Ma Fuqiang, Xie Yuan, Luo Manjie, Wang Shuhao, Hu You, Liu Yukun, Feng Yan, Yang Guang-Yu
State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
School of Statistics, East China Normal University, Shanghai 200241, China.
Synth Syst Biotechnol. 2016 Oct 4;1(3):195-206. doi: 10.1016/j.synbio.2016.09.001. eCollection 2016 Sep.
Cell-free synthetic biology system organizes multiple enzymes (parts) from different sources to implement unnatural catalytic functions. Highly adaption between the catalytic parts is crucial for building up efficient artificial biosynthetic systems. Protein engineering is a powerful technology to tailor various enzymatic properties including catalytic efficiency, substrate specificity, temperature adaptation and even achieve new catalytic functions. However, altering enzymatic pH optimum still remains a challenging task. In this study, we proposed a novel sequence homolog-based protein engineering strategy for shifting the enzymatic pH optimum based on statistical analyses of sequence-function relationship data of enzyme family. By two statistical procedures, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (Lasso), five amino acids in GH11 xylanase family were identified to be related to the evolution of enzymatic pH optimum. Site-directed mutagenesis of a thermophilic xylanase from revealed that four out of five mutations could alter the enzymatic pH optima toward acidic condition without compromising the catalytic activity and thermostability. Combination of the positive mutants resulted in the best mutant M31 that decreased its pH optimum for 1.5 units and showed increased catalytic activity at pH < 5.0 compared to the wild-type enzyme. Structure analysis revealed that all the mutations are distant from the active center, which may be difficult to be identified by conventional rational design strategy. Interestingly, the four mutation sites are clustered at a certain region of the enzyme, suggesting a potential "hot zone" for regulating the pH optima of xylanases. This study provides an efficient method of modulating enzymatic pH optima based on statistical sequence analyses, which can facilitate the design and optimization of suitable catalytic parts for the construction of complicated cell-free synthetic biology systems.
无细胞合成生物学系统将来自不同来源的多种酶（部件）组织起来，以实现非天然的催化功能。催化部件之间的高度适配对于构建高效的人工生物合成系统至关重要。蛋白质工程是一种强大的技术，可用于定制各种酶的特性，包括催化效率、底物特异性、温度适应性，甚至实现新的催化功能。然而，改变酶的最适pH值仍然是一项具有挑战性的任务。在本研究中，我们基于对酶家族序列-功能关系数据的统计分析，提出了一种基于序列同源性的新型蛋白质工程策略，用于改变酶的最适pH值。通过人工神经网络（ANNs）和最小绝对收缩和选择算子（Lasso）这两种统计程序，确定了GH11木聚糖酶家族中的五个氨基酸与酶最适pH值的演变有关。对一种嗜热木聚糖酶进行定点诱变，结果表明五个突变中有四个可以在不影响催化活性和热稳定性的情况下，使酶的最适pH值向酸性条件转变。阳性突变体的组合产生了最佳突变体M31，其最适pH值降低了1.5个单位，并且与野生型酶相比，在pH < 5.0时显示出更高的催化活性。结构分析表明，所有突变都远离活性中心，这可能难以通过传统的理性设计策略来识别。有趣的是，四个突变位点聚集在酶的某个区域，表明存在一个调节木聚糖酶最适pH值的潜在“热点区域”。本研究提供了一种基于序列统计分析来调节酶最适pH值的有效方法，这有助于设计和优化用于构建复杂无细胞合成生物学系统的合适催化部件。
