自然产品的化学空间中的进化树
Bo Yang1, Chijian Xiang1, Tongtong Li1
1Borch Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN 47907, USA.
bioRxiv : the preprint server for biology
|September 15, 2025
概括
密切相关的物种产生类似的自然产物 (NP),这一发现得到了深度学习的化学结构分析的支持. 这项研究揭示了NP的化学空间内的进化关系.
科学领域:
- 生物化学 生物化学
- 进化生物学 进化生物学
- 计算化学计算化学
背景情况:
- 自然产品 (NPs) 对生物适应至关重要,它们的分布受到进化和生态因素的影响.
- 相关物种之间化学相似性的假设在不同的种类中尚未被广泛测试.
- 了解分类学和化学资料之间的联系对于进化和生物化学研究至关重要.
研究的目的:
- 系统地评估自然产品的分类学接近性和化学相似性之间的相关性.
- 用计算方法调查NP的化学空间中是否可以识别进化关系.
- 开发一种新的计算框架来研究二次新陈代谢的演变.
主要方法:
- 利用莲花数据库进行大规模的天然产品数据.
- 采用了五个深度学习编码器,包括Chemformer和SMILES Transformer,将NP映射到一个高维化学空间中.
- 分析了NP的化学相似性,与各种物种的分类学距离相关.
主要成果:
- 在花植物 (Magnoliopsida) 和针叶树 (Pinopsida) 的NP中,较短的分类学距离和更高的化学相似性之间显示出显著的相关性.
- 在真菌和甲基动物中观察到类似的趋势,尽管由于水平基因转移和融合进化等因素而复杂化.
- 证明进化树可以在NP的化学表示空间内统计重建.
结论:
- 在使用先进的深度学习技术时,在自然产品的化学空间中,进化史在统计上是可回收的.
- 这项研究提供了一种新的计算方法来探索二次新陈代谢的进化动态.
- 结果可以帮助药物发现,使得从研究不足或灭绝的物种中重建NP.
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