Quantitative Analysis of the Substrate Specificity of Human Rhinovirus 3C Protease and Exploration of Its Substrate Recognition Mechanisms

蛋白酶 鼻病毒 基质(水族馆) 化学 生物 底物特异性 计算生物学 病毒学 生物化学 病毒 生态学
作者
Xian Fan,Xinzhi Li,Yu Zhou,Meng Mei,Pi Liu,Jing Zhao,Wenfang Peng,Zhengbing Jiang,Shihui Yang,Brent L. Iverson,Guimin Zhang,Yi Li
出处
期刊:ACS Chemical Biology [American Chemical Society]
卷期号:15 (1): 63-73 被引量:22
标识
DOI:10.1021/acschembio.9b00539
摘要

Human rhinovirus 3C protease (HRV 3C-P) is a high-value commercial cysteine protease that could specifically recognize the short peptide sequence of LEVLFQ↓GP. In here, a strategy based on our previous Yeast Endoplasmic Reticulum Sequestration Screening (YESS) approach was developed in Saccharomyces cerevisiae, a model microorganism, to fully characterize the substrate specificity of a typical human virus protease, HRV 3C-P, in a quantitative and fast manner. Our results demonstrated that HRV 3C-P had very high specificity at P1 and P1' positions, only recognizing Gln/Glu at the P1 position and Gly/Ala/Cys/Ser at the P1' position, respectively. Comparably, it exhibited efficient recognition of most residues at the P2' position, except Trp. Further biochemical characterization through site mutagenesis, enzyme structural modeling, and comparison with other 3C proteases indicated that the S1 pocket of HRV 3C-P was constituted by neutral and basic amino acids, in which His160 and Thr141 specifically interacted with Gln or Glu residues at the substrate P1 position. Additionally, the stringent S1' pocket determined its unique property of only accommodating residues without or with short side chains. Based on our characterization, LEVLFQ↓GM was identified as a more favorable substrate than the original LEVLFQ↓GP at high temperature, which might be caused by the conversion of random coils to β-turns in HRV 3C-P along with the temperature increase. Our studies prompted a further understanding of the substrate specificity and recognition mechanism of HRV 3C-P. Besides, the YESS-PSSC combined with the enzyme modeling strategy in this study provides a general strategy for deciphering the substrate specificities of proteases.
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