Chen Fu, Yuan Le, Ding Shaozhen, Tian Yu, Hu Qian-Nan
College of Biotechnology, Tianjin University of Science and Technology, Tianjin, People's Republic of China.
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.
Brief Bioinform. 2020 Jul 15;21(4):1238-1248. doi: 10.1093/bib/bbz065.
A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.
近年来，化学、反应和酶数据库不断增多，出现了用于数据驱动的合理生物合成设计的新计算方法和软件工具。随着大数据时代的到来，尤其是在生物医学领域，数据驱动的合理生物合成设计可能有助于构建面向目标的底盘生物。改造底盘生物复杂的代谢系统，以从廉价生物质中生物合成目标分子，是细胞工厂设计的主要目标。数据驱动的细胞工厂设计过程可分为几个部分：（1）目标分子选择；（2）代谢反应和途径设计；（3）基于蛋白质结构域和生物合成反应的结构转化预测新型酶；（4）构建用于代谢途径的大规模DNA；以及（5）DNA组装方法和可视化工具。构建一站式细胞工厂系统可以实现从分子水平到底盘水平的自动化设计。在本文中，我们概述了数据驱动的合理生物合成设计步骤，并对各个步骤中的相关工具进行了概述。
