Publication Details

Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics

VASINA Michal, VANACEK Pavel, HON Jiří, KOVAR David, FALDYNOVA Hana, KUNKA Antonín, BURYSKA Tomas, BADENHORST Christoffel, MAZURENKO Stanislav, BEDNÁŘ David, STAVRAKIS Stavros, BORNSCHEUER Uwe, DEMELLO Andrew, DAMBORSKÝ Jiří and PROKOP Zbyněk. Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics. Chem Catalysis, vol. 2, no. 10, 2022, pp. 2704-2725. ISSN 2667-1093. Available from: https://www.cell.com/chem-catalysis/fulltext/S2667-1093(22)00503-6
Czech title
Pokročilé dolování databáze účinných haloalkanových dehalogenů sekvenční a strukturní bionofrmatikou a mikrofluidy
Type
journal article
Language
english
Authors
Vasina Michal (FNUSA)
Vanacek Pavel (FNUSA)
Hon Jiří, Ing., Ph.D. (DIFS FIT BUT)
Kovar David (FNUSA)
Faldynova Hana (FNUSA)
Kunka Antonín, Mgr., Ph.D. (LL)
Buryska Tomas (FNUSA)
Badenhorst Christoffel ()
Mazurenko Stanislav, Ph.D. (LL)
Bednář David, Mgr. (LL)
Stavrakis Stavros ()
Bornscheuer Uwe ()
Demello Andrew ()
Damborský Jiří, prof. Mgr., Dr. (LL)
Prokop Zbyněk, doc. RnDr., Ph.D. (LL)
URL
Keywords

biocatalysts; bioinformatics; bioprospecting; enzyme diversity; enzyme mining; global data analysis; haloalkane dehalogenases; microfluidics;

Abstract

Next-generation sequencing doubles genomic databases every 2.5 years. The accumulation of sequence data provides a unique opportunity to identify interesting biocatalysts directly in the databases without tedious and time-consuming engineering. Herein, we present a pipeline integrating sequence and structural bioinformatics with microfluidic enzymology for bioprospecting of efficient and robust haloalkane dehalogenases. The bioinformatic part identified 2,905 putative dehalogenases and prioritized a "small-but-smart'' set of 45 genes, yielding 40 active enzymes, 24 of which were biochemically characterized by microfluidic enzymology techniques. Combining microfluidics with modern global data analysis provided precious mechanistic insights related to the high catalytic efficiency of selected enzymes. Overall, we have doubled the dehalogenation "toolbox'' characterized over three decades, yielding biocatalysts that surpass the efficiency of currently available wild-type and engineered enzymes. This pipeline is generally applicable to other enzyme families and can accelerate the identification of efficient biocatalysts for industrial use.

Published
2022
Pages
2704-2725
Journal
Chem Catalysis, vol. 2, no. 10, ISSN 2667-1093
Publisher
Elsevier Science
DOI
10.1016/j.checat.2022.09.011
UT WoS
000901460400007
EID Scopus
2-s2.0-85140056855
BibTeX 
@ARTICLE{FITPUB12935,
   author = "Michal Vasina and Pavel Vanacek and Ji\v{r}\'{i} Hon and David Kovar and Hana Faldynova and Anton\'{i}n Kunka and Tomas Buryska and Christoffel Badenhorst and Stanislav Mazurenko and David Bedn\'{a}\v{r} and Stavros Stavrakis and Uwe Bornscheuer and Andrew Demello and Ji\v{r}\'{i} Damborsk\'{y} and Zbyn\v{e}k Prokop",
   title = "Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics",
   pages = "2704--2725",
   journal = "Chem Catalysis",
   volume = 2,
   number = 10,
   year = 2022,
   ISSN = "2667-1093",
   doi = "10.1016/j.checat.2022.09.011",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/12935"
}
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