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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.01191

Peptide-MHC class I binding prediction based on Deep Learning with Novel feature

  • 1Harbin Institute of Technology, China
  • 2Harbin Medical University, China

Peptide-based vaccine development needs accurate pre-diction of the binding affinity between major histocom-patibility complex I (MHC I) proteins and their peptide ligands. Nowadays more and more machine learning methods have been developed to predict binding affinity and some of them have become the popular tools. How-ever most of them are designed by the shallow neural networks. As Bengio said that deep neural networks can learn better fits with less data than shallow neural net-works. In our case, some of the alleles only have dozens of peptides data. In addition, we transform each peptide into a characteristic matrix and input it into the model. As we know when dealing with the problem that the input is a matrix, CNN can find the most critical features by itself. Obviously, compared with the traditional neural network model, CNN is more suitable for predicting binding affin-ity. Different from the previous studies which are based on BLOSUM, we used novel feature to do the prediction. Since we consider that the order of the sequence, hy-dropathy index, polarity and the length of the peptide could affect the binding affinity and the properties of these amino acids are key factors for their binding to MHC, we extracted these information from each peptide. In order to make full use of the data we have obtained, we have integrated different length of peptides into 15mer based on the binding mode of peptide to MHC I. In order to demonstrate that our method is reliable to pre-dict pep-tide-MHC binding, we compared our method with several popular methods. The experiments show the superiority of our method.

Keywords: deep learning, Convolutional Neural Network, epitope prediction, Peptide-MHC class I binding prediction, HLA

Received: 15 Aug 2019; Accepted: 28 Oct 2019.

Copyright: © 2019 Zhao, Cheng, Zang and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mx. Tianyi Zang, Harbin Institute of Technology, Harbin, China,
Mx. Yang Hu, Harbin Institute of Technology, Harbin, China,