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.

Základní údaje

Originální název

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

Autoři

VAŠINA, Michal; David KOVÁŘ; Jiří DAMBORSKÝ; Ding YUN; Tianjin YANG; Andrew DE MELLO; Stanislav MAZURENKO; Stavros STAVRAKIS a Zbyněk PROKOP

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

20800 2.8 Environmental biotechnology

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 12.100

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/23:00131491

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

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

Štítky

14314010, 143180, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 30. 1. 2024 10:30, prof. Mgr. Jiří Damborský, Dr.

Anotace

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.

Návaznosti

EF17_043/0009632, projekt VaV

Název: CETOCOEN Excellence

LM2018121, projekt VaV

Název: Výzkumná infrastruktura RECETOX (Akronym: RECETOX RI)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, RECETOX RI

LM2018131, projekt VaV

Název: Česká národní infrastruktura pro biologická data (Akronym: ELIXIR-CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Česká národní infrastruktura pro biologická data

LX22NPO5102, projekt VaV

Název: Národní ústav pro výzkum rakoviny (Akronym: NÚVR)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Národní ústav pro výzkum rakoviny, 5.1 EXCELES

814418, interní kód MU

Název: Synthetic biology-guided engineering of Pseudomonas putida for biofluorination (Akronym: SinFonia)

Investor: Evropská unie, Synthetic biology-guided engineering of Pseudomonas putida for biofluorination, Leadership in enabling and industrial technologies (LEIT) (Industrial Leadership)

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

Název: CETOCOEN Excellence (Akronym: CETOCOEN Excellence)

Investor: Evropská unie, CETOCOEN Excellence, Spreading excellence and widening participation

Citovat

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

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