scholarly journals Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

2020 ◽  
Author(s):  
Sugandh Kumar ◽  
Pratima Kumari ◽  
Geetanjali Agnihotri ◽  
Preethy VijayKumar ◽  
Shaheerah Khan ◽  
...  
Keyword(s):  
Interaction Network ◽  
Viral Proteins ◽  
Inhibitory Effect ◽  
Host Proteins ◽  
Disease Manifestation ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approved Drugs ◽  
Viral Lifecycle ◽  
Fda Approved Drugs

<p>The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. </p> <p>In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect.</p> <p>Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.</p>

scholarly journals Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

2020 ◽  
Author(s):  
Sugandh Kumar ◽  
Pratima Kumari ◽  
Geetanjali Agnihotri ◽  
Preethy VijayKumar ◽  
Shaheerah Khan ◽  
...  
Keyword(s):  
Interaction Network ◽  
Viral Proteins ◽  
Inhibitory Effect ◽  
Host Proteins ◽  
Disease Manifestation ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approved Drugs ◽  
Viral Lifecycle ◽  
Fda Approved Drugs

<p>The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. </p> <p>In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect.</p> <p>Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.</p>

scholarly journals Identification of drugs targeting multiple viral and human proteins using computational analysis for repurposing against COVID-19

2020 ◽  
Author(s):  
Sugandh Kumar ◽  
Pratima Kumari ◽  
Geetanjali Agnihotri ◽  
Preethy V Kumar ◽  
Bharti Singh ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Differentially Expressed Genes ◽  
Computational Analysis ◽  
State Of The Art ◽  
Viral Proteins ◽  
Host Proteins ◽  
Disease Manifestation ◽  
Human Proteins ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract The SARS-CoV2 is a highly contagious pathogen that causes COVID-19 disease. It has affected millions of people globally with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against nine SARS-CoV2 proteins to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Additionally, we analyzed if the identified molecules can also affect the human proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) as a result of SARS-CoV2 infection were identified using NCBI-GEO data (GEO-ID: GSE-147507). Targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 74 molecules that can bind to various SARS-CoV2 and human host proteins. Their possible use in COVID-19 have also been reviewed in detail. We hope that this study will help development of multipotent drugs, simultaneously targeting the viral and host proteins, for the treatment of COVID-19.

scholarly journals Identifying potential key genes and existing drugs for Multiple sclerosis, Schizophrenia, and Autism- an in silico approach

2021 ◽  
Author(s):  
SOUVIK CHAKRABORTY ◽  
Sajal Dey ◽  
Sushmita Bhowmick
Keyword(s):  
Multiple Sclerosis ◽  
Interaction Network ◽  
Gene Interaction ◽  
Autism Spectrum ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Neurological Conditions ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Protein Protein Interaction Network

Nowadays, neurological conditions are a major concern as it not only preys on a patients health but also is a huge economic burden that is placed on the patients family. The diagnosis and treatment of disease sometimes cause methodological limitations. This is mainly common for individuals who have the signs of MS and schizophrenia (SZ). Patients suffering from multiple sclerosis are more likely to develop schizophrenia. Besides, a significant portion of patients who have been diagnosed with Autism Spectrum Disorder (ASD) later acquire the symptoms of Schizophrenia. In this study, we used bioinformatics tools to determine differentially expressed genes (DEGs) in all these diseases, and then we created a protein-protein interaction network using the online software STRING and identified 15 significant genes with the help of Cytohubba a plug-in tool in Cytoscape, the offline software (version3.8.2). We then used a drug-gene interaction database to conduct a drug-gene interaction study of the 15 hub genes and from there we showed that there are 37 existing FDA-approved drugs were obtained. These findings may provide a new and common therapeutic approach for MS, SZ, and ASD therapy.

scholarly journals Identification of 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Vicky Mody ◽  
Joanna Ho ◽  
Savannah Wills ◽  
Ahmed Mawri ◽  
Latasha Lawson ◽  
...  
Keyword(s):  
Inhibitory Effect ◽  
Dynamics Simulation ◽  
Simulation Studies ◽  
Inhibitory Effects ◽  
Major Threat ◽  
Main Protease ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Computational Molecular Modeling

AbstractEmerging outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is a major threat to public health. The morbidity is increasing due to lack of SARS-CoV-2 specific drugs. Herein, we have identified potential drugs that target the 3-chymotrypsin like protease (3CLpro), the main protease that is pivotal for the replication of SARS-CoV-2. Computational molecular modeling was used to screen 3987 FDA approved drugs, and 47 drugs were selected to study their inhibitory effects on SARS-CoV-2 specific 3CLpro enzyme in vitro. Our results indicate that boceprevir, ombitasvir, paritaprevir, tipranavir, ivermectin, and micafungin exhibited inhibitory effect towards 3CLpro enzymatic activity. The 100 ns molecular dynamics simulation studies showed that ivermectin may require homodimeric form of 3CLpro enzyme for its inhibitory activity. In summary, these molecules could be useful to develop highly specific therapeutically viable drugs to inhibit the SARS-CoV-2 replication either alone or in combination with drugs specific for other SARS-CoV-2 viral targets.

scholarly journals Selective killing of homologous recombination-deficient cancer cell lines by inhibitors of the RPA:RAD52 protein-protein interaction

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248941
Author(s):  
Mona Al-Mugotir ◽  
Jeffrey J. Lovelace ◽  
Joseph George ◽  
Mika Bessho ◽  
Dhananjaya Pal ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Protein Interaction ◽  
Ovarian Cancer Cells ◽  
Protein Protein Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Synthetic lethality is a successful strategy employed to develop selective chemotherapeutics against cancer cells. Inactivation of RAD52 is synthetically lethal to homologous recombination (HR) deficient cancer cell lines. Replication protein A (RPA) recruits RAD52 to repair sites, and the formation of this protein-protein complex is critical for RAD52 activity. To discover small molecules that inhibit the RPA:RAD52 protein-protein interaction (PPI), we screened chemical libraries with our newly developed Fluorescence-based protein-protein Interaction Assay (FluorIA). Eleven compounds were identified, including FDA-approved drugs (quinacrine, mitoxantrone, and doxorubicin). The FluorIA was used to rank the compounds by their ability to inhibit the RPA:RAD52 PPI and showed mitoxantrone and doxorubicin to be the most effective. Initial studies using the three FDA-approved drugs showed selective killing of BRCA1-mutated breast cancer cells (HCC1937), BRCA2-mutated ovarian cancer cells (PE01), and BRCA1-mutated ovarian cancer cells (UWB1.289). It was noteworthy that selective killing was seen in cells known to be resistant to PARP inhibitors (HCC1937 and UWB1 SYr13). A cell-based double-strand break (DSB) repair assay indicated that mitoxantrone significantly suppressed RAD52-dependent single-strand annealing (SSA) and mitoxantrone treatment disrupted the RPA:RAD52 PPI in cells. Furthermore, mitoxantrone reduced radiation-induced foci-formation of RAD52 with no significant activity against RAD51 foci formation. The results indicate that the RPA:RAD52 PPI could be a therapeutic target for HR-deficient cancers. These data also suggest that RAD52 is one of the targets of mitoxantrone and related compounds.

scholarly journals Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness

2021 ◽  
Author(s):  
Takatsugu Kosugi ◽  
Masahito Ohue
Keyword(s):  
Physicochemical Properties ◽  
Protein Interaction ◽  
Quantitative Estimate ◽  
Early Stage ◽  
Assessment Method ◽  
Early Screening ◽  
Protein Protein Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Discriminatory Performance

The quantification of drug-likeness is very useful for screening drug candidates. The quantitative estimate of drug-likeness (QED) is the most commonly used quantitative drug efficacy assessment method proposed by Bickerton <i>et al</i>. However, QED is not considered suitable for screening compounds that target protein-protein interactions (PPI), which have garnered significant interest in recent years. Therefore, we developed a method called the quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs and developed using the QED concept, involving modeling physicochemical properties based on the information available on the drug. QEPPI models the physicochemical properties of compounds that have been reported in the literature to act on PPIs. Compounds in iPPI-DB, which comprises PPI inhibitors and stabilizers, and FDA-approved drugs were evaluated using QEPPI. The results showed that QEPPI is more suitable for the early screening of PPI-targeting compounds than QED. QEPPI was also considered an extended concept of "Rules of Four" (RO4), a PPI inhibitor index proposed by Morelli <i>et al</i>. To compare the discriminatory performance of QEPPI and RO4, we evaluated their discriminatory performance using the datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. Results of the F-score of RO4 and QEPPI were 0.446 and 0.499, respectively. QEPPI demonstrated better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it could be used as an initial filter for efficient screening of PPI-targeting compounds, which has been difficult in the past.<br>

scholarly journals Insights into the molecular mechanism of anticancer drug ruxolitinib repurposable in COVID-19 therapy

2021 ◽  
Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal
Keyword(s):  
Biological Process ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Similar Response ◽  
Alveolar Cells ◽  
Protein Protein Interaction ◽  
Programming Tools ◽  
R Programming ◽  
Approved Drugs ◽  
Protein Protein Interaction Network

AbstractDue to non-availability of specific therapeutics against COVID-19, repurposing of approved drugs is a reasonable option. Cytokines imbalance in COVID-19 resembles cancer; exploration of anti-inflammatory agents, might reduce COVID-19 mortality. The current study investigates the effect of ruxolitinib treatment in SARS-CoV-2 infected alveolar cells compared to the uninfected one from the GSE5147507 dataset. The protein-protein interaction network, biological process and functional enrichment of differentially expressed genes were studied using STRING App of the Cytoscape software and R programming tools. The present study indicated that ruxolitinib treatment elicited similar response equivalent to that of SARS-CoV-2 uninfected situation by inducing defense response in host against virus infection by RLR and NOD like receptor pathways. Further, the effect of ruxolitinib in SARS-CoV-2 infection was mainly caused by significant suppression of IFIH1, IRF7 and MX1 genes as well as inhibition of DDX58/IFIH1-mediated induction of interferon-I and -II signalling.

scholarly journals Insight into Bacterial Virulence Mechanisms against Host Immune Response via the Yersinia pestis-Human Protein-Protein Interaction Network

2011 ◽  
Vol 79 (11) ◽  
pp. 4413-4424 ◽  
Author(s):  
Huiying Yang ◽  
Yuehua Ke ◽  
Jian Wang ◽  
Yafang Tan ◽  
Sebenzile K. Myeni ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Mapk Signaling ◽  
Human Protein ◽  
Content Type ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Protein Protein Interaction Network

ABSTRACTAYersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66Y. pestisbait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted byY. pestiswere significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted byY. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted byY. pestisare highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance ofY. pestisto phagocytosis. Interference with TLR and MAPK signaling pathways byY. pestisreflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting withY. pestis(16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibitin vitroactin assembly mediated by VASP.

scholarly journals Identifying diabetes-related important protein targets with few interacting partners with the PageRank algorithm

2015 ◽  
Vol 2 (4) ◽  
pp. 140252 ◽  
Author(s):  
Vince I. Grolmusz
Keyword(s):  
Early Diagnosis ◽  
Interaction Network ◽  
Mathematical Tool ◽  
Protein Targets ◽  
Pagerank Algorithm ◽  
Protein Protein Interaction ◽  
Mathematical Algorithm ◽  
Long Time ◽  
Human Proteins ◽  
Developed Nations

Diabetes is a growing concern for the developed nations worldwide. New genomic, metagenomic and gene-technologic approaches may yield considerable results in the next several years in its early diagnosis, or in advances in therapy and management. In this work, we highlight some human proteins that may serve as new targets in the early diagnosis and therapy. With the help of a very successful mathematical tool for network analysis that formed the basis of the early successes of Google TM , Inc., we analyse the human protein–protein interaction network gained from the IntAct database with a mathematical algorithm. The novelty of our approach is that the new protein targets suggested do not have many interacting partners (so, they are not hubs or super-hubs), so their inhibition or promotion probably will not have serious side effects. We have identified numerous possible protein targets for diabetes therapy and/or management; some of these have been well known for a long time (these validate our method), some of them appeared in the literature in the last 12 months (these show the cutting edge of the algorithm), and the remainder are still unknown to be connected with diabetes, witnessing completely new hits of the method.

scholarly journals Network controllability enrichment analysis reveals that SARS-CoV-2 infection tends to target indispensable nodes of a directed human protein-protein interaction network

2021 ◽  
Author(s):  
Ho-Joon Lee
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
A Genome ◽  
Human Proteins ◽  
Protein Protein Interaction Network ◽  
Network Controllability

The COVID-19 disease has been a global threat caused by the new coronavirus species, SARS-CoV-2, since early 2020 with an urgent need for therapeutic interventions. In order to provide insight into human proteins targeted by SARS-CoV-2, here we study a directed human protein-protein interaction network (dhPPIN) based on our previous work on network controllability of virus targets. We previously showed that human proteins targeted by viruses tend to be those whose removal in a dhPPIN requires more control of the network dynamics, which were classified as indispensable nodes. In this study we introduce a more comprehensive rank-based enrichment analysis of our previous dhPPIN for SARS-CoV-2 infection and show that SARS-CoV-2 also tends to target indispensable nodes in the dhPPIN using multiple proteomics datasets, supporting validity and generality of controllability analysis of viral infection in humans. Also, we find differential controllability among SARS-CoV-2, SARS-CoV-1, and MERS-CoV from a comparative proteomics study. Moreover, we show functional significance of indispensable nodes by analyzing heterogeneous datasets from a genome-wide CRISPR screening study, a time-course phosphoproteomics study, and a genome-wide association study. Specifically, we identify SARS-CoV-2 ORF3A as most frequently interacting with indispensable proteins in the dhPPIN, which are enriched in TGF-beta signaling and tend to be sources nodes and interact with each other. Finally, we built an integrated network model of ORF3A-interacting indispensable proteins with multiple functional supports to provide hypotheses for experimental validation as well as therapeutic opportunities. Therefore, a sub-network of indispensable proteins targeted by SARS-CoV-2 could serve as a prioritized network of drug targets and a basis for further functional and mechanistic studies from a network controllability perspective.

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