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

J 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:

URL

Impact factor

Impact factor: 9.400

RIV identification code

RIV/00216224:14310/22:00128314

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1016/j.checat.2022.09.011

UT WoS

000901460400007

Keywords in English

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

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

Name: CETOCOEN Excellence

LM2018121, research and development project

Name: Výzkumná infrastruktura RECETOX (Acronym: RECETOX RI)

Investor: Ministry of Education, Youth and Sports of the CR, RECETOX RI

LM2018131, research and development project

Name: Česká národní infrastruktura pro biologická data (Acronym: ELIXIR-CZ)

Investor: Ministry of Education, Youth and Sports of the CR, Czech National Infrastructure for Biological Data

814418, interní kód MU

Name: Synthetic biology-guided engineering of Pseudomonas putida for biofluorination (Acronym: SinFonia)

Investor: European Union, Leadership in enabling and industrial technologies (LEIT) (Industrial Leadership)

857560, interní kód MU
(CEP code: EF17_043/0009632)

Name: CETOCOEN Excellence (Acronym: CETOCOEN Excellence)

Investor: European Union, Spreading excellence and widening participation

Citovat

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.

@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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