可解释的深度学习和虚拟进化识别了抗微生物对抗多药耐药的人类病原体的活性.
Beilun Wang1, Peijun Lin2, Yuwei Zhong3
1School of Computer Science and Engineering, Southeast University, Nanjing, China. beilun@seu.edu.cn.
Nature microbiology
|January 17, 2025
概括
人工智能 (AI) 加快了抗微生物 (AMP) 的发现. 一个人工智能模型,EvoGradient,从人类口腔细菌中识别和优化了新型AMP,显示出对抗多药耐药病原体的强大活性.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 抗菌素耐药性 (AMR) 是一个日益增长的全球健康威胁.
- 迫切需要新的抗微生物化合物来对抗耐药性病原体.
- 人工智能 (AI) 为药物发现提供了一个强大的方法.
研究的目的:
- 开发一个人工智能驱动的平台,用于识别和优化抗微生物 (AMP).
- 应用这个平台,从未探索过的微生物来源发现新的AMP.
- 为了验证计算机设计的AMP对多药耐药 (MDR) 细菌的疗效.
主要方法:
- 开发了EvoGradient,这是一个可解释的深度学习模型,用于预测AMP强度和指导序列修改.
- 应用EvoGradient从少量的人类口腔细菌中虚拟演化.
- 合成和实验验证的顶级计算机设计的AMP候选者与MDR病原体小组对抗.
- 使用小鼠模型进行了体内研究,以评估最强大的AMP的治疗潜力.
主要成果:
- 通过in silico定向进化,EvoGradient成功地确定了32个强大的AMP候选者.
- 六种合成的AMP对抗抗卡巴胺耐药的肠道细菌 (例如大肠杆菌,肺炎),Acinetobacter baumannii和抗万科胺耐药的Enterococcus faecium.表现出显著的活性.
- 化合物,pep-19-mod,通过系统和局部给药,在小鼠大腿感染模型中实现了体内95%以上的细菌负载减少.
结论:
- 人工智能驱动的化定向进化是发现和优化新型AMP的有效策略.
- 开发的EvoGradient平台可以加速识别有前途的抗菌药物候选药物.
- 这种方法在应对抗微生物药物耐药性的挑战方面具有重大潜力.
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