Key Laboratory of Biorheological Science and Technology (Ministry of Education), Chongqing University, Chongqing, 400044, China; College of Bioengineering, Chongqing University, Chongqing, 400044, China.
Key Laboratory of Biorheological Science and Technology (Ministry of Education), Chongqing University, Chongqing, 400044, China.
Mol Immunol. 2021 Nov;139:177-183. doi: 10.1016/j.molimm.2021.09.004. Epub 2021 Sep 20.
The prediction of human leukocyte antigen (HLA) class II binding peptides plays important roles in understanding the mechanism of immune recognition and developing effective epitope-based vaccines. In this work, gated recurrent unit (GRU)-based recurrent neural network (RNN) was successfully employed to establish a pan-specific prediction model of HLA-II-binding peptides by using only the HLA and peptide sequence information. In comparison with the existing pan-specific models of HLA-II-binding peptides, the GRU-based RNN model covered a broad spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP molecules with peptide lengths varying from 8 to 43 mers. The results demonstrated strong discriminant capabilities of the GRU-based RNN model, of which the AUC values were 0.92, 0.88, and 0.88 for the training, validation, and test sets, respectively. Also, the GRU-based model showed state-of-the-art performances in predicting the binding peptides with the length ranging from 8-32 mers, which provides an efficient method for predicting HLA-II-binding peptides of longer lengths in comparison with the available methods. Overall, taking the advantages of the RNN architecture, the established pan-specific GRU model can be used for predicting accurately the HLA-II-binding peptides in a simple and direct manner.
人类白细胞抗原（HLA）II 类结合肽的预测在理解免疫识别机制和开发有效的基于表位的疫苗方面发挥着重要作用。在这项工作中，基于门控循环单元（GRU）的循环神经网络（RNN）被成功地用于仅使用 HLA 和肽序列信息来建立 HLA-II 结合肽的泛特异性预测模型。与现有的 HLA-II 结合肽的泛特异性模型相比，基于 GRU 的 RNN 模型涵盖了广泛的 HLA-II 分子，包括 50 个 HLA-DR、47 个 HLA-DQ 和 19 个 HLA-DP 分子，肽长度从 8 到 43 个残基不等。结果表明，基于 GRU 的 RNN 模型具有很强的判别能力，其在训练集、验证集和测试集中的 AUC 值分别为 0.92、0.88 和 0.88。此外，基于 GRU 的模型在预测 8-32 个残基长度的结合肽方面表现出了最先进的性能，与现有的方法相比，这为预测更长长度的 HLA-II 结合肽提供了一种高效的方法。总的来说，基于 RNN 架构的优势，所建立的泛特异性 GRU 模型可以简单直接地用于准确预测 HLA-II 结合肽。
