Abstract:

Natural products and their derivatives from animals, plants and microorganisms are the main sources of lead compounds in modern drug discovery and development. Canonical natural product discovery relies largely on biological activity-guided or chromatographic feature-oriented screening strategies, which have achieved great success in the last century. With this strategy, a large number of active natural products with novel structures have been discovered, providing numerous lead compounds for new drug research and development. However, with the passage of time, the limitations of this method, such as time consumption, labor intensity and the frequent isolation of high-abundance natural products, have led to a deceleration in the pace of novel active natural product discovery. This slowdown has increasingly failed to meet the demands of drug development and combat the rising threat of multi-drug resistance. The establishment of modern molecular biology, along with the developments of DNA sequencing and computational technology, has made it possible to study the biosynthesis of natural products, enabling us to connect genetic sequence with natural product structure and predict the potential of natural products produced by specific biological samples at the genetic level. Therefore, genome mining-directed discovery for natural products emerged. In addition to the classic mining methods dependent on the conservation of genes encoding for core biosynthesis or tailoring steps in natural product biosynthesis, recently developed activity-oriented genome mining with self-resistance mechanisms, phylogeny-based approaches by tree building and analysis to understand the evolution of genes and intelligence-assisted genome mining strategies provide huge opportunities for naturally medicinal products discovery. This paper reviews the brief history of genome mining, highlighting advancements in related databases, tools and algorithms. It focuses on recent cases and applications of classic genome mining as well as self-resistance mechanisms, evolutionary theory and artificial intelligence guided mining in the discovery of naturally active products. Furthermore, it explores the impact and development of genome mining in drug discovery and the multidisciplinary fields. Since Genomic information contains enormous chemical potential, we can realize the discovery of natural small molecules with high throughput, high novelty and high efficiency, and accelerate the development of new drugs, new chemicals and new catalysts through promoting the convergence of genome mining and other disciplines, improving the analysis and processing capabilities of genetic information, enhancing the expression of downstream gene clusters and the structure prediction ability of natural products.

Key words: genome mining, drug, natural products, biosynthesis, artificial intelligence, database

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，极大地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。

关键词: 基因组挖掘, 药物, 天然产物, 生物合成, 人工智能, 数据库