scholarly journals Topological network based drug repurposing for coronavirus 2019

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255270
Author(s):  
Mahnaz Habibi ◽  
Golnaz Taheri
Keyword(s):  
Clinical Trials ◽  
Protein Interaction ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Diabetes Type 2 ◽  
Host Cells ◽  
Health Concern ◽  
Virus Protein ◽  
Human Virus ◽  
Fast Recognition

The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become the current health concern and threat to the entire world. Thus, the world needs the fast recognition of appropriate drugs to restrict the spread of this disease. The global effort started to identify the best drug compounds to treat COVID-19, but going through a series of clinical trials and our lack of information about the details of the virus’s performance has slowed down the time to reach this goal. In this work, we try to select the subset of human proteins as candidate sets that can bind to approved drugs. Our method is based on the information on human-virus protein interaction and their effect on the biological processes of the host cells. We also define some informative topological and statistical features for proteins in the protein-protein interaction network. We evaluate our selected sets with two groups of drugs. The first group contains the experimental unapproved treatments for COVID-19, and we show that from 17 drugs in this group, 15 drugs are approved by our selected sets. The second group contains the external clinical trials for COVID-19, and we show that 85% of drugs in this group, target at least one protein of our selected sets. We also study COVID-19 associated protein sets and identify proteins that are essential to disease pathology. For this analysis, we use DAVID tools to show and compare disease-associated genes that are contributed between the COVID-19 comorbidities. Our results for shared genes show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases. In the last part of this work, we recommend 56 potential effective drugs for further research and investigation for COVID-19 treatment. Materials and implementations are available at: https://github.com/MahnazHabibi/Drug-repurposing.

scholarly journals Drug Repurposing against SARS-CoV-2 RDRP - a computational quest against CoVID- 19

2020 ◽  
Author(s):  
Hirak Jyoti Chakrabortya ◽  
Prasenjit Paria ◽  
Aditi Gangopadhyay ◽  
Sayak Ganguli
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Homology Modelling ◽  
Treatment Strategies ◽  
Drug Repurposing ◽  
Definitive Treatment ◽  
Virus Protein ◽  
Phase I Clinical Trials ◽  
Time Saving ◽  
Human Virus

Abstract The present CoVID-19 pandemic was first detected in December 2019 in Wuhan, China, and is rapidly spreading worldwide. To date, it has affected 465,915 individuals in 200 countries, and has been responsible for 21,031 deaths. In the absence of definitive treatment strategies, there is a pressing demand for drug discovery against CoVID-19. Drug repurposing is a cost- effective and time-saving strategy which essentially involves the identification of novel targets for known drug candidates. This reduces the time and cost of drug discovery, as the pharmacokinetics and toxicity profiles of the drugs are already known, which makes phase-I clinical trials redundant. Here, we employed a computational drug repurposing strategy for identifying drug hits against the RNA-dependent RNA polymerase (RDRP) protein of CoVID-19. Analysis of the human-virus protein-protein associations revealed that the viral RDRP (NSP12) is associated with multiple host proteins that partake in cellular processes, which indicated that NSP12 could be a potential target for drug discovery. This, combined with the fact that the RDRP protein is a potential antiviral target in several viral diseases, led us to consider the NSP12 as a potential drug target for CoVID-19. Owing to the absence of an experimentally-derived structure in the PDB, we constructed the NSP12 protein of CoVID-19 by homology modelling, and the potential druggable sites were analysed. The 13,533 entries in DrugBank were initially screened using the sequence of CoVID-19 NSP12. The 7 hits thus identified were subjected to a consensus docking and scoring strategy for identifying hits against the druggable site of CoVID-19 NSP12. Analysis of the docking scores and protein- ligand interactions revealed that two hits – N-alpha-[(benzyloxy)carbonyl]-n-[(1r)-4- hydroxy-1-methyl-2-oxobutyl]-l-phenylalaninamide and S-[5-(trifluoromethyl)-4h-1,2,4- triazol-3-yl] 5-(phenylethynyl) furan-2 -carbothioate, had stronger binding affinity than remdesivir, which is being presently tested in clinical trials for its antiviral activity against CoVID-19. This indicated that these two compounds might be effective against CoVID-19, however, further experimentation is necessary for obtaining substantial evidence. We believe that the results of this study could offer a novel avenue for drug development against CoVID- 19.

scholarly journals Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masoumeh Adhami ◽  
Balal Sadeghi ◽  
Ali Rezapour ◽  
Ali Akbar Haghdoost ◽  
Habib MotieGhader
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Target Therapy ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Starting Point ◽  
Experimental Validations

Abstract Background 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. Results 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. Conclusions 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.

scholarly journals The Network of Interactions Between Classical Swine Fever Virus Nonstructural Protein p7 and Host Proteins

2020 ◽  
Vol 11 ◽  
Author(s):  
Jindai Fan ◽  
Mengru Zhang ◽  
Chenchen Liu ◽  
Mengjiao Zhu ◽  
Zilin Zhang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Classical Swine Fever ◽  
Cellular Protein ◽  
Host Cells ◽  
Cellular Proteins ◽  
Economic Losses ◽  
Host Proteins

Classical swine fever (CSF) is a highly contagious viral disease causing severe economic losses to the swine industry. As viroporins of viruses modulate the cellular ion balance and then take over the cellular machinery, blocking the activity of viroporin or developing viroporin-defective attenuated vaccines offers new approaches to treat or prevent viral infection. Non-structural protein p7 of CSF virus (CSFV) is a viroporin, which was highly involved in CSFV virulence. Deciphering the interaction between p7 and host proteins will aid our understanding of the mechanism of p7-cellular protein interaction affecting CSFV replication. In the present study, seven host cellular proteins including microtubule-associated protein RP/EB family member 1 (MAPRE1), voltage-dependent anion channel 1 (VDAC1), proteasome maturation protein (POMP), protein inhibitor of activated STAT 1 (PIAS1), gametogenetin binding protein 2 (GGNBP2), COP9 signalosome subunit 2 (COPS2), and contactin 1 (CNTN1) were identified as the potential interactive cellular proteins of CSFV p7 by using yeast two-hybrid (Y2H) screening. Plus, the interaction of CSFV p7 with MAPRE1 and VDAC1 was further evaluated by co-immunoprecipitation and GST-pulldown assay. Besides, the p7-cellular protein interaction network was constructed based on these seven host cellular proteins and the STRING database. Enrichment analysis of GO and KEGG indicated that many host proteins in the p7-cellular protein interaction network were mainly related to the ubiquitin-proteasome system, cGMP-PKG signaling pathway, calcium signaling pathway, and JAK-STAT pathway. Overall, this study identified potential interactive cellular proteins of CSFV p7, constructed the p7-cellular protein interaction network, and predicted the potential pathways involved in the interaction between CSFV p7 and host cells.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahnaz Habibi ◽  
Golnaz Taheri ◽  
Rosa Aghdam
Keyword(s):  
Biological Networks ◽  
Drug Repurposing ◽  
Diabetes Type 2 ◽  
Minimum Size ◽  
Essential Proteins ◽  
Fast Recognition ◽  
Associated Proteins ◽  
Significant Enrichment ◽  
Effective Drugs ◽  
Clinical Drug

AbstractThe 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.

scholarly journals COVID-19: A Drug Repurposing and Biomarker Identification by Using Comprehensive Gene-Disease Associations through Protein-Protein Interaction Network Analysis

2020 ◽  
Author(s):  
Suresh Kumar
Keyword(s):  
High Risk ◽  
Protein Interaction ◽  
Randomized Clinical Trials ◽  
Antiviral Drug ◽  
Interaction Network ◽  
Drug Repurposing ◽  
High Risk Group ◽  
Asymptomatic Group ◽  
Protein Protein Interaction ◽  
Key Genes

COVID-19 (2019-nCoV) is a pandemic disease with an estimated mortality rate of 3.4% (estimated by the WHO as of March 3, 2020). Until now there is no antiviral drug and vaccine for COVID-19. The current overwhelming situation by COVID-19 patients in hospitals is likely to increase in the next few months. About 15 percent of patients with serious disease in COVID-19 require immediate health services. Rather than waiting for new anti-viral drugs or vaccines that take a few months to years to develop and test, several researchers and public health agencies are attempting to repurpose medicines that are already approved for another similar disease and have proved to be fairly effective. This study aims to identify FDA approved drugs that can be used for drug repurposing and identify biomarkers among high- risk and asymptomatic groups. In this study gene-disease association related to COVID-19 reported mild, severe symptoms and clinical outcomes were determined. The high-risk group was studied related to SARS-CoV-2 viral entry and life cycle by using Disgenet and compared with curated COVID-19 gene data sets from the CTD database. The overlapped gene sets were enriched and the selected genes were constructed for protein-protein interaction networks. Through interactome, key genes were identified for COVID-19 and also for high risk and asymptomatic groups. The key hub genes involved in COVID-19 were VEGFA, TNF, IL-6, CXCL8, IL10, CCL2, IL1B, TLR4, ICAM1, MMP9. The identified key genes were used for drug-gene interaction for drug repurposing. The chloroquine, lenalidomide, pentoxifylline, thalidome, sorafenib, pacitaxel, rapamycin, cortisol, statins were proposed to be probable drug repurposing candidates for the treatment of COVID-19. However, these predicted drug candidates need to be validated through randomized clinical trials. Also, a key gene involved in high risk and the asymptomatic group were identified, which can be used as probable biomarkers for early identification.

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