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深度学习驱动的发现来自大型原生体基因组的新型抗菌和实验性表征

Wenhao Li¹, Geyi Zhu¹, Muhammad Zubair²

¹School of Life Sciences, Anhui Agricultural University, Hefei 230036, China.

Journal of chemical information and modeling

|September 17, 2025

概括

科学家使用深度学习从原生体中发现了3000多种新型抗菌 (AMP). 这些新的AMP显示出显著的抗菌活性,为传统抗生素提供了有希望的替代品.

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:

抗生素耐药性需要新的抗菌剂.
抗微生物 (AMP) 是一个有希望的替代品.
原生体是AMP的一个未被充分探索的来源.

研究的目的:

使用深度学习系统地探索新型抗微生物 (AMP) 的原生体基因组.
识别和验证来自原生菌的AMP作为传统抗生素的潜在替代品.

主要方法:

深度学习模型 (BERT,CNN) 用于选来自2120个原生体基因组的66亿多个序列.
一个多阶段的选过程包括冗余检查和现有的AMP识别模型.
合成了18种候选,并通过实验验证了其抗菌活性.

主要成果:

从原生体基因组中确定了3133种新型候选抗微生物 (AMP).
实验验证证证实了所有18种合成的对测试的细菌物种的抗菌活性.
AMP_N2和AMP_N3表现出强大的抗菌作用,血液溶解率低 (<3%).

结论:

原生体代表了发现新型抗微生物 (AMP) 的重要储存库.
深度学习是一种有效的策略,用于在未经探索的微生物来源中大规模发现AMP.
这项研究提供了一种强大的方法,用于从各种基因组数据中识别功能AMP.

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