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- W3136197390 abstract "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 CDR3B 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 the 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 insight into the learned predictive features of TCR-epitope binding specificity and advances associated classification tasks." @default.
- W3136197390 created "2021-03-29" @default.
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- W3136197390 date "2021-03-20" @default.
- W3136197390 modified "2023-10-16" @default.
- W3136197390 title "Predicting TCR-epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning" @default.
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- W3136197390 doi "https://doi.org/10.1101/2021.03.19.436191" @default.
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