MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing
MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing
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DOI：
10.1016/j.cels.2020.06.010
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发表时间：
2020-07-22
期刊：
影响因子：
9.3
通讯作者：
Laserson, Uri
中科院分区：
文献类型：
--
作者：
O'Donnell, Timothy J.;Rubinsteyn, Alex;Laserson, Uri
主要组织相容性复合体（MHC）I类蛋白上呈递肽的计算预测是研究T细胞免疫的重要工具。可用于开发此类预测因子的数据已经随着质谱法的使用而扩展，以鉴定天然呈递的MHC配体。除了阐明结合基序之外，所鉴定的配体还反映了在MHC结合之前发生的抗原加工步骤。在这里，我们开发了一个综合预测的MHC I类呈递，结合新的模型MHC I类结合和抗原处理。仅考虑结合模型首先预测的肽与MHC强烈结合，训练抗原处理模型以区分已发表的质谱鉴定的MHC I类配体与未观察到的肽。集成模型优于两个单独的组件，以及NetMHCpan 4.0和MixMHCpred 2.0.2的质谱实验。我们的预测器在开源MHCflurry包2.0版（github.com/openvax/mhcflurry）中实现。
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).