使用机器学习来预测尸菌和其木匠宿主之间的蛋白质-蛋白质相互作用
Ian Will1, William C Beckerson2, Charissa de Bekker3,4
1Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL, 32816, USA. ian.will@knights.ucf.edu.
Scientific reports
|August 24, 2023
概括
这项研究预测了尸菌和木匠之间的蛋白质相互作用,揭示了潜在的效应因子和宿主目标,推动了对真菌传播的行为操纵.
科学领域:
- 菌类学 菌类学是指菌类学.
- 神经生物学 神经生物学 神经生物学
- 生物化学 生物化学
背景情况:
- 寄生真菌使用效应蛋白来操纵宿主生理和行为.
- 尸菌,Ophiocordyceps camponoti-floridani,改变了木匠的行为,导致了独特的"峰会疾病"的传播.
- 假设跨特异性蛋白质-蛋白质相互作用 (PPI) 可以调解这种宿主操纵.
研究的目的:
- 使用机器学习预测和分析O. camponoti-floridani和Camponotus floridanus之间的宿主寄生虫PPI.
- 为了产生机械学假设,说明真菌如何操纵的行为.
- 识别潜在的真菌效应蛋白及其宿主点.
主要方法:
- 利用D-SCRIPT,一种机器学习工具,用于对寄生虫和宿主之间的PPI进行高通量预测.
- 对宿主和寄生虫蛋白质的功能丰富分析了预测的PPI.
- 专注于寄生虫蛋白质编码的基因上调在操纵宿主行为期间调节.
主要成果:
- 预测约有6000个PPI涉及2083个宿主和129个寄生虫蛋白质.
- 在宿主蛋白中识别过多的功能性注释,包括神经调节性G蛋白合受体和氧化减少过程.
- 发现了寄生虫蛋白酶和新型小分泌蛋白质的丰富,这表明了潜在的效应作用.
结论:
- 该研究提供了关于尸行为操纵分子机制的新假设.
- 确定了特定的宿主点 (例如,G蛋白合受体) 和寄生虫作用因子 (例如,蛋白酶,小分泌蛋白).
- 突出了机器学习在预测宿主-寄生虫系统中的物种间相互作用方面的实用性.
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