基于合成生物学策略的酶蛋白元件规模化挖掘

张建志; 付立豪; 唐婷; 张嵩亚; 朱静; 李拓; 王子宁; 司同

doi:10.12211/2096-8280.2020-028

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基于合成生物学策略的酶蛋白元件规模化挖掘

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- 基于合成生物学策略的酶蛋白元件规模化挖掘
  封面论文
- Scalable mining of proteins for biocatalysis via synthetic biology
- 合成生物学 2020年1卷第3期页码：319-336
- 作者机构：
  
  中国科学院深圳先进技术研究院，深圳合成生物学创新研究院，中国科学院定量工程生物学重点实验室，广东　深圳 518055
- 作者简介：
  
  [ "张建志（1988—），男，博士，助理研究员，研究方向为合成生物学、代谢工程。E-mail：zhangjz@siat.ac.cn" ]
  [ "司同（1987—），男，博士，研究员，研究方向为合成生物学。E-mail：tong.si@siat.ac.cn" ]
- 基金信息：
  
  深圳合成生物学创新研究院主题项目(ZTXM20190002)
- DOI：10.12211/2096-8280.2020-028
  中图分类号： Q55
- 收稿：2020-03-17，
  
  修回：2020-04-29，
  
  纸质出版：2020-06-30
- 稿件说明：
移动端阅览
张建志, 付立豪, 唐婷, 张嵩亚, 朱静, 李拓, 王子宁, 司同. 基于合成生物学策略的酶蛋白元件规模化挖掘[J]. 合成生物学, 2020, 1(3): 319-336

ZHANG Jianzhi, FU Lihao, TANG Ting, ZHANG Songya, ZHU Jing, LI Tuo, WANG Zining, SI Tong. Scalable mining of proteins for biocatalysis via synthetic biology[J]. Synthetic Biology Journal, 2020, 1(3): 319-336
张建志, 付立豪, 唐婷, 张嵩亚, 朱静, 李拓, 王子宁, 司同. 基于合成生物学策略的酶蛋白元件规模化挖掘[J]. 合成生物学, 2020, 1(3): 319-336 DOI： 10.12211/2096-8280.2020-028.

ZHANG Jianzhi, FU Lihao, TANG Ting, ZHANG Songya, ZHU Jing, LI Tuo, WANG Zining, SI Tong. Scalable mining of proteins for biocatalysis via synthetic biology[J]. Synthetic Biology Journal, 2020, 1(3): 319-336 DOI： 10.12211/2096-8280.2020-028.

摘要

生物制造以人工生物体系为催化剂合成工业化学品、药物和功能材料，具有低碳循环、绿色清洁等特征。酶蛋白是构建生物催化系统的重要功能单元，然而，由于缺乏准确预测序列-功能关系的方法，目前酶的理性设计仍面临巨大挑战。因此，需要利用合成生物学工程化的思路和手段，从自然界中大规模挖掘新的酶蛋白元件，相关研究不但可以为开发工业酶制剂和构建细胞合成代谢提供优质元件，而且有利于快速获得酶蛋白序列-结构-功能间的对应关系，为建立预测与设计模型提供基础。本文针对酶元件工程化挖掘的关键技术进行综述：介绍了计算机辅助设计的算法和软件，用于将数据库中海量的酶蛋白序列按照实验目的进行聚类分析和优先化排序；总结了规模化合成组装、异源表达和功能筛选酶蛋白元件的高通量实验技术；讨论了如何综合利用计算与实验手段，系统性探索酶家族成员的催化性能。未来，通过综合计算机辅助设计、自动化合成生物构建、高通量测试等方法，设计和建设高度集成的工程化研究平台，成为实现对酶蛋白资源进行系统化的研究和挖掘的重要方向。

Abstract

Biomanufacturing provides a sustainable alternative to traditional petrochemical processes in producing chemicals

drugs

and functional materials. Enzymes are cores for creating catalytic biosystems with diverse functions. Due to the lack of predictive models for enzyme functions

however

rational design is still challenging. On the other hand

next-generation sequencing reveals millions of diverse natural enzymes

of which only a tiny fraction have been experimentally characterized. Synthetic biology applies engineering principles to study

engineer

and create biological systems. Through standardization and modularization

synthetic biology enables large-scale prototyping of enzyme sequences

which not only helps to identify efficient biocatalytic parts

but also accelerates quantitative understanding of sequence-structure-function relationship. Here we review recent advances in scalable mining of enzymes

via

synthetic biology. We firstly introduce computational tools for functional clustering and prioritization of promising sequences from enormous genome/protein databases

followed by experimental approaches for high-throughput cloning

expression

and characterization of selected candidates. We then discuss the applications of such tools in systematic studies of enzyme (super) families. We conclude with future perspectives in creating integrated synthetic biology foundries to accelerate enzyme mining.

关键词

Keywords

references

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