在自然产品发现领域的多态学和整合分析
Daoyuan Xie1, Yu Tian2, Fan Zheng3
1Department of Oncology, Deyang People's Hospital of Chengdu University of Traditional Chinese Medicine; Laboratory of Translational Medicine Research, Deyang People's Hospital of Chengdu University of Traditional Chinese Medicine.
Journal of visualized experiments : JoVE
|December 15, 2025
概括
先进的多omics和计算方法通过整合基因组学,代谢学,转录学和蛋白质学来加速自然产品的发现. 这些工具,包括人工智能和机器学习,克服了对新药化合物的传统查限制.
科学领域:
- 药物发现 药物发现 药物发现
- 生物活性化合物 生物活性化合物
- 自然产品研究 自然产品研究
背景情况:
- 传统的自然产品 (NP) 选是缓慢且低效的.
- 先进的多学科和计算方法提供了解决方案.
- 整合不同的数据类型是加速NP发现的关键.
研究的目的:
- 审查自然产品发现的创新方法.
- 要突出多omics数据与计算工具的整合.
- 讨论人工智能和机器学习在NP研究中的作用.
主要方法:
- 代谢学 (LC-MS/MS,GNPS) 进行研究.
- 基因组学/转基因组学 (antiSMASH,DeepBGC,PRISM) 是一个研究领域.
- 转录组学 (RNA-seq,流体组学) 是一个学科.
- 蛋白质组学 (TMT/iTRAQ,没有标签,化学蛋白质组学)
- 生物信息学,人工智能和机器学习
主要成果:
- 多omics平台使综合性化合物分析和生物合成基因集群的识别成为可能.
- 转录组学和蛋白质组学揭示了通路调节和分子点.
- 人工智能/机器学习有效地整合了多学科数据,用于功能预测和知识发现.
结论:
- 协同的多omics方法显著提高自然产品的发现.
- 解决当前的技术挑战对于未来的进步至关重要.
- 高通量,智能数据集成将推动下一代NP研究.
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