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高度连接的蛋白质相互作用的抑制对冠状病毒感染的影响。

The impact of the suppression of highly connected protein interactions on the corona virus infection.

机构信息

Departamento de Física, Facultad de Ciencias, Universidad de Chile, Casilla 653, 78000024, Santiago, Chile.

Centro de Nanociencia y Nanotecnología CEDENNA, Avda. Ecuador 3493, Estación Central, 9170124, Santiago, Chile.

出版信息

Sci Rep. 2022 Jun 2;12(1):9188. doi: 10.1038/s41598-022-13373-0.

Abstract

Several highly effective Covid-19 vaccines are in emergency use, although more-infectious coronavirus strains, could delay the end of the pandemic even further. Because of this, it is highly desirable to develop fast antiviral drug treatments to accelerate the lasting immunity against the virus. From a theoretical perspective, computational approaches are useful tools for antiviral drug development based on the data analysis of gene expression, chemical structure, molecular pathway, and protein interaction mapping. This work studies the structural stability of virus-host interactome networks based on the graphical representation of virus-host protein interactions as vertices or nodes connected by commonly shared proteins. These graphical network visualization methods are analogous to those use in the design of artificial neural networks in neuromorphic computing. In standard protein-node-based network representation, virus-host interaction merges with virus-protein and host-protein networks, introducing redundant links associated with the internal virus and host networks. On the contrary, our approach provides a direct geometrical representation of viral infection structure and allows the effective and fast detection of the structural robustness of the virus-host network through proteins removal. This method was validated by applying it to H1N1 and HIV viruses, in which we were able to pinpoint the changes in the Interactome Network produced by known vaccines. The application of this method to the SARS-CoV-2 virus-host protein interactome implies that nonstructural proteins nsp4, nsp12, nsp16, the nuclear pore membrane glycoprotein NUP210, and ubiquitin specific peptidase USP54 play a crucial role in the viral infection, and their removal may provide an efficient therapy. This method may be extended to any new mutations or other viruses for which the Interactome Network is experimentally determined. Since time is of the essence, because of the impact of more-infectious strains on controlling the spread of the virus, this method may be a useful tool for novel antiviral therapies.

摘要

几种高效的新冠病毒疫苗已投入紧急使用,但传染性更强的冠状病毒株可能会进一步延迟大流行的结束。因此,开发快速抗病毒药物治疗方法以加速对病毒的持久免疫力是非常可取的。从理论角度来看,基于基因表达、化学结构、分子途径和蛋白质相互作用图谱的数据分析,计算方法是开发抗病毒药物的有用工具。这项工作研究了基于病毒-宿主蛋白质相互作用的图形表示作为由共同共享的蛋白质连接的顶点或节点的病毒-宿主互作网络的结构稳定性。这些图形网络可视化方法类似于神经形态计算中人工神经网络设计中使用的方法。在标准的基于蛋白质节点的网络表示中,病毒-宿主相互作用与病毒-蛋白质和宿主-蛋白质网络合并,引入了与内部病毒和宿主网络相关的冗余链接。相反,我们的方法提供了病毒感染结构的直接几何表示,并允许通过去除蛋白质来有效地快速检测病毒-宿主网络的结构鲁棒性。该方法通过应用于 H1N1 和 HIV 病毒进行了验证,我们能够确定已知疫苗引起的互作网络的变化。该方法应用于 SARS-CoV-2 病毒-宿主蛋白质互作网络表明,非结构蛋白 nsp4、nsp12、nsp16、核孔膜糖蛋白 NUP210 和泛素特异性肽酶 USP54 在病毒感染中起着至关重要的作用,去除它们可能提供有效的治疗方法。该方法可扩展到任何新的突变或其他实验确定互作网络的病毒。由于时间紧迫,由于传染性更强的病毒株对控制病毒传播的影响,该方法可能是新的抗病毒治疗的有用工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acfe/9163126/5987d4e50c09/41598_2022_13373_Fig1_HTML.jpg

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