MHCSeqNet: a deep neural network model for universal MHC binding prediction

BMC Bioinformatics

Sira Sriswasdi¹  Natapol Pornputtapong²  Ekapol Chuangsuwanich³  Poomarin Phloyphisut³ 

[1] Computational Molecular Biology Group, Faculty of Medicine, Chulalongkorn University;Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University;Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University;

关键词: MHC epitope prediction;    Deep learning;    Recurrent neural networks;   

DOI  :  10.1186/s12859-019-2892-4

来源: DOAJ

【 摘 要 】

Abstract Background Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synthetic peptide vaccine have been proven effective in both mouse models and human patients. Because only a tiny fraction of cancer-specific neoepitopes actually elicits immune response, selection of potent, immunogenic neoepitopes remains a challenging step in cancer vaccine development. A basic approach for immunogenicity prediction is based on the premise that effective neoepitope should bind with the Major Histocompatibility Complex (MHC) with high affinity. Results In this study, we developed MHCSeqNet, an open-source deep learning model, which not only outperforms state-of-the-art predictors on both MHC binding affinity and MHC ligand peptidome datasets but also exhibits promising generalization to unseen MHC class I alleles. MHCSeqNet employed neural network architectures developed for natural language processing to model amino acid sequence representations of MHC allele and epitope peptide as sentences with amino acids as individual words. This consideration allows MHCSeqNet to accept new MHC alleles as well as peptides of any length. Conclusions The improved performance and the flexibility offered by MHCSeqNet should make it a valuable tool for screening effective neoepitopes in cancer vaccine development.

【 授权许可】

Unknown