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

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 learn

SUBMITTER: Phloyphisut P 

PROVIDER: S-EPMC6540523 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature