Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China.
Department of Ocean Science and Hong Kong Branch of the Southern Marine Science and Engineering Guangdong Laboratory, The Hong Kong University of Science and Technology, Hong Kong, China.
Mar Drugs. 2022 Jun 16;20(6):398. doi: 10.3390/md20060398.
Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.
大规模的基因组挖掘分析已经确定了大量的隐性生物合成基因簇（BGCs），它们是新型生物活性天然产物的重要来源。鉴于天然产物（NP）候选物的数量之多，有效的策略和计算方法是选择合适的 BGC 进行进一步 NP 表征和生产的关键。本综述通过突出展示那些成功生产出具有高化学新颖性、新型生物合成途径和强大生物活性化合物的研究，讨论了基于基因组学的方法，用于优先考虑从大规模基因组数据中提取的候选 BGC。我们根据 BGC 优先级的逻辑对这些研究进行分组：检测抗性基因的存在、使用系统发育基因组学分析作为指导以及针对特定化学结构进行靶向。我们还简要评论了该领域中使用的不同生物信息学工具，并在进行大规模基因组挖掘研究时检查了实际考虑因素。
