Motivation The binding of a peptide antigen to a class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient’s immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. Results In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. Availability and implementation The source code and installation instruction for MHCSeqNet2 is available at https://github.com/cmb-chula/MHCSeqNet2 Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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MHCSeqNet2 - 改进的低数据等位基因的肽类 I MHC 结合预测

动机 肽抗原与 I 类主要组织相容性复合物 (MHC) 蛋白的结合是免疫系统识别受感染细胞或癌细胞的关键过程的一部分。这种机制使得基于肽的疫苗得以开发，该疫苗可以激活患者的免疫反应来治疗癌症。因此，准确预测肽-MHC 结合的能力是为每位患者优先选择最佳肽的重要组成部分。然而，许多MHC等位基因的肽-MHC结合实验数据仍然缺乏，这限制了现有预测模型的准确性。结果在本研究中，我们提出了 MHCSeqNet 的改进版本，它利用子字级肽特征、MHC 等位基因的 3D 结构嵌入以及扩展的训练数据集，以通过少量数据实现 MHC 等位基因更好的泛化性。MHC 等位基因嵌入的可视化证实该模型能够对具有相似结合特异性的等位基因进行分组，包括训练数据集中没有肽配体的等位基因。此外，外部评估表明 MHCSeqNet2 可以通过少量训练数据提高 MHC 等位基因的 T 细胞表位的优先级。可用性和实施​​ MHCSeqNet2 的源代码和安装说明可在 https://github.com/cmb-chula/MHCSeqNet2 上获取 补充信息 补充数据可在 Bioinformatics online 上获取。