Computational prediction of the peptides presented on major histocompatibility complex (MHC) class I proteins is an important tool for studying T cell immunity. The data available to develop such predictors have expanded with the use of mass spectrometry to identify naturally presented MHC ligands. In addition to elucidating binding motifs, the identified ligands also reflect the antigen processing steps that occur prior to MHC binding. Here, we developed an integrated predictor of MHC class I presentation that combines new models for MHC class I binding and antigen processing. Considering only peptides first predicted by the binding model to bind strongly to MHC, the antigen processing model is trained to discriminate published mass spectrometry-identified MHC class I ligands from unobserved peptides. The integrated model outperformed the two individual components as well as NetMHCpan 4.0 and MixMHCpred 2.0.2 on held-out mass spectrometry experiments. Our predictors are implemented in the open source MHCflurry package, version 2.0 (github.com/openvax/mhcflurry).

主要组织相容性复合体 (MHC) I 类蛋白质上呈递的肽的计算预测是研究 T 细胞免疫的重要工具。随着质谱法的使用，可用于开发此类预测因子的数据已扩展到鉴定天然存在的 MHC 配体。除了阐明结合基序外，鉴定的配体还反映了 MHC 结合之前发生的抗原加工步骤。在这里，我们开发了 MHC I 类呈递的综合预测器，它结合了 MHC I 类结合和抗原处理的新模型。仅考虑由结合模型首先预测与 MHC 强结合的肽，训练抗原处理模型以区分已发表的质谱鉴定的 MHC I 类配体和未观察到的肽。在保留的质谱实验中，集成模型的性能优于两个单独的组件以及 NetMHCpan 4.0 和 MixMHCpred 2.0.2。我们的预测器在开源 MHCflurry 包 2.0 版 (github.com/openvax/mhcflurry) 中实现。