Improved predictive models of peptide presentation on MHC I

Large surveys of peptides naturally presented on major histocompatibility class I (MHC I) proteins have enabled improved MHC I ligand prediction by dramatically expanding the available data for many MHC I alleles. However, it is unclear to what extent antigen processing signals can also be learned from these datasets. Here, we developed a predictor of antigen processing by training neural networks to discriminate mass spec-identified MHC I ligands from unobserved peptides, where both classes of peptides are predicted to be strong MHC I binders. The resulting predictor shows qualitative consistency with established preferences for the transporter associated with antigen processing, proteasomal cleavage, and endoplasmic reticulum aminopeptidases. When we combined the antigen processing predictor with a novel pan-allele MHC I binding predictor in a logistic regression model, the combination model significantly outperformed the two components alone as well as the NetMHCpan 4.0 and MixMHCpred 2.0.2 tools at predicting mass spec-identified MHC I ligands. Our predictors are implemented in the open source MHCflurry package, version 1.6.0 (github.com/openvax/mhcflurry).针对主要组织相容性复合体I类（major histocompatibility class I, MHC I）蛋白天然呈递的肽段开展的大规模研究，通过极大扩充众多MHC I等位基因的可用数据集，推动了MHC I配体预测模型的性能优化。然而，目前尚不明确此类数据集能否在同等程度上支持抗原加工相关信号的学习。本研究中，我们通过训练神经网络区分两类肽段——经质谱（mass spec）鉴定的MHC I配体与未被观测到的肽段，构建了一款抗原加工预测器；两类肽段均被预测为强效MHC I结合肽段。所得预测器与已报道的抗原加工相关转运体、蛋白酶体切割及内质网氨肽酶的底物偏好性特征具有定性一致性。将该抗原加工预测器与一款新型泛等位基因MHC I结合预测器整合至逻辑回归模型后，该组合模型在预测质谱鉴定的MHC I配体任务中，性能显著优于两个单一组件，同时也优于NetMHCpan 4.0与MixMHCpred 2.0.2两款工具。本研究的预测器已集成至开源工具包MHCflurry 1.6.0（github.com/openvax/mhcflurry）中。