基于计算机模拟和实验验证:从土壤微生物群中挖掘和表征新型抗菌
Chunming Xu1, Aiping Han2, Yuan Tian2
1School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, China; Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Food chemistry
|December 3, 2024
概括
我们开发了一个深度学习管道,从土壤DNA中找到抗微生物 (AMP). 这种方法有效地确定了食品安全应用的有希望的AMP候选者.
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.
- 食品科学 食品科学 食品科学
背景情况:
- 传统的抗微生物 (AMP) 查复杂且昂贵.
- 增强营养方法对于改善食品安全和延长保质期至关重要.
- 需要新的方法来有效地从大型数据集中发现AMP.
研究的目的:
- 开发和验证深度学习管道,以从土壤元基因组数据中识别潜在的AMP.
- 发现新的AMP候选物,在食品保存中具有潜在的应用.
- 为了证明计算方法在加速AMP发现方面的有效性.
主要方法:
- 一个深度学习模型被训练并应用于土壤元基因组数据.
- 候选AMP使用表面电荷分析,Hemopred和ToxinPred进行了选.
- 用分子对接和动力学模拟来评估结合亲和力和稳定性.
- 为了验证,进行了化学合成和抗菌活性测试.
主要成果:
- 深度学习管道在AMP预测中实现了高准确度 (92.71%) 和精度 (91.29%).
- 确定了9种候选,其中P4 (GTAWRWHYRARS) 显示出与MrkH.最好的结合亲和力.
- 合成的P4对Klebsiella pneumoniae,大肠杆菌和金黄色葡萄球菌表现出显著的抗菌活性.
结论:
- 开发的深度学习管道有效地发现了从元基因组数据中发现新的AMP.
- P4显示出作为食品应用中安全有效的抗微生物剂的潜力.
- 这种综合方法加速了AMP的发现,为传统方法提供了一个有希望的替代方案.
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