Attentive Variational Information Bottleneck for TCR-peptide Interaction Prediction.

Abstract

We present a multi-sequence generalization of Variational Information Bottleneck (VIB) (Alemi et al., 2016) and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention (Vaswani et al., 2017) to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T cell receptors (TCRs) and peptides. Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution (OOD) amino acid sequences. The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr. Supplementary data are available at Bioinformatics online.