Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis.
Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis.
复制标题
基于蛋白-蛋白相互作用网络分析的新型候选治疗药物的再利用
DOI：
10.1186/s12896-021-00680-z
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发表时间：
2021-03-12
影响因子：
3.5
通讯作者：
MotieGhader H
中科院分区：
文献类型：
--
作者：
Adhami M;Sadeghi B;Rezapour A;Haghdoost AA;MotieGhader H
新型冠状病毒病（COVID-19）在中国武汉首次出现，并迅速在全球蔓延。研究人员正试图尽快找到治疗这种疾病的方法。本研究旨在鉴定COVID-19相关基因，寻找新的药物靶向治疗。目前，还没有针对SARS-CoV-2的有效药物，同时，药物发现方法耗时且成本高昂。为了应对这一挑战，本研究利用基于网络的药物再利用策略来快速识别针对SARS-CoV-2的潜在药物。为此，通过蛋白-蛋白相互作用（PPI）网络分析，提出了7种治疗COVID-19的潜在药物。首先，收集了524种与SARS-CoV-2病毒相互作用的人类蛋白质，然后针对这些蛋白质重建了PPI网络。接下来，由于mirna在生物过程中的重要作用，从miRWalk 2.0数据库中分别获得了上述模块基因的靶mirna，并作为未来分析的重要线索进行报道。最后，从DGIDb数据库中获取药物靶向模块基因列表，并针对获得的蛋白质模块分别重构药物基因网络。基于PPI网络的网络分析，确定了7个蛋白质簇为与SARS-CoV-2病毒相关性更强的蛋白质复合物。此外，还确定了7种候选治疗药物来控制COVID-19的基因调控。紫杉醇是最有效的治疗候选药物，之前曾被提及作为COVID-19的治疗药物，在两个不同的模块中有四个基因靶点。其他6种候选药物，即硼替佐米、卡铂、克里唑替尼、阿糖胞苷、柔红霉素和伏立诺他，其中一些药物之前被发现对COVID-19有效，它们在不同的模块中有3个基因靶点。最终，卡铂、克里唑替尼和阿糖胞苷药物被发现为新的潜在药物，可以作为COVID-19的治疗药物进行研究。我们用于预测针对COVID-19的可重复使用候选药物的计算策略为治疗目的提供了有效和快速的结果。然而，需要进一步的实验分析和测试，如临床适用性，毒性和实验验证，以达到更准确和改进的治疗。我们提出的蛋白质复合物和相关的mirna，以及发现的候选药物，可能是其他研究人员在COVID-19大流行的紧急情况下进一步分析的起点。在线版本包含补充材料，可在10.1186/s12896-021-00680-z获得。
The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic. The online version contains supplementary material available at 10.1186/s12896-021-00680-z.
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影响因子：
12.7
作者：
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通讯作者：
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影响因子：
3.1
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作者：
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通讯作者：
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影响因子：
4.4
作者：
Adhami, Masoumeh;MotieGhader, Habib;Sadeghi, Balal
通讯作者：
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