Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed and demonstrated with their excellent performances for peptide-HLA class I binding prediction. However, there is still a lack of effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. We present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction in this work. Our model is an end-to-end neural network model without the need for pre-or post-processing on input samples compared with existing pan-specific models. Besides state-of-the-art performance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide by its attention mechanism-based binding core prediction capability. The leave-one-allele-out cross-validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII.

中文翻译：

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DeepSeqPanII：一种具有注意力机制的可解释的循环神经网络模型，用于肽-HLA II 类结合预测


人类白细胞抗原 (HLA) 复合物分子通过将细胞表面的肽呈递给 T 细胞，在免疫相互作用中发挥重要作用。随着深度学习的显着进展，一系列基于神经网络的模型被提出并证明其在肽-HLA I类结合预测方面具有优异的性能。然而，由于其固有的挑战，HLA II类蛋白与肽的结合仍然缺乏有效的结合预测模型。我们在这项工作中提出了一种新型的基于序列的泛特异性神经网络结构 DeepSeaPanII，用于肽-HLA II 类结合预测。与现有的泛特定模型相比，我们的模型是端到端的神经网络模型，不需要对输入样本进行预处理或后处理。除了在结合亲和力预测方面最先进的性能外，DeepSeqPanII 还可以通过其基于注意力机制的结合核心预测功能来提取有关肽结合机制的生物学见解。留一等位基因交叉验证和基准评估结果表明，我们提出的网络模型在 HLA-II 肽结合方面实现了最先进的性能。源代码和训练模型可在 https://github.com/pcpLiu/DeepSeqPanII 免费获取。