深度学习与定量结构-活动关系相结合,加速了抗真菌的新设计
Kedong Yin1,2, Ruifang Li1,3, Shaojie Zhang1,3
1Zhengzhou Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, Zhengzhou, Henan, 450001, P. R. China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|February 8, 2025
概括
新型抗真菌 (AFPs) 是使用深度学习设计的,用于对抗抗药 Candida 感染. 一个,AFP-13,在动物模型中显示出卓越的治疗效果,提供了一个有前途的新治疗策略.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 耐药Candida感染构成重大威胁,需要新的抗真菌疗法.
- 抗真菌 (AFPs) 提供了一个有前途的替代品,因为它们独特的作用机制,减少耐药性的发展.
- 现有的抗真菌药物发现方法在快速识别有效候选药物方面面临挑战.
研究的目的:
- 为抗真菌 (AFPs) 开发一种新的 de novo 设计方法,以克服抗药性.
- 选和识别具有针对病原性真菌的广谱活性强大的AFP.
- 在临床前动物模型中评估设计的AFPs的治疗疗效.
主要方法:
- 开发了一种深度学习-定量结构-活动关系实证选 (DL-QSARES) 方法,整合了深度学习和QSAR.
- 通过主导氨基酸和二化合物的重组生成候选AFPs (c_AFPs).
- 利用基于物理化学性质的自然语言处理和QSAR来选c_AFPs.
主要成果:
- 选了49种具有最低抑制度 (MIC) 的有前途的c_AFPs,其抗 Candida albicans的抑制度在3.9-125μg mL-1.1之间.
- 鉴定了四种主要的c_AFPs (AFP-8, -10, -11,和-13) 与MIC<10μg mL-1对四种病原性真菌.
- 在相关的动物感染模型中证明了对AFP-13的卓越治疗效果.
结论:
- DL-QSARES方法有效地设计出具有强大活性的新型抗真菌.
- 在治疗真菌感染方面,AFP-13具有显著的治疗潜力,包括对常规药物的耐药性.
- 这种方法加速了下一代抗真菌剂的发现,以解决紧急的临床需求.
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