Genome mining-directed discovery for natural medicinal products

XI Mengyu; HU Yiling; GU Yucheng; GE Huiming

doi:10.12211/2096-8280.2023-086

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Genome mining-directed discovery for natural medicinal products

Invited Review | 更新时间：2025-03-19

- Genome mining-directed discovery for natural medicinal products
  封面论文
- Synthetic Biology Journal Vol. 5, Issue 3, Pages: 447-473(2024)
- 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：
- 基金信息：
- DOI：10.12211/2096-8280.2023-086
  CLC： Q31
- Received：28 November 2023，
  
  Revised：2024-02-20，
  
  Published：30 June 2024
- 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.

关键词

Keywords

references

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