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