集成机器学习和基于结构的虚拟查识别了针对50S核糖体抑制活动的天然产品,用于对抗Cutibacterium acnes
Jixing Liu1, Henry H Y Tong1,2, Hang Zheng3
1Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Molecules (Basel, Switzerland)
|November 27, 2025
概括
研究人员通过计算选大型天然产品库,以向Cutibacterium acnes (C. acnes) 的50S核糖体子单元,确定了强效的抗化合物. 三素显示出针对C. acnes. 的显著抗菌活性.
科学领域:
- 微生物学和药理学 微生物学和药理学
- 计算化学和药物发现
背景情况:
- 是一种炎症性皮肤疾病,其中Cutibacterium acnes (C. acnes) 起着关键作用.
- 50S大核糖体子单元对C. acnes蛋白质合成和活力至关重要,使其成为潜在的治疗点.
研究的目的:
- 开发和应用一个集成的计算-实验工作流程来识别新的抗C. acnes化合物.
- 发现针对50S核糖体子单元的新抗菌剂,用于治疗.
主要方法:
- 使用机器学习定量结构-活性关系 (ML-QSAR) 模型对50S/23SrRNA配体进行训练,选了大量的天然产品库 (186,659种化合物).
- 化合物进一步使用ADMET属性和基于结构的对抗23S的对接进行过.rRNA.
- 实验验证包括针对C. acnes.的最小抑制度 (MIC) 测定.
主要成果:
- 计算工作流成功地对图书馆进行了分类,确定了六个实验结果.
- 三种化合物表现出显著的抗C. acnes活性,MIC ≤8μg/mL.
- 五环三胺的三胺是最强大的打击,MICs从0.5-2μg/mL不等.
结论:
- 专注于50S核糖体亚单元的多阶段计算查策略对于发现抗C. acnes化合物是有效的.
- 这种综合方法有效地优先考虑具有可量化的抗菌活性对抗C. acnes.的天然产品.
- 三素代表了一个有希望的化合物,用于开发新的治疗方法.
关键词:
50S 核糖体亚单元这种细菌是Cutibacterium acnes.一般性 (acne vulgaris) 是一种停靠的对接方式机器学习是机器学习.自然产品是自然产品的产品.一次性三次性三次性更多相关视频
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