计算预测MHC-I肽段结合亲和力是免疫生物信息学中的一个重要问题，也对于个性化治疗癌症疫苗中新抗原的鉴定至关重要。最近，基于深度学习的最前沿方法在这个问题上无法达到令人满意的性能，特别是对于非9个氨基酸的肽段。这是因为这些方法生成输入时，仅仅是将给定的两个序列进行简单拼接：一个肽段和（MHC I类分子的伪序列），无法精确捕捉与具有可变长度的肽段相关的MHC结合基序中的锚定位置。因此，我们开发了一种能感知锚定位置并具有高性能的深度模型DeepMHCI，它采用位置感知门控层和残余结合交互卷积层。这使得模型能够控制肽段中的信息流，以感知锚定位置，并通过多个卷积核直接建模肽段与MHC伪（结合）序列之间的相互作用。通过对四个基准数据集进行广泛实验证明了DeepMHCI的性能，包括五折交叉验证、独立测试集验证、外部HPV疫苗识别和外部CD8+表位识别等各种设置。通过绑定基序的可视化实验结果表明，DeepMHCI在所有竞争方法中表现出色，特别是在非9个氨基酸的肽段结合预测方面。DeepMHCI可通过https://github.com/ZhuLab-Fudan/DeepMHCI公开获取。附加数据可在Bioinformatics online上找到。©2023作者。由牛津大学出版。

Computationally predicting MHC-I peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels.The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as five-fold cross-validation, validation with the independent testing set, external HPV vaccine identification and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction.DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.