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微生物酶的生物勘探:行业和医疗保健的当前趋势。

Bioprospecting of microbial enzymes: current trends in industry and healthcare.

机构信息

Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India.

出版信息

Appl Microbiol Biotechnol. 2022 Mar;106(5-6):1813-1835. doi: 10.1007/s00253-022-11859-5. Epub 2022 Mar 7.

DOI:10.1007/s00253-022-11859-5
PMID:35254498
Abstract

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

摘要

微生物酶在生产食品、药品和其他商业产品方面发挥着不可或缺的作用。已经从生命的所有领域(如植物、微生物和动物)报道了许多新型酶。然而,具有工业应用价值的微生物来源的酶是有限的。本文讨论了最需要的工业酶如果胶酶、纤维素酶、脂肪酶和蛋白酶的分类、应用、来源和挑战。此外,还探讨了通过定向进化、理性和从头设计等蛋白质工程技术生产新型酶,以改进现有工业酶的方法。我们还探讨了宏基因组学、纳米技术、OMICS 和机器学习方法在新型酶的生物勘探中的作用。总的来说,这篇综述涵盖了生物催化剂在工业和医疗保健应用中的基础,并概述了现有的微生物酶优化工具。

关键点

  1. 微生物生物活性分子对治疗和工业应用至关重要。

  2. 高通量 OMICS 是发现新型酶最有效的方法。

  3. 全面的数据库和高效的机器学习模型是快速推进从头设计和发现新型酶的迫切需要。

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