Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
Biotechnol Adv. 2023 Sep;66:108171. doi: 10.1016/j.biotechadv.2023.108171. Epub 2023 May 5.
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.
如今，对新型生物技术产品的需求大幅增长，这得益于生物催化应用的不断发展，这些应用为化学工艺提供了可持续的绿色替代方案。生物催化应用的成功与否在很大程度上取决于我们能够多快地识别和表征适合工业工艺条件的酶变体。虽然微型化和并行化极大地提高了下一代测序系统的通量，但随后对获得的候选物进行表征仍然是识别所需生物催化剂的一个限制过程。目前只有少数用于酶分析的商业微流控系统，将众多已发表的原型转化为商业平台仍需要简化。这篇综述介绍了在生物催化剂的功能和结构分析中应用微流控工具的最新技术、趋势和展望。我们讨论了现有技术的优缺点、它们的可重复性和稳健性以及是否适合常规实验室使用。我们还强调了微流控在利用机器学习为生物催化剂开发提供支持方面的未开发潜力。
