Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning.
Genes (Basel)
; 12(4)2021 04 15.
Article
em En
| MEDLINE
| ID: mdl-33920780
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Receptores de Antígenos de Linfócitos T
/
Biologia Computacional
/
Epitopos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Genes (Basel)
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos