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通过机器学习在全球微生物群中发现抗菌
Célio Dias Santos-Júnior1, Marcelo D T Torres2, Yiqian Duan3
1Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Laboratory of Microbial Processes & Biodiversity - LMPB, Department of Hydrobiology, Universidade Federal de São Carlos - UFSCar, São Carlos, São Paulo 13565-905, Brazil.
Cell
|June 6, 2024
概括
研究人员使用机器学习发现了近100万种抗菌 (AMP), 这为从全球微生物群中开发新型抗生素开辟了新的途径.
科学领域:
- 微生物学
- 生物信息学
- 药物发现
背景情况:
- 抗生素耐药性细菌的增加迫切需要新型抗菌剂的发现.
- 抗菌 (AMP) 是具有广泛活性的一类有前途的天然化合物.
- 现有的AMP数据库有限,这凸显了对微生物基因组的全面探索的需要.
研究的目的:
- 开发一种机器学习方法,从全球微生物群中预测抗菌 (AMPs).
- 创建一个全新的AMPSphere目录.
- 验证预测的AMP对耐药病原体的疗效.
主要方法:
- 在大基因组和 prokaryotic 基因组的大数据集上训练了一种机器学习模型.
- 构建了AMPSphere目录,其中包含863,498个非冗余.
- 合成并实验测试了100种预测的AMP在体外和体内对病原体和初生菌进行测试.
主要成果:
- 在AMPSphere中识别和目录了近100万个新型的原生细胞AMP序列.
- 对抗菌活性进行了验证79种合成AMP,其中63种针对耐药病原体.
- 观察到AMP会破坏细菌膜,证实了它们的作用机制.
- 在不同息地发现AMP产生的变异,并深入了解它们的进化起源.
结论:
- 机器学习方法成功地发现了大量潜在的抗生素候选者.
- AMPSphere为未来的抗生素发现工作提供了一个开放的平台.
- 经过验证的AMP显示出对抗抗生素耐药性危机的潜力.
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