CcBHLA: pan-specific peptide–HLA class I binding prediction via Convolutional and BiLSTM features
bioRxiv (Cold Spring Harbor Laboratory)(2023)
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 ().
### Competing Interest Statement
The authors have declared no competing interest.
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Key words
peptide–hla,binding,ccbhla class,pan-specific
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