机器学习预测药用植物中的3D域交换蛋白
Aakanksha Pandey1, Atul Kumar Upadhyay2
1Thapar Institute of Engineering and Technology; apandey_phd20@thapar.edu.
Journal of visualized experiments : JoVE
|September 1, 2025
概括
机器学习可以准确地预测药用植物的3D域互换,识别参与压力反应和生物合成的蛋白质. 这有助于了解蛋白质的功能和药物发现的潜力.
科学领域:
- 蛋白质组学和生物信息学
- 植物分子生物学
- 结构生物学
背景情况:
- 三维域互换是植物应激反应和二次代谢物生物合成的关键蛋白质寡合化过程.
- 了解这些结构重组对于药用植物研究至关重要.
研究的目的:
- 开发和评估用于预测药用植物基因组3D域互换的机器学习模型.
- 功能性注释预测的3D域互换蛋白质,并确定它们在生物途径中的作用.
主要方法:
- 使用随机森林和K-近邻分类器来预测3D域互换模式.
- 使用基因本体学 (GO) 和基因和基因组的京都百科全书 (KEGG) 途径进行功能丰富分析.
- 在二次代谢物生物合成途径中分析域分布.
主要成果:
- 实现了高预测准确度 (随机森林为91.6%,K-最近邻居为88.7%).
- 确定了420个 (31%) 涉及3D域互换的序列.
- 参与光合作用,氧化化和通过二次代谢物合成调节生物/无生物应激反应的注释蛋白.
结论:
- 机器学习有效预测药用植物中的3D域互换和蛋白质功能.
- 已识别的3D域互换蛋白在植物应激耐受性和二次代谢中发挥关键作用.
- 这些发现支持药物发现和生物工程的应用.
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