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bioRxiv · 10.1101/2023.04.24.538196

CcBHLA: pan-specific peptide-HLA class I binding prediction via Convolutional and BiLSTM features

Wu, Y.·Cao, L.·Wu, Z.·Wu, X.·Wang, X.·Duan, H.
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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).

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Wu, Y., Cao, L., Wu, Z., Wu, X., Wang, X., Duan, H.. 2023-04-28. CcBHLA: pan-specific peptide-HLA class I binding prediction via Convolutional and BiLSTM features. https://doi.org/10.1101/2023.04.24.538196

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