Koşaloğlu-Yalçın Zeynep, Lee Jenny, Greenbaum Jason, Schoenberger Stephen P, Miller Aaron, Kim Young J, Sette Alessandro, Nielsen Morten, Peters Bjoern
Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA.
Division of Hematology and Oncology, Center for Personalized Cancer Therapy, San Diego Moore's Cancer Center, University of California, San Diego, San Diego, CA, USA.
iScience. 2022 Feb 18;25(2):103850. doi: 10.1016/j.isci.2022.103850. Epub 2022 Feb 1.
Many steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions.
利用计算方法可以预测MHC I类抗原加工途径的许多步骤。在这里，我们表明，通过考虑肽源蛋白的丰度水平，可以进一步改进表位预测。我们结合生物物理原理、现有的MHC结合预测工具以及源蛋白的丰度估计，推导出一个函数，用于估计肽成为MHC I类配体的可能性。我们发现，这种组合改进了对天然洗脱配体和癌症新抗原表位的预测。我们在SARS-CoV-2表位数据集上比较了不同抗原丰度测量方法的使用情况，包括RNA-Seq的mRNA表达、Ribo-Seq的基因翻译以及蛋白质组学的蛋白质丰度。在所有情况下，表位预测都比单独的结合预测有所改进，并且在使用蛋白质组学数据时表现最佳。我们的结果突出了纳入抗原丰度水平以改进表位预测的价值。
