In-depth analysis of biocatalysts by microfluidics: An emerging
source of data for machine learning

J 2023

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

VAŠINA, Michal, David KOVÁŘ, Jiří DAMBORSKÝ, Ding YUN, Tianjin YANG et. al.

Basic information

Original name

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Authors

VAŠINA, Michal (203 Czech Republic, belonging to the institution), David KOVÁŘ (203 Czech Republic, belonging to the institution), Jiří DAMBORSKÝ (203 Czech Republic, guarantor, belonging to the institution), Ding YUN, Tianjin YANG, Andrew DE MELLO, Stanislav MAZURENKO (643 Russian Federation, belonging to the institution), Stavros STAVRAKIS and Zbyněk PROKOP (203 Czech Republic, belonging to the institution)

Edition

Biotechnology Advances, OXFORD, Elsevier, 2023, 0734-9750

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

20800 2.8 Environmental biotechnology

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 16.000 in 2022

RIV identification code

RIV/00216224:14310/23:00131491

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1016/j.biotechadv.2023.108171

UT WoS

001009341400001

Keywords in English

Enzyme; Biochemical characterization; Biotechnology; Catalytic activity; Thermostability; Steady-state kinetics; Protein crystallography; Big data; Protein engineering; Artificial intelligence

Abstract

V originále

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications that provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

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

LX22NPO5102, research and development project

Name: Národní ústav pro výzkum rakoviny (Acronym: NÚVR)

Investor: Ministry of Education, Youth and Sports of the CR, National institute for cancer research, 5.1 EXCELES

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, David KOVÁŘ, Jiří DAMBORSKÝ, Ding YUN, Tianjin YANG, Andrew DE MELLO, Stanislav MAZURENKO, Stavros STAVRAKIS and Zbyněk PROKOP. In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. Biotechnology Advances. OXFORD: Elsevier, 2023, vol. 66, September 2023, p. 1-22. ISSN 0734-9750. Available from: https://dx.doi.org/10.1016/j.biotechadv.2023.108171.

@article{2303827,
   author = {Vašina, Michal and Kovář, David and Damborský, Jiří and Yun, Ding and Yang, Tianjin and de Mello, Andrew and Mazurenko, Stanislav and Stavrakis, Stavros and Prokop, Zbyněk},
   article_location = {OXFORD},
   article_number = {September 2023},
   doi = {http://dx.doi.org/10.1016/j.biotechadv.2023.108171},
   keywords = {Enzyme; Biochemical characterization; Biotechnology; Catalytic activity; Thermostability; Steady-state kinetics; Protein crystallography; Big data; Protein engineering; Artificial intelligence},
   language = {eng},
   issn = {0734-9750},
   journal = {Biotechnology Advances},
   title = {In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning},
   url = {https://www.sciencedirect.com/science/article/pii/S0734975023000782?via%3Dihub},
   volume = {66},
   year = {2023}
}

TY  - JOUR
ID  - 2303827
AU  - Vašina, Michal - Kovář, David - Damborský, Jiří - Yun, Ding - Yang, Tianjin - de Mello, Andrew - Mazurenko, Stanislav - Stavrakis, Stavros - Prokop, Zbyněk
PY  - 2023
TI  - In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning
JF  - Biotechnology Advances
VL  - 66
IS  - September 2023
SP  - 1-22
EP  - 1-22
PB  - Elsevier
SN  - 07349750
KW  - Enzyme
KW  - Biochemical characterization
KW  - Biotechnology
KW  - Catalytic activity
KW  - Thermostability
KW  - Steady-state kinetics
KW  - Protein crystallography
KW  - Big data
KW  - Protein engineering
KW  - Artificial intelligence
UR  - https://www.sciencedirect.com/science/article/pii/S0734975023000782?via%3Dihub
N2  - Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications that provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.
ER  -

VAŠINA, Michal, David KOVÁŘ, Jiří DAMBORSKÝ, Ding YUN, Tianjin YANG, Andrew DE MELLO, Stanislav MAZURENKO, Stavros STAVRAKIS and Zbyněk PROKOP. In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. \textit{Biotechnology Advances}. OXFORD: Elsevier, 2023, vol.~66, September 2023, p.~1-22. ISSN~0734-9750. Available from: https://dx.doi.org/10.1016/j.biotechadv.2023.108171.

Detailed Information on Publication Record