Predicting epitopes Based on TCR sequence using an embedding deep neural network artificial intelligence approach

Abstract

T cells receptors are fundamental in recognizing antigens and mediating an appropriate specific immune response against them. Today TCR sequencing has contributed to forming a large repertoire for different immune-associated pathologies. However, predicting epitopes based on TCR sequences has not been satisfactory achieved. We formed a deep neural network using a combination of an embedding autoencoder and selu and relu layers to predict epitopes based on TCR TCR {beta}-chain CDR3. We trained our model using the VDJ database (VDJdb) and validated it using the manually curated catalog of pathology-associated T cell receptor sequences (McPAS-TCR). We used various metrics to measure the accuracy of our tool. We found that our tool can achieve an accuracy of 98 %. Overall our approach presents a step toward identifying microbes crosslinking epitopes that could be playing an important role in various immune diseases.