基因组挖掘指导天然药物分子的发现
封面论文Genome mining-directed discovery for natural medicinal products
- 合成生物学 2024年5卷第3期 页码：447-473
- 作者机构：
1.南京大学生命科学学院，医药生物技术全国重点实验室，江苏 南京 210023
2.南京大学化学化工学院，江苏 南京 210023
3.先正达Jealott’s Hill国际研发中心，英国，伯克郡，布拉克内尔 RG42 6EY
- 作者简介：
[ "奚萌宇（1995—），女，博士研究生。研究方向为解析放线菌来源天然产物生物合成途径和机制；基因组挖掘发现新颖天然产物。E-mail：dg20240120@smail.nju.edu.cn" ]
[ "胡逸灵（1989—），男，博士，博士后。研究方向为天然产物的基因组挖掘和人工智能在天然产物发现中的应用。E-mail：huyiling10@163.com" ]
[ "戈惠明（1980—），男，教授，博士生导师。研究方向为挖掘微生物中新型药源分子；解析重要微生物活性分子的生物合成途径和机制；工程改造新型生物催化剂；合成生物学智造高值化学品。E-mail：hmge@nju.edu.cn" ]
- 基金信息：国家重点研发计划(2018YFA0902000);国家自然科学基金(81925033;22193071;81991522)
DOI：10.12211/2096-8280.2023-086
中图分类号： Q31收稿：2023-11-28，
修回：2024-02-20，
纸质出版：2024-06-30
- 稿件说明：
移动端阅览
奚萌宇， 胡逸灵， 顾玉诚， 戈惠明. 基因组挖掘指导天然药物分子的发现［J］. 合成生物学， 2024， 5（3）： 447-473
XI Mengyu， HU Yiling， GU Yucheng， GE Huiming. Genome mining-directed discovery for natural medicinal products［J］. Synthetic Biology Journal， 2024， 5（3）： 447-473
奚萌宇， 胡逸灵， 顾玉诚， 戈惠明. 基因组挖掘指导天然药物分子的发现［J］. 合成生物学， 2024， 5（3）： 447-473 DOI： 10.12211/2096-8280.2023-086.
XI Mengyu， HU Yiling， GU Yucheng， GE Huiming. Genome mining-directed discovery for natural medicinal products［J］. Synthetic Biology Journal， 2024， 5（3）： 447-473 DOI： 10.12211/2096-8280.2023-086.
摘要
天然产物是临床药物的主要来源，也是新药研发过程中先导化合物结构设计和优化的灵感源泉。但传统策略天然药源分子的发现却遭遇了瓶颈，新颖天然产物的数量逐渐无法满足现代药物开发的需求和应对全球多药耐药的威胁。随着测序技术的快速迭代，生物学的研究进入了基因组时代，基因组挖掘指导天然产物定向发现的策略得以确立，成功摆脱了传统天然产物发现策略对于生物样本生物量的依赖，极大提高了活性天然产物发现的特异性和成功率。本文简述了基因组挖掘以及相关数据库和生物信息学工具的发展，详细介绍了包括基于核心基因或后修饰基因的经典挖掘手段，自抗性机制、进化理论指导的基因组挖掘和人工智能在活性天然产物发现中的具体应用，并对基因组挖掘在药物发现和多学科交叉领域的影响和发展进行了展望。基因组信息中蕴藏着无可估量的化学潜能，促进基因组挖掘与其他学科间的交叉融合，提升对遗传信息的处理和分析能力，增强下游基因簇表达通量和产物结构预测能力，可实现天然小分子高通量、高新颖性和高效率的发现，为开发具有自主知识产权的新药物、新化学品和新型酶催化剂服务。
Abstract
Natural products and their derivatives are main sources for lead compounds in drug discovery and development. Canonical natural product discovery relies largely on biological activity-guided or chromatographic identification-oriented screening strategies
which have achieved great success so far. However
the limitations of these methods
such as time consumption
labor intensity
and the noises of abundant natural products
have constrained productivities in discovering novel active natural products for drug development and combating the rising threat of drug resistance. Modern biotechnology
particularly the development of DNA sequencing and computational technology
has made it possible to study the biosynthesis of natural products
enabling us to connect genetic sequences with natural product structures for predicting the potentials of natural products produced by specific biological species at the genetic level. Therefore
genome mining-directed discovery for natural products has emerged. In addition to mining methods dependent on the conservation of genes encoding core enzymes for natural product biosynthesis
recently developed activity-oriented and intelligence-assisted genome mining strategies provide more opportunities for discovering naturally medicinal products. This article reviews the history of genome mining
highlighting advances in related databases
tools
and algorithms
with a focus on recent cases and applications of classic genome mining as well as self-resistance mechanism
evolutionary theory and artificial intelligence guided mining in the discovery of naturally active products. Since genomic information contains enormous chemical potentials
the discovery of natural products with high throughput and efficiency can accelerate the development of new drugs
new chemicals and new catalysts.
2
关键词
Keywords
references
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