Literature DB >> 29181567

From molecular engineering to process engineering: development of high-throughput screening methods in enzyme directed evolution.

Lidan Ye1, Chengcheng Yang1, Hongwei Yu2.   

Abstract

With increasing concerns in sustainable development, biocatalysis has been recognized as a competitive alternative to traditional chemical routes in the past decades. As nature's biocatalysts, enzymes are able to catalyze a broad range of chemical transformations, not only with mild reaction conditions but also with high activity and selectivity. However, the insufficient activity or enantioselectivity of natural enzymes toward non-natural substrates limits their industrial application, while directed evolution provides a potent solution to this problem, thanks to its independence on detailed knowledge about the relationship between sequence, structure, and mechanism/function of the enzymes. A proper high-throughput screening (HTS) method is the key to successful and efficient directed evolution. In recent years, huge varieties of HTS methods have been developed for rapid evaluation of mutant libraries, ranging from in vitro screening to in vivo selection, from indicator addition to multi-enzyme system construction, and from plate screening to computation- or machine-assisted screening. Recently, there is a tendency to integrate directed evolution with metabolic engineering in biosynthesis, using metabolites as HTS indicators, which implies that directed evolution has transformed from molecular engineering to process engineering. This paper aims to provide an overview of HTS methods categorized based on the reaction principles or types by summarizing related studies published in recent years including the work from our group, to discuss assay design strategies and typical examples of HTS methods, and to share our understanding on HTS method development for directed evolution of enzymes involved in specific catalytic reactions or metabolic pathways.

Entities:  

Keywords:  Biocatalysis; Biosynthesis; Categorization; Directed evolution; High-throughput screening method

Mesh:

Substances:

Year:  2017        PMID: 29181567     DOI: 10.1007/s00253-017-8568-y

Source DB:  PubMed          Journal:  Appl Microbiol Biotechnol        ISSN: 0175-7598            Impact factor:   4.813


  8 in total

Review 1.  Robotics for enzyme technology: innovations and technological perspectives.

Authors:  Mandeep Dixit; Kusum Panchal; Dharini Pandey; Nikolaos E Labrou; Pratyoosh Shukla
Journal:  Appl Microbiol Biotechnol       Date:  2021-05-10       Impact factor: 4.813

Review 2.  High-throughput droplet-based microfluidics for directed evolution of enzymes.

Authors:  Flora W Y Chiu; Stavros Stavrakis
Journal:  Electrophoresis       Date:  2019-08-29       Impact factor: 3.535

Review 3.  Peculiarities of promiscuous L-threonine transaldolases for enantioselective synthesis of β-hydroxy-α-amino acids.

Authors:  Shan Wang; Hai Deng
Journal:  Appl Microbiol Biotechnol       Date:  2021-04-26       Impact factor: 4.813

4.  Research progress of pathway and genome evolution in microbes.

Authors:  Chaoqun Huang; Chang Wang; Yunzi Luo
Journal:  Synth Syst Biotechnol       Date:  2022-02-14

5.  Substrate multiplexed protein engineering facilitates promiscuous biocatalytic synthesis.

Authors:  Allwin D McDonald; Peyton M Higgins; Andrew R Buller
Journal:  Nat Commun       Date:  2022-09-06       Impact factor: 17.694

6.  Development of an Improved Peroxidase-Based High-Throughput Screening for the Optimization of D-Glycerate Dehydratase Activity.

Authors:  Benjamin Begander; Anna Huber; Manuel Döring; Josef Sperl; Volker Sieber
Journal:  Int J Mol Sci       Date:  2020-01-03       Impact factor: 5.923

7.  Highly thermostable carboxylic acid reductases generated by ancestral sequence reconstruction.

Authors:  Adam Thomas; Rhys Cutlan; William Finnigan; Mark van der Giezen; Nicholas Harmer
Journal:  Commun Biol       Date:  2019-11-22

8.  SynPharm and the guide to pharmacology database: A toolset for conferring drug control on engineered proteins.

Authors:  Jamie A Davies
Journal:  Protein Sci       Date:  2020-11-02       Impact factor: 6.725

  8 in total

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