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• W4367302194 abstract "Abstract Human major histocompatibility complex (MHC) proteins are encoded by the human leukocyte antigen (HLA) gene complex. When exogenous peptide fragments form peptide-HLA (pHLA) complexes with HLA molecules on the outer surface of cells, they can be recognized by T cells and trigger an immune response. Therefore, determining whether an HLA molecule can bind to a given peptide can improve the efficiency of vaccine design and facilitate the development of immunotherapy. This paper regards peptide fragments as natural language, we combine textCNN and BiLSTM to build a deep neural network model to encode the sequence features of HLA and peptides. Results on independent and external test datasets demonstrate that our CcBHLA model outperforms the state-of-the-art known methods in detecting HLA class I binding peptides. And the method is not limited by the HLA class I allele and the length of the peptide fragment. Users can download the model for binding peptide screening or retrain the model with private data on github ( https://github.com/hongliangduan/CcBHLA-pan-specific-peptide-HLA-class-I-binding-prediction-via-Convolutional-and-BiLSTM-features.git )." @default.
• W4367302194 created "2023-04-29" @default.
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• W4367302194 date "2023-04-28" @default.
• W4367302194 modified "2023-09-24" @default.
• W4367302194 title "CcBHLA: pan-specific peptide–HLA class I binding prediction via Convolutional and BiLSTM features" @default.
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• W4367302194 doi "https://doi.org/10.1101/2023.04.24.538196" @default.
• W4367302194 hasPublicationYear "2023" @default.
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