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全球微生物组的计算探索,以发现抗生素

Célio Dias Santos-Júnior¹, Marcelo Der Torossian Torres^2,3,4, Yiqian Duan¹

¹Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China.

bioRxiv : the preprint server for biology

|September 11, 2023

概括

研究人员开发了一种机器学习方法,从微生物基因组中发现新型抗菌 (AMP). 这导致了AMPSphere目录,确定了数千种潜在的新抗生素来对抗抗药性.

科学领域:

微生物学微生物学
生物信息学是一种生物信息学.
药物发现药物发现药物发现

背景情况:

抗生素耐药性的上升需要紧急发现新型抗菌剂.
Prokaryotic 抗微生物 (AMP) 是一种有前途的治疗药物.
大规模的基因组和元基因组数据集为AMP识别提供了尚未开发的潜力.

研究的目的:

开发一种基于机器学习的方法,用于预测 prokaryotic 抗微生物 (AMP).
从广泛的微生物数据中创建一个全面的新型AMP目录.
实验验证预测的AMP对耐药病原体的疗效.

主要方法:

利用在63,410个元基因组和87,920个微生物基因组的数据集上训练的机器学习模型.
创建了AMPSphere目录,包含863,498个非冗余序列.
合成并实验测试了50种预测的AMP用于体外和体内抗菌活性.

主要成果:

确定了大量的新型AMP,其产量因息地而异 (例如,在动物相关样本中更高).
验证了50种合成的AMP,通过向细菌膜,对抗耐药病原体表现出强烈的活性.
AMPSphere目录提供了关于AMP进化和在原生生物微生物组内的分布的见解.

结论:

机器学习方法成功地从 prokaryotic 微生物组中识别了许多新的 AMP.
经过验证的AMP显示出作为抗生素耐药细菌的新治疗剂的潜力.
AMPSphere 作为未来抗生素发现和研究的宝贵资源.

关键词:

抗微生物活性的抗微生物活性.抗微生物类的抗微生物.全球微生物群机器学习是机器学习.转基因组学是指转基因组学.

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