基于整合机器学习和基于约束的建模的代谢物-蛋白质相互作用的预测
Fayaz Soleymani Babadi1, Zahra Razaghi-Moghadam2, Fatemeh Zare-Mirakabad1
1Departement of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.
Bioinformatics advances
|July 31, 2023
概括
我们开发了SARTRE,这是一个使用代谢网络的影子价格来预测代谢物-蛋白质相互作用的计算框架. 萨特可以准确地预测这些相互作用,进步我们对细胞代谢的理解.
科学领域:
- 系统生物学和计算生物学.
- 代谢网络分析代谢网络分析
- 蛋白质-代谢物相互作用
背景情况:
- 代谢物-蛋白质相互作用对于调节细胞代谢和蛋白质功能至关重要.
- 使用基因组规模的代谢网络预测这些相互作用仍然是一个重大挑战.
- 现有的方法缺乏整合代谢网络数据到相互作用预测的综合方法.
研究的目的:
- 介绍SARTRE,一种用于预测代谢物-蛋白质相互作用的新型计算框架.
- 为了利用来自流量变化分析的特征,特别是影子价格,用于相互作用预测.
- 使用已建立的互动原子数据和代谢模型来验证框架的准确性.
主要方法:
- 开发SARTRE计算框架的开发.
- 从基因组规模代谢模型的流量变化分析中提取影子价格.
- 监督机器学习 (随机森林分类器) 的应用,用于预测代谢物-蛋白相互作用.
主要成果:
- 在 *Escherichia coli* (AUC=0.86) 和 *Saccharomyces cerevisiae* (AUC=0.85) 中,SARTRE 实现了对*Escherichia coli* (AUC=0.86) 和*Saccharomyces cerevisiae* (AUC=0.85) 中代谢物-蛋白相互作用的高预测精度.
- 影子价格被确定为准确交互预测的关键特征.
- 萨特雷的表现与深度学习方法相匹敌,在某些方面甚至优于它.
结论:
- 从基于约束的代谢网络分析中得出的特征,如影子价格,对于预测代谢物-蛋白质相互作用是有效的.
- SARTRE提供了一个强大的计算工具,用于揭示代谢物-蛋白相互作用的功能作用.
- 该框架的成功凸显了将代谢网络特性集成到相互作用预测模型中的潜力.
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