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起着重要作用。在这项工作中，基于门控循环单元 (GRU) 的循环神经网络 (RNN) 仅使用 HLA 和肽序列信息，成功地建立了 HLA-II 结合肽的泛特异性预测模型。与现有的 HLA-II 结合肽泛特异性模型相比，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 mers 不等。结果证明了基于 GRU 的 RNN 模型的强大判别能力，其中 AUC 值分别为 0.92、0.88 和 0.88，用于训练、验证、和测试集，分别。此外，基于 GRU 的模型在预测长度范围为 8-32 mers 的结合肽方面表现出最先进的性能，这提供了一种有效的方法来预测更长长度的 HLA-II 结合肽。可用的方法。总的来说，利用RNN架构的优势，建立的泛特异性GRU模型可以简单直接地准确预测HLA-II结合肽。