Title: MHCherryPan: a novel pan-specific model for binding affinity prediction of class I HLA-peptide

Authors: Xuezhi Xie; Yuanyuan Han; Kaizhong Zhang

Addresses: Department of Computer Science, Western University, London, Ontario, Canada ' Department of Computer Science, Western University, London, Ontario, Canada ' Department of Computer Science, Western University, London, Ontario, Canada

Abstract: The Human Leukocyte Antigen (HLA) system or complex plays an irreplaceable role in regulating the humans' immune system. Accurate prediction of peptide binding with HLA can efficiently promote to identify those neoantigens, which potentially make a great change in immune drug development. HLA is one of the most polymorphic genetic systems in humans, and thousands of HLA allelic versions exist (Choo, 2007). Owing the high polymorphism of HLA complex, it is still pretty difficult to accurately predict the binding affinity. In this paper, we proposed a novel algorithm which combined convolutional neural network and long short-term memory to solve this problem. Our model has been tested with the experimental benchmark from IEDB and shows the state-of-the-art performance compared with other currently popular algorithms.

Keywords: bioinformatics; deep learning; health informatics; machine learning; HLA; MHC.

DOI: 10.1504/IJDMB.2020.112850

International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.3, pp.201 - 219

Received: 08 Apr 2020
Accepted: 08 Apr 2020

Published online: 21 Nov 2020 *

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