SwarmTCR: a computational approach to predict the specificity of T Cell Receptors

Abstract

MotivationComputationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to tackle this problem is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance. ResultsWe compared the performance of SwarmTCR against a state-of-the-art method (TCRdist) and showed that SwarmTCR performed significantly better on epitopes EBV-BRLF1300, EBV-BRLF1109, NS4B214-222 with single cell data and epitopes EBV-BRLF1300, EBV-BRLF1109, IAV-M158 with bulk sequencing data ( and {beta} chains). In addition, we show that the weights returned by SwarmTCR are biologically interpretable. AvailabilitySwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR) Contactdghersi@unomaha.edu