Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Genes

JacobR. Leistico¹  Tim Miller¹  JunS. Song¹  AlanM. Luu¹  Somang Kim¹ 

[1] Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;

关键词: T cell receptors;    epitope binding specificity;    deep learning;    metric learning;    multimodal learning;   

DOI  :  10.3390/genes12040572

来源: DOAJ

【 摘 要 】

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

【 授权许可】

Unknown