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

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Publikace nespadá pod Ústav výpočetní techniky, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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VAŠINA Michal KOVÁŘ David DAMBORSKÝ Jiří YUN Ding YANG Tianjin DE MELLO Andrew MAZURENKO Stanislav STAVRAKIS Stavros PROKOP Zbyněk

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Biotechnology Advances
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://www.sciencedirect.com/science/article/pii/S0734975023000782?via%3Dihub
Doi http://dx.doi.org/10.1016/j.biotechadv.2023.108171
Klíčová slova Enzyme; Biochemical characterization; Biotechnology; Catalytic activity; Thermostability; Steady-state kinetics; Protein crystallography; Big data; Protein engineering; Artificial intelligence
Popis 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.
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