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通过深度图形网络对蛋白质-蛋白质相互作用网络的敏感性分析

Alessandro Dipalma¹, Michele Fontanesi², Alessio Micheli²

¹Department of Computer Science, University of Pisa, Largo Bruno Pontecorvo, 3, 56125, Pisa, PI, Italy. alessandro.dipalma@phd.unipi.it.

BMC bioinformatics

|May 9, 2025

概括

这项研究引入了一种新的方法,通过生物化学途径 (BP) 获得动态性质来丰富静态蛋白质-蛋白质相互作用网络 (PPIN). 开发的模型有效地预测了PPIN内部的敏感性关系,增强了它们对生物洞察的有用性.

科学领域:

系统生物学系统生物学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

蛋白与蛋白相互作用网络 (PPIN) 提供了细胞过程的静态视图.
现有的PPIN缺乏对于理解生物系统至关重要的动态信息.
生物化学途径 (BPs) 捕获动态,但范围有限,计算密集.

研究的目的:

从BP中计算出的动态属性丰富静态PPIN.
开发一个计算模型,直接从PPIN中预测动态属性.
探索PPIN结构在推断动态行为中的潜力.

主要方法:

使用ODE模拟来分析BP,并计算化学物种之间的敏感性.
敏感性数据被整合到PPIN中,以创建DyPPIN数据集.
在DyPPIN上训练了一个深度图形网络 (DGN),以预测蛋白质敏感性关系.

主要成果:

通过DyPPIN数据集,DGN能够成功地预测PPIN内部的敏感性关系.
发现PPIN结构对于推断灵敏度至关重要.
蛋白序列嵌入进一步提高了模型的预测准确性.

结论:

本文介绍了PPIN直接敏感性分析的第一个模型.
单独的PPIN结构就包含了足够的信息来推断动态性质.
该管道可适应药物设计,重新利用和个性化医学的应用.

关键词:

深度图形网络是一个深度图形网络.蛋白质与蛋白质的相互作用灵敏度分析是一种灵敏度分析.

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