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一个 SARS-CoV-2(COVID-19)的生物网络,用于寻找药物再利用的靶点。

A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing.

机构信息

Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

出版信息

Sci Rep. 2021 Apr 30;11(1):9378. doi: 10.1038/s41598-021-88427-w.

Abstract

The Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus needs a fast recognition of effective drugs to save lives. In the COVID-19 situation, finding targets for drug repurposing can be an effective way to present new fast treatments. We have designed a two-step solution to address this approach. In the first step, we identify essential proteins from virus targets or their associated modules in human cells as possible drug target candidates. For this purpose, we apply two different algorithms to detect some candidate sets of proteins with a minimum size that drive a significant disruption in the COVID-19 related biological networks. We evaluate the resulted candidate proteins sets with three groups of drugs namely Covid-Drug, Clinical-Drug, and All-Drug. The obtained candidate proteins sets approve 16 drugs out of 18 in the Covid-Drug, 273 drugs out of 328 in the Clinical-Drug, and a large number of drugs in the All-Drug. In the second step, we study COVID-19 associated proteins sets and recognize proteins that are essential to disease pathology. This analysis is performed using DAVID to show and compare essential proteins that are contributed between the COVID-19 comorbidities. Our results for shared proteins show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases.

摘要

由 SARS-CoV-2 病毒引起的 2019 年冠状病毒病 (COVID-19) 需要快速识别有效的药物来挽救生命。在 COVID-19 情况下,寻找药物再利用的靶点可能是提供新的快速治疗方法的有效途径。我们设计了一个两步解决方案来解决这个问题。在第一步中,我们确定病毒靶点或其在人细胞中的相关模块中的必需蛋白作为可能的药物靶标候选物。为此,我们应用两种不同的算法来检测一些候选蛋白集,这些蛋白集的最小尺寸会导致与 COVID-19 相关的生物网络发生重大中断。我们用三组药物(Covid-Drug、Clinical-Drug 和 All-Drug)来评估得到的候选蛋白集。从 Covid-Drug 中获得了 18 种药物中的 16 种,从 Clinical-Drug 中获得了 328 种药物中的 273 种,以及大量的 All-Drug。在第二步中,我们研究了 COVID-19 相关蛋白集,并识别了对疾病病理至关重要的蛋白。使用 DAVID 进行此分析,以显示和比较 COVID-19 合并症之间贡献的必需蛋白。我们对共享蛋白的结果显示,心血管疾病、高血压、2 型糖尿病、肾脏疾病和肺部疾病显著富集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a59d/8087682/6f30dc3bb3b0/41598_2021_88427_Fig1_HTML.jpg

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