| VAŠINA, Michal, Pavel VAŇÁČEK, Jiri HON, David KOVÁŘ, Hana FALDYNOVÁ, Antonín KUNKA, Tomáš BURYŠKA, Christoffel P. S. BADENHORST, Stanislav MAZURENKO, David BEDNÁŘ, Stavros STAVRAKIS, Uwe T. BORNSCHEUER, Andrew DEMELLO, Jiří DAMBORSKÝ and Zbyněk PROKOP. Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics. Chem Catalysis. Elsevier, 2022, vol. 2, No 10, p. 2704-2725. ISSN 2667-1093. Available from: https://dx.doi.org/10.1016/j.checat.2022.09.011. |
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@article{2250027,
author = {Vašina, Michal and Vaňáček, Pavel and Hon, Jiri and Kovář, David and Faldynová, Hana and Kunka, Antonín and Buryška, Tomáš and Badenhorst, Christoffel P. S. and Mazurenko, Stanislav and Bednář, David and Stavrakis, Stavros and Bornscheuer, Uwe T. and deMello, Andrew and Damborský, Jiří and Prokop, Zbyněk},
article_number = {10},
doi = {http://dx.doi.org/10.1016/j.checat.2022.09.011},
keywords = {enzyme mining; enzyme diversity; biocatalysts; microfluidics; bioinformatics; global data analysis; haloalkane dehalogenases; bioprospecting},
language = {eng},
issn = {2667-1093},
journal = {Chem Catalysis},
title = {Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics},
url = {https://doi.org/10.1016/j.checat.2022.09.011},
volume = {2},
year = {2022}
}
TY - JOUR
ID - 2250027
AU - Vašina, Michal - Vaňáček, Pavel - Hon, Jiri - Kovář, David - Faldynová, Hana - Kunka, Antonín - Buryška, Tomáš - Badenhorst, Christoffel P. S. - Mazurenko, Stanislav - Bednář, David - Stavrakis, Stavros - Bornscheuer, Uwe T. - deMello, Andrew - Damborský, Jiří - Prokop, Zbyněk
PY - 2022
TI - Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics
JF - Chem Catalysis
VL - 2
IS - 10
SP - 2704-2725
EP - 2704-2725
PB - Elsevier
SN - 26671093
KW - enzyme mining
KW - enzyme diversity
KW - biocatalysts
KW - microfluidics
KW - bioinformatics
KW - global data analysis
KW - haloalkane dehalogenases
KW - bioprospecting
UR - https://doi.org/10.1016/j.checat.2022.09.011
N2 - 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.
ER -
VAŠINA, Michal, Pavel VAŇÁČEK, Jiri HON, David KOVÁŘ, Hana FALDYNOVÁ, Antonín KUNKA, Tomáš BURYŠKA, Christoffel P. S. BADENHORST, Stanislav MAZURENKO, David BEDNÁŘ, Stavros STAVRAKIS, Uwe T. BORNSCHEUER, Andrew DEMELLO, Jiří DAMBORSKÝ and Zbyněk PROKOP. Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics. \textit{Chem Catalysis}. Elsevier, 2022, vol.~2, No~10, p.~2704-2725. ISSN~2667-1093. Available from: https://dx.doi.org/10.1016/j.checat.2022.09.011.
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