Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity

TL;DR

This paper introduces ATMTCR, an attention-aware contrastive learning model that improves prediction of TCR-antigen binding specificity by leveraging Transformer encodings and masked contrastive views, enhancing interpretability and performance.

Contribution

The paper presents a novel contrastive learning approach with attention-guided masking for TCR-antigen binding prediction, outperforming existing methods and providing interpretability.

Findings

Contrastive learning significantly improves prediction accuracy.

The model effectively identifies important amino acids.

High-order semantic information is captured in TCR sequences.

Abstract

It has been verified that only a small fraction of the neoantigens presented by MHC class I molecules on the cell surface can elicit T cells. The limitation can be attributed to the binding specificity of T cell receptor (TCR) to peptide-MHC complex (pMHC). Computational prediction of T cell binding to neoantigens is an challenging and unresolved task. In this paper, we propose an attentive-mask contrastive learning model, ATMTCR, for inferring TCR-antigen binding specificity. For each input TCR sequence, we used Transformer encoder to transform it to latent representation, and then masked a proportion of residues guided by attention weights to generate its contrastive view. Pretraining on large-scale TCR CDR3 sequences, we verified that contrastive learning significantly improved the prediction performance of TCR binding to peptide-MHC complex (pMHC). Beyond the detection of important amino acids and their locations in the TCR sequence, our model can also extracted high-order semantic information underlying the TCR-antigen binding specificity. Comparison experiments were conducted on two independent datasets, our method achieved better performance than other existing algorithms. Moreover, we effectively identified important amino acids and their positional preferences through attention weights, which indicated the interpretability of our proposed model.