IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul-Aug;19(4):2188-2196. doi: 10.1109/TCBB.2021.3074927. Epub 2022 Aug 8.
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.
人类白细胞抗原 (HLA) 复合体分子通过在细胞表面呈现肽来在免疫相互作用中发挥重要作用。随着深度学习的显著进展，已经提出了一系列基于神经网络的模型，并因其对肽-HLA I 类结合预测的优异性能而得到证明。然而，由于其固有的挑战，仍然缺乏有效的用于 HLA 类 II 蛋白与肽结合的结合预测模型。在这项工作中，我们提出了一种新的基于序列的泛特异性神经网络结构 DeepSeaPanII，用于肽-HLA 类 II 结合预测。与现有的泛特异性模型相比，我们的模型是一个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的最新性能外，DeepSeqPanII 还可以通过其基于注意力机制的结合核心预测能力，提取关于肽结合机制的生物学见解。留一基因座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 肽结合方面取得了最新的性能。源代码和训练好的模型可在 https://github.com/pcpLiu/DeepSeqPanII 上免费获取。
