Mori Akihiro, Hara Shoichi, Sugahara Tomohiro, Kojima Takaaki, Iwasaki Yugo, Kawarasaki Yasuaki, Sahara Takehiko, Ohgiya Satoru, Nakano Hideo
Laboratory of Molecular Biotechnology, Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
Biomolecular Engineering Laboratory, School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka 422-8526, Japan.
J Biosci Bioeng. 2015 Nov;120(5):518-25. doi: 10.1016/j.jbiosc.2015.03.003. Epub 2015 Apr 23.
The secretion efficiency of foreign proteins in recombinant microbes is strongly dependent on the combination of the signal peptides (SPs) used and the target proteins; therefore, identifying the optimal SP sequence for each target protein is a crucial step in maximizing the efficiency of protein secretion in both prokaryotes and eukaryotes. In this study, we developed a novel method, named the SP optimization tool (SPOT), for the generation and rapid screening of a library of SP-target gene fusion constructs to identify the optimal SP for maximizing target protein secretion. In contrast to libraries generated in previous studies, SPOT fusion constructs are generated without adding the intervening sequences associated with restriction enzyme digestion sites. Therefore, no extra amino acids are inserted at the N-terminus of the target protein that might affect its function or conformational stability. As a model system, β-galactosidase (LacA) from Aspergillus oryzae was used as a target protein for secretion from Saccharomyces cerevisiae. In total, 60 SPs were selected from S. cerevisiae secretory proteins and utilized to generate the SP library. While many of the SP-LacA fusions were not secreted, several of the SPs, AGA2, CRH1, PLB1, and MF(alpha)1, were found to enhance LacA secretion compared to the WT sequence. Our results indicate that SPOT is a valuable method for optimizing the bioproduction of any target protein, and could be adapted to many host strains.
重组微生物中外源蛋白的分泌效率在很大程度上取决于所使用的信号肽(SPs)与目标蛋白的组合;因此,为每个目标蛋白确定最佳的SP序列是提高原核生物和真核生物中蛋白分泌效率的关键步骤。在本研究中,我们开发了一种名为SP优化工具(SPOT)的新方法,用于生成和快速筛选SP-靶基因融合构建体文库,以确定使目标蛋白分泌最大化的最佳SP。与以往研究中生成的文库不同,SPOT融合构建体的生成无需添加与限制酶切割位点相关的间隔序列。因此,不会在目标蛋白的N端插入可能影响其功能或构象稳定性的额外氨基酸。作为一个模型系统,米曲霉的β-半乳糖苷酶(LacA)被用作从酿酒酵母中分泌的目标蛋白。总共从酿酒酵母分泌蛋白中选择了60个SPs,并用于生成SP文库。虽然许多SP-LacA融合蛋白未被分泌,但与野生型序列相比,发现一些SPs,即AGA2、CRH1、PLB1和MF(alpha)1,可增强LacA的分泌。我们的结果表明,SPOT是优化任何目标蛋白生物生产的一种有价值的方法,并且可以适用于许多宿主菌株。