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arXiv 2008.09649physics.bio-phcond-mat.stat-mechphysics.med-ph
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Human-SARS-CoV-2相互作用组的多尺度统计物理

Multiscale statistical physics of the Human-SARS-CoV-2 interactome

Arsham Ghavasieh, Sebastiano Bontorin, Oriol Artime, Manlio De Domenico

摘要:

蛋白质-蛋白质相互作用(PPI)网络已被用于研究SARS-CoV-2病毒蛋白对人类细胞功能的影响,从而加深了对COVID-19的理解,并为药物再利用策略提供了基础。然而,我们对这种病毒与其他病毒之间的相似性的了解仍然非常有限。在这里,我们从统计物理的角度将新型冠状病毒PPI网络与45种已知病毒进行比较。我们的研究结果表明,经典的分析,如渗滤是不敏感的区别特征的病毒,而生化传播模式的分析,使我们能够有意义地分类的病毒和定量比较它们对人类蛋白质的影响。值得注意的是,当使用类吉布斯密度矩阵来表示每个系统的状态时,由谱熵测量的相应宏观统计特性揭示了多尺度病毒簇的存在。总的来说,我们的研究结果表明,SARS-CoV-2在小尺度上与SARS-CoV和甲型流感病毒表现出相似性,而在更大尺度上,它与HIV 1和HTLV 1等病毒表现出更多的相似性。

英文摘要:

Protein-protein interaction (PPI) networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID--19 and providing ground for drug repurposing strategies. However, our knowledge of (dis)similarities between this one and other viral agents is still very limited. Here we compare the novel coronavirus PPI network against 45 known viruses, from the perspective of statistical physics. Our results show that classic analysis such as percolation is not sensitive to the distinguishing features of viruses, whereas the analysis of biochemical spreading patterns allows us to meaningfully categorize the viruses and quantitatively compare their impact on human proteins. Remarkably, when Gibbsian-like density matrices are used to represent each system's state, the corresponding macroscopic statistical properties measured by the spectral entropy reveals the existence of clusters of viruses at multiple scales. Overall, our results indicate that SARS-CoV-2 exhibits similarities to viruses like SARS-CoV and Influenza A at small scales, while at larger scales it exhibits more similarities to viruses such as HIV1 and HTLV1.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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