Wisconsin Institute for Discovery and Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI, USA.
Evolution of Metabolic Diversity Laboratory, Unidad de Genómica Avanzada (Langebio), Cinvestav-IPN, Guanajuato, Mexico.
Methods Mol Biol. 2022;2489:129-155. doi: 10.1007/978-1-0716-2273-5_8.
Genome mining has become an invaluable tool in natural products research to quickly identify and characterize the biosynthetic pathways that assemble secondary or specialized metabolites. Recently, evolutionary principles have been incorporated into genome mining strategies in an effort to better assess and prioritize novelty and understand their chemical diversification for engineering purposes. Here, we provide an introduction to the principles underlying evolutionary genome mining, including bioinformatic strategies and natural product biosynthetic databases. We introduce workflows for traditional genome mining, focusing on the popular pipeline antiSMASH, and methods to predict enzyme substrate specificity from genomic information. We then provide an in-depth discussion of evolutionary genome mining workflows, including EvoMining, CORASON, ARTS, and others, as adopted by our group for the discovery and prioritization of natural products biosynthetic gene clusters and their products.
基因组挖掘已成为天然产物研究中一种非常宝贵的工具，可以快速鉴定和描述组装次生代谢物或特殊代谢物的生物合成途径。最近，进化原理已被纳入基因组挖掘策略中，以更好地评估和优先考虑新颖性，并理解其用于工程目的的化学多样化。在这里，我们介绍了进化基因组挖掘的基本原则，包括生物信息学策略和天然产物生物合成数据库。我们介绍了传统基因组挖掘的工作流程，重点介绍了流行的 pipeline antiSMASH，并介绍了从基因组信息预测酶底物特异性的方法。然后，我们深入讨论了进化基因组挖掘工作流程，包括我们小组用于发现和优先考虑天然产物生物合成基因簇及其产物的 EvoMining、CORASON、ARTS 等方法。
