Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms

Huiting Chen; Zhaozhong Zhu; Ye Qiu; Xingyi Ge; Heping Zheng; Yousong Peng

doi:10.1016/j.virs.2022.04.006

June 2022

Huiting Chen, Zhaozhong Zhu, Ye Qiu, Xingyi Ge, Heping Zheng and Yousong Peng. Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms[J]. Virologica Sinica, 2022, 37(3): 437-444. doi: 10.1016/j.virs.2022.04.006

Citation: Huiting Chen, Zhaozhong Zhu, Ye Qiu, Xingyi Ge, Heping Zheng, Yousong Peng. Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms .VIROLOGICA SINICA, 2022, 37(3) : 437-444. http://dx.doi.org/10.1016/j.virs.2022.04.006

开发机器学习方法预测冠状病毒3C样蛋白酶的切割位点

湖南大学生物学院, 医学病毒学湖南省重点实验室

通讯作者： 彭友松, pys2013@hnu.edu.cn
收稿日期： 2021-11-01
录用日期： 2022-04-02

摘要

冠状病毒3C样蛋白酶属于半胱氨酸蛋白酶，它在病毒感染和免疫逃逸中发挥重要作用。然而，目前仍然缺少有效的工具用于快速确定该蛋白酶的切割位点。本研究首先系统研究了冠状病毒3C样蛋白酶对于冠状病毒多聚蛋白的切割位点多样性，发现冠状病毒alpha、beta和gamma等属中的酶切位点高度保守，而且在酶切位点附近存在很强的氨基酸偏好性。基于该发现，我们使用氨基酸指数来表征酶切位点附近的氨基酸序列，然后建立了一个随机森林模型来预测冠状病毒3C样蛋白酶的切割位点。该模型在交叉验证中的AUC值达到了0.96，表明该模型具有很好的预测性能。为了进一步评估该模型的预测能力，我们整理了一个单独的测试数据集。该数据集由90种来自多种冠状病毒宿主的蛋白组成，它们都已经被实验证实可以被冠状病毒3C样蛋白酶切割。在该测试数据集上，上述随机森林模型的AUC值为0.95，而且能够预测正确其中80%的酶切位点，表明该模型具有很好的实用性。接下来我们使用该模型预测了1352种人类蛋白可能被冠状病毒的3C样蛋白酶切割，这些蛋白富集到细胞骨架相关的功能，如微管和肌动蛋白等。最后，我们为上述随机森林模型建立了相应的在线服务器，用于帮助预测冠状病毒3C样蛋白酶的切割位点。综上，该研究为冠状病毒3C样蛋白酶的切割位点确定提供了一个快速有效的工具，同时也为揭示冠状病毒致病性的分子机制提供了参考。
- 冠状病毒
- , 3C样蛋白酶
- , 酶切位点
- , 机器学习

Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms

Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China

Corresponding author: Yousong Peng, pys2013@hnu.edu.cn
Received Date: 01 November 2021
Accepted Date: 02 April 2022

Abstract

The coronavirus 3C-like (3CL) protease, a cysteine protease, plays an important role in viral infection and immune escape. However, there is still a lack of effective tools for determining the cleavage sites of the 3CL protease. This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein, and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus, Betacoronavirus and Gammacoronavirus. Strong residue preferences were observed at the neighboring positions of the cleavage sites. A random forest (RF) model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes, and the model achieved an AUC of 0.96 in cross-validations. The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts. It achieved an AUC of 0.95 and predicted correctly 80% of the cleavage sites. Then, 1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model. These proteins were enriched in several GO terms related to the cytoskeleton, such as the microtubule, actin and tubulin. Finally, a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model. Overall, the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.
- Coronavirus
- , 3C-like protease
- , Cleavage sites
- , Machine-learning algorithms

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