揭示了通过基础模型驱动的框架在肠道微生物中抗菌的演变
Wenhui Li1, Baicheng Huang2, Menghao Guo2
1Research Center for Life Sciences Computing, Zhejiang Lab, Hangzhou, Zhejiang 311121, China; School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Cell reports
|May 30, 2025
概括
抗微生物耐药性是一种全球性威胁. 一个新的AI工具AMP-SEMiner发现了数百万个抗微生物 (AMP) 候选者,其中许多具有强烈的抗细菌活性,为新的治疗提供了希望.
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:
- 抗菌素耐药性 (AMR) 是一个关键的全球健康挑战.
- 抗微生物 (AMP) 是一种有前途的AMR替代疗法.
- 蛋白质语言模型 (PLM) 改善了蛋白质功能预测,有助于AMP的发现.
研究的目的:
- 开发一个人工智能驱动的框架,AMP-SEMiner,用于系统的AMP识别.
- 为了利用PLM,结构聚类和进化分析,在元基因组组装基因组中发现AMP.
- 验证新发现的AMP候选药物的抗菌疗效.
主要方法:
- 开发AMP-SEMiner,这是一个整合PLM,结构聚类和进化分析的AI框架.
- 应用AMP-SEMiner用于从小型开放式读取框架中识别AMP和甲基因组数据中的AMP含有的蛋白质.
- 与抗生素相比,鉴定出AMP候选药物的抗菌活性和疗效的实验验证.
主要成果:
- 在各种环境样本中识别了超过160万个AMP候选人.
- 实验验证确认了20个测试候选物中的9个的抗菌活性.
- 五种经过验证的AMP候选药物显示出抗菌疗效超过现有抗生素;衍生品也显示出高疗效.
结论:
- AMP-SEMiner是从大规模基因组数据中发现新型AMP的有效工具.
- 这项研究扩大了已知的AMP的范围,并为开发替代抗菌疗法提供了候选药物.
- 对来自人类肠道微生物群的AMP的分析揭示了它们的进化动态和生态作用的洞察力.
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