Liu Yan, Kieslich Chris A, Morikis Dimitrios, Liao Jiayu
Department of Bioengineering, University of California at Riverside, 900 University Avenue, Riverside, CA 92521, USA.
Protein Eng Des Sel. 2014 Apr;27(4):117-26. doi: 10.1093/protein/gzu004.
SUMOylation, one of the most important protein post-translational modifications, plays critical roles in a variety of physiological and pathological processes. SENP (Sentrin/SUMO-specific protease), a family of SUMO-specific proteases, is responsible for the processing of pre-SUMO and removal of SUMO from conjugated substrates. SUMO4, the latest discovered member in the SUMO family, has been found as a type 1 diabetes susceptibility gene and its maturation is not understood so far. Despite the 14 amino acid differences between pre-SUMO4 and SUMO2, pre-SUMO4 is not processed by SENP2 but pre-SUMO2 does. A novel interdisciplinary approach involving computational modeling and a FRET-based protease assay was taken to engineer pre-SUMO4 as a substrate of SENP2. Given the difference in net charge between pre-SUMO4 and pre-SUMO2, the computational framework analysis of electrostatic similarities of proteins was applied to determine the contribution of each ionizable amino acid in a model of SENP2-(pre-SUMO4) binding, and to propose pre-SUMO4 mutations. The specificities of the SENP2 toward different pre-SUMO4 mutants were determined using a quantitative FRET assay by characterizing the catalytic efficiencies (kcat/KM). A single amino acid mutation made pre-SUMO4 amenable to SENP2 processing and a combination of two amino acid mutations made it highly accessible as SENP2 substrate. The combination of the two approaches provides a powerful protein engineering tool for future SUMOylation studies.
SUMO化是最重要的蛋白质翻译后修饰之一,在多种生理和病理过程中发挥关键作用。SENP(Sentrin/SUMO特异性蛋白酶)是SUMO特异性蛋白酶家族,负责前体SUMO的加工以及从缀合底物上去除SUMO。SUMO4是SUMO家族中最新发现的成员,已被发现是1型糖尿病易感基因,其成熟过程至今仍不清楚。尽管前体SUMO4与SUMO2之间有14个氨基酸差异,但前体SUMO4不能被SENP2加工,而前体SUMO2可以。我们采用了一种涉及计算建模和基于荧光共振能量转移(FRET)的蛋白酶检测的新型跨学科方法,将前体SUMO4设计为SENP2的底物。鉴于前体SUMO4与前体SUMO2之间净电荷的差异,应用蛋白质静电相似性的计算框架分析来确定SENP2-(前体SUMO4)结合模型中每个可电离氨基酸的贡献,并提出前体SUMO4的突变。通过表征催化效率(kcat/KM),使用定量FRET检测法确定SENP2对不同前体SUMO4突变体的特异性。单个氨基酸突变使前体SUMO4易于被SENP2加工,两个氨基酸突变的组合使其作为SENP2底物具有高度可及性。这两种方法的结合为未来的SUMO化研究提供了一个强大的蛋白质工程工具。