APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules

Zhihao Su, Yejian Wu, Kaiqiang Cao, Jie Du, Lujing Cao, Zhipeng Wu, Xinyi Wu, Xinqiao Wang, Ying Song, Xudong Wang, Hongliang Duan

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

原文English
頁(從 - 到)38-47
頁數10
期刊Methods
228
DOIs
出版狀態Published - 8月 2024

指紋

深入研究「APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules」主題。共同形成了獨特的指紋。

引用此