Detailed Information on Publication Record
2022
Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics
VAŠINA, Michal, Pavel VAŇÁČEK, Jiri HON, David KOVÁŘ, Hana FALDYNOVÁ et. al.Basic information
Original name
Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics
Authors
VAŠINA, Michal (203 Czech Republic, belonging to the institution), Pavel VAŇÁČEK (203 Czech Republic, belonging to the institution), Jiri HON, David KOVÁŘ (203 Czech Republic, belonging to the institution), Hana FALDYNOVÁ (203 Czech Republic, belonging to the institution), Antonín KUNKA (203 Czech Republic, belonging to the institution), Tomáš BURYŠKA (203 Czech Republic, belonging to the institution), Christoffel P. S. BADENHORST, Stanislav MAZURENKO (643 Russian Federation, belonging to the institution), David BEDNÁŘ (203 Czech Republic, belonging to the institution), Stavros STAVRAKIS, Uwe T. BORNSCHEUER, Andrew DEMELLO, Jiří DAMBORSKÝ (203 Czech Republic, belonging to the institution) and Zbyněk PROKOP (203 Czech Republic, guarantor, belonging to the institution)
Edition
Chem Catalysis, Elsevier, 2022, 2667-1093
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10608 Biochemistry and molecular biology
Country of publisher
Netherlands
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 9.400
RIV identification code
RIV/00216224:14310/22:00128314
Organization unit
Faculty of Science
UT WoS
000901460400007
Keywords in English
enzyme mining; enzyme diversity; biocatalysts; microfluidics; bioinformatics; global data analysis; haloalkane dehalogenases; bioprospecting
Tags
Tags
International impact, Reviewed
Změněno: 27/1/2023 10:32, Mgr. Marie Šípková, DiS.
Abstract
V originále
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.
Links
EF17_043/0009632, research and development project |
| ||
LM2018121, research and development project |
| ||
LM2018131, research and development project |
| ||
814418, interní kód MU |
| ||
857560, interní kód MU (CEP code: EF17_043/0009632) |
|