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基于计算机预测严重急性呼吸综合征冠状病毒 2 主蛋白酶切割位点。

In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites.

机构信息

School of Biosciences, University of Exeter, Exeter, UK.

出版信息

Proteins. 2022 Mar;90(3):791-801. doi: 10.1002/prot.26274. Epub 2021 Nov 12.

Abstract

One of the emerging subjects to combat the SARS-CoV-2 virus is to design accurate and efficient drug such as inhibitors against the viral protease to stop the viral spread. In addition to laboratory investigation of the viral protease, which is fundamental, the in silico research of viral protease such as the protease cleavage site prediction is critically important and urgent. However, this problem has yet to be addressed. This article has, for the first time, investigated this problem using the pattern recognition approaches. The article has shown that the pattern recognition approaches incorporating a specially tailored kernel function for dealing with amino acids has the outstanding performance in the accuracy of cleavage site prediction and the discovery of the prototype cleavage peptides.

摘要

对抗 SARS-CoV-2 病毒的新兴课题之一是设计准确有效的药物,如针对病毒蛋白酶的抑制剂,以阻止病毒传播。除了实验室对病毒蛋白酶的研究是基础之外,对病毒蛋白酶的计算机研究,如蛋白酶切割位点预测,也是至关重要和紧迫的。然而,这个问题尚未得到解决。本文首次使用模式识别方法对此问题进行了研究。文章表明,在处理氨基酸时采用特殊设计的核函数的模式识别方法,在切割位点预测的准确性和原型切割肽的发现方面具有出色的性能。

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本文引用的文献

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