通过知识意识的快速扩散模型,可控制的产生病原体特异性抗菌
Yongkang Wang1, Menglu Li1, Feng Huang1
1College of Informatics, Huazhong Agricultural University, No.1, Shizishan Street, Wuhan, 430070, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 15, 2025
概括
KPPepGen是一个新的生成框架,使用知识意识提示来设计病原体特定的抗微生物. 这种方法提高了的性能,特别是对于数据稀缺的病原体,并且在湿实验室评估中显示出有希望的结果.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 生成型模型显示了抗微生物 (AMP) 设计的潜力.
- 对特定病原体的有限数据阻碍了针对性AMP的开发.
- 现有的方法难以产生病原体特异性.
研究的目的:
- 介绍KPPepGen,这是一个可控制的生成框架,用于设计病原体特定的AMP.
- 扩展KPPepGen以使用提示引导扩散和组合突变优化.
- 解决针对目标病原体的AMP设计中的数据稀缺性挑战.
主要方法:
- 开发了KPPepGen,这是一个使用基因本体学和知识图的知识意识病原体提示的框架.
- 集成的提示引导部分扩散与多位点组合突变用于类优化.
- 利用由知识注射指导的扩散模型,用于生物学上可信的生成.
主要成果:
- 对于56种病原体,KPPepGen产生了有效的,显示出高度的新奇性和有利的特性.
- 在有限的培训数据下,对病原体实现了超过10%的性能改善.
- 在湿实验室评估中产生了9种具有强烈抗菌活性和低细胞毒性对大肠杆菌和金黄色杆菌的新型.
- 在Magainin 2优化中表现出高成功率 (44.3%),与基于ESM的方法相比,平均有7.6%的改进.
结论:
- KPPepGen有效地产生病原体特定的AMP,克服了数据稀缺性的限制.
- 该框架捕捉了个别病原体的基本序列和结构模式.
- KPPepGen显著提高AMP的性能,并为抗菌药物发现提供了一个有前途的工具.
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