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通过蛋白酶体切割位点预测鉴定 CD8 T 细胞表位。

Identification of CD8 T cell epitopes through proteasome cleavage site predictions.

机构信息

Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, Complutense University of Madrid, Pza Ramon y Cajal, s/n, 28040, Madrid, Spain.

出版信息

BMC Bioinformatics. 2020 Dec 14;21(Suppl 17):484. doi: 10.1186/s12859-020-03782-1.

Abstract

BACKGROUND

We previously introduced PCPS (Proteasome Cleavage Prediction Server), a web-based tool to predict proteasome cleavage sites using n-grams. Here, we evaluated the ability of PCPS immunoproteasome cleavage model to discriminate CD8 T cell epitopes.

RESULTS

We first assembled an epitope dataset consisting of 844 unique virus-specific CD8 T cell epitopes and their source proteins. We then analyzed cleavage predictions by PCPS immunoproteasome cleavage model on this dataset and compared them with those provided by a related method implemented by NetChop web server. PCPS was clearly superior to NetChop in term of sensitivity (0.89 vs. 0.79) but somewhat inferior with regard to specificity (0.55 vs. 0.60). Judging by the Mathew's Correlation Coefficient, PCPS predictions were overall superior to those provided by NetChop (0.46 vs. 0.39). We next analyzed the power of C-terminal cleavage predictions provided by the same PCPS model to discriminate CD8 T cell epitopes, finding that they could be discriminated from random peptides with an accuracy of 0.74. Following these results, we tuned the PCPS web server to predict CD8 T cell epitopes and predicted the entire SARS-CoV-2 epitope space.

CONCLUSIONS

We report an improved version of PCPS named iPCPS for predicting proteasome cleavage sites and peptides with CD8 T cell epitope features. iPCPS is available for free public use at https://imed.med.ucm.es/Tools/pcps/ .

摘要

背景

我们之前介绍了 PCPS(蛋白酶体切割预测服务器),这是一个使用 n-gram 预测蛋白酶体切割位点的基于网络的工具。在这里,我们评估了 PCPS 免疫蛋白酶体切割模型区分 CD8 T 细胞表位的能力。

结果

我们首先组装了一个由 844 个独特的病毒特异性 CD8 T 细胞表位及其来源蛋白组成的表位数据集。然后,我们分析了该数据集上 PCPS 免疫蛋白酶体切割模型的切割预测,并将其与 NetChop 网络服务器实现的相关方法提供的预测进行了比较。PCPS 在灵敏度方面明显优于 NetChop(0.89 对 0.79),但在特异性方面略逊一筹(0.55 对 0.60)。根据马修斯相关系数,PCPS 的预测总体上优于 NetChop(0.46 对 0.39)。接下来,我们分析了相同 PCPS 模型提供的 C 末端切割预测区分 CD8 T 细胞表位的能力,发现它们可以与随机肽区分开来,准确率为 0.74。基于这些结果,我们对 PCPS 网络服务器进行了调整,以预测 CD8 T 细胞表位,并预测了整个 SARS-CoV-2 表位空间。

结论

我们报告了一个名为 iPCPS 的 PCPS 改进版本,用于预测蛋白酶体切割位点和具有 CD8 T 细胞表位特征的肽。iPCPS 可在 https://imed.med.ucm.es/Tools/pcps/ 免费供公众使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ab2/7734783/999a8d4a4407/12859_2020_3782_Fig1_HTML.jpg

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