Literature DB >> 33540582

Biosensor-Based Directed Evolution of Methanol Dehydrogenase from Lysinibacillus xylanilyticus.

Thien-Kim Le1, Su-Bin Ju1,2, Hye-Won Lee3, Jin-Young Lee3, So-Hyung Oh3,4, Kil-Koang Kwon3, Bong-Hyun Sung3,4, Seung-Goo Lee3,4, Soo-Jin Yeom1,2.   

Abstract

Methanol dehydrogenase (Mdh), is a crucial enzyme for utilizing methane and methanol as carbon and energy sources in methylotrophy and synthetic methylotrophy. Engineering of Mdh, especially NAD-dependent Mdh, has thus been actively investigated to enhance methanol conversion. However, its poor catalytic activity and low methanol affinity limit its wider application. In this study, we applied a transcriptional factor-based biosensor for the direct evolution of Mdh from Lysinibacillus xylanilyticus (Lxmdh), which has a relatively high turnover rate and low KM value compared to other wild-type NAD-dependent Mdhs. A random mutant library of Lxmdh was constructed in Escherichia coli and was screened using formaldehyde-detectable biosensors by incubation with low methanol concentrations. Positive clones showing higher fluorescence were selected by fluorescence-activated cell sorting (FACS) system, and their catalytic activities toward methanol were evaluated. The successfully isolated mutants E396V, K318N, and K46E showed high activity, particularly at very low methanol concentrations. In kinetic analysis, mutant E396V, K318N, and K46E had superior methanol conversion efficiency, with 79-, 23-, and 3-fold improvements compared to the wild-type, respectively. These mutant enzymes could thus be useful for engineering synthetic methylotrophy and for enhancing methanol conversion to various useful products.

Entities:  

Keywords:  Lysinibacillus xylanilyticus; biosensor; methanol dehydrogenase; screening; synthetic methylotrophy

Mesh:

Substances:

Year:  2021        PMID: 33540582      PMCID: PMC7867188          DOI: 10.3390/ijms22031471

Source DB:  PubMed          Journal:  Int J Mol Sci        ISSN: 1422-0067            Impact factor:   5.923


  19 in total

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Journal:  ACS Synth Biol       Date:  2016-08-09       Impact factor: 5.110

2.  Development of a novel cellulase biosensor that detects crystalline cellulose hydrolysis using a transcriptional regulator.

Authors:  Kil Koang Kwon; Soo-Jin Yeom; Dae-Hee Lee; Ki Jun Jeong; Seung-Goo Lee
Journal:  Biochem Biophys Res Commun       Date:  2017-11-24       Impact factor: 3.575

3.  Engineering the biological conversion of methanol to specialty chemicals in Escherichia coli.

Authors:  W Brian Whitaker; J Andrew Jones; R Kyle Bennett; Jacqueline E Gonzalez; Victoria R Vernacchio; Shannon M Collins; Michael A Palmer; Samuel Schmidt; Maciek R Antoniewicz; Mattheos A Koffas; Eleftherios T Papoutsakis
Journal:  Metab Eng       Date:  2016-11-01       Impact factor: 9.783

4.  Construction of Methanol-Sensing Escherichia coli by the Introduction of a Paracoccus denitrificans MxaY-Based Chimeric Two-Component System.

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Journal:  J Microbiol Biotechnol       Date:  2017-06-28       Impact factor: 2.351

5.  Identification of a magnesium-dependent NAD(P)(H)-binding domain in the nicotinoprotein methanol dehydrogenase from Bacillus methanolicus.

Authors:  Harm J Hektor; Harm Kloosterman; Lubbert Dijkhuizen
Journal:  J Biol Chem       Date:  2002-09-25       Impact factor: 5.157

6.  Characterization and evolution of an activator-independent methanol dehydrogenase from Cupriavidus necator N-1.

Authors:  Tung-Yun Wu; Chang-Ting Chen; Jessica Tse-Jin Liu; Igor W Bogorad; Robert Damoiseaux; James C Liao
Journal:  Appl Microbiol Biotechnol       Date:  2016-02-05       Impact factor: 4.813

7.  Evolution of enzymes with new specificity by high-throughput screening using DmpR-based genetic circuits and multiple flow cytometry rounds.

Authors:  Kil Koang Kwon; Dae-Hee Lee; Su Jin Kim; Su-Lim Choi; Eugene Rha; Soo-Jin Yeom; Bindu Subhadra; Jinhyuk Lee; Ki Jun Jeong; Seung-Goo Lee
Journal:  Sci Rep       Date:  2018-02-08       Impact factor: 4.379

8.  A synthetic microbial biosensor for high-throughput screening of lactam biocatalysts.

Authors:  Soo-Jin Yeom; Moonjeong Kim; Kil Koang Kwon; Yaoyao Fu; Eugene Rha; Sung-Hyun Park; Hyewon Lee; Haseong Kim; Dae-Hee Lee; Dong-Myung Kim; Seung-Goo Lee
Journal:  Nat Commun       Date:  2018-11-29       Impact factor: 14.919

9.  Development of a formaldehyde biosensor with application to synthetic methylotrophy.

Authors:  Benjamin M Woolston; Timothy Roth; Ishwar Kohale; David R Liu; Gregory Stephanopoulos
Journal:  Biotechnol Bioeng       Date:  2017-11-03       Impact factor: 4.530

10.  Methylotrophic Bacillus methanolicus encodes two chromosomal and one plasmid born NAD+ dependent methanol dehydrogenase paralogs with different catalytic and biochemical properties.

Authors:  Anne Krog; Tonje M B Heggeset; Jonas E N Müller; Christiane E Kupper; Olha Schneider; Julia A Vorholt; Trond E Ellingsen; Trygve Brautaset
Journal:  PLoS One       Date:  2013-03-19       Impact factor: 3.240

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  4 in total

1.  Cheating the Cheater: Suppressing False-Positive Enrichment during Biosensor-Guided Biocatalyst Engineering.

Authors:  Vikas D Trivedi; Karishma Mohan; Todd C Chappell; Zachary J S Mays; Nikhil U Nair
Journal:  ACS Synth Biol       Date:  2021-12-16       Impact factor: 5.249

Review 2.  Methanol Dehydrogenases as a Key Biocatalysts for Synthetic Methylotrophy.

Authors:  Thien-Kim Le; Yu-Jin Lee; Gui Hwan Han; Soo-Jin Yeom
Journal:  Front Bioeng Biotechnol       Date:  2021-12-24

3.  In Vitro One-Pot 3-Hydroxypropanal Production from Cheap C1 and C2 Compounds.

Authors:  Su-Bin Ju; Min-Ju Seo; Soo-Jin Yeom
Journal:  Int J Mol Sci       Date:  2022-04-03       Impact factor: 5.923

4.  Phage-Assisted Continuous Evolution and Selection of Enzymes for Chemical Synthesis.

Authors:  Krysten A Jones; Harrison M Snodgrass; Ketaki Belsare; Bryan C Dickinson; Jared C Lewis
Journal:  ACS Cent Sci       Date:  2021-09-13       Impact factor: 14.553

  4 in total

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