T cell receptor sequence clustering and antigen specificity

There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering

IMPROVE:a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.</p