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