scholarly journals A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

2020 ◽  
Vol 8 ◽  
Author(s):  
Ushashi Banerjee ◽  
Santhosh Sankar ◽  
Amit Singh ◽  
Nagasuma Chandra
Keyword(s):  
Drug Targets ◽  
Metabolic Flux ◽  
Latent Tuberculosis ◽  
Interaction Network ◽  
New Drugs ◽  
Prolonged Treatment ◽  
Flux Analysis ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Global Population

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

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 Literature based discovery of alternative TCM medicine for adverse reactions to depression drugs

2020 ◽  
Vol 21 (S5) ◽  
Author(s):  
Qing Xie ◽  
Kyoung Min Yang ◽  
Go Eun Heo ◽  
Min Song
Keyword(s):  
Side Effects ◽  
Herbal Medicines ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
New Drugs ◽  
Protein Protein Interaction ◽  
Target Drug ◽  
Target Disease ◽  
Literature Based Discovery

Abstract Background In recent years, Traditional Chinese Medicine (TCM) and alternative medicine have been widely used along with western drugs as a complementary form of treatment. In this study, we first use the scientific literature to identify western drugs with obvious side effects. Then, we find TCM alternatives for these western drugs to ameliorate their side effects. Results We used depression as a case study. To evaluate our method, we showed the relation between herb-ingredients-target-disease for representative alternative herbs of western drugs. Further, a protein-protein interaction network of western drugs and alternative herbs was produced, and we performed enrichment analysis of the targets of the active ingredients of the herbs and examined the enrichment of Gene Ontology terms for Biological Process, Cellular Component, and Molecular Function and KEGG Pathway levels, to show how these targets affect different levels of gene expression. Conclusion Our proposed method is able to select herbs that are highly relevant to the target indication (depression) and are able to treat the side effects caused by the target drug. The compounds from our selected alternative herbal medicines can therefore be complementary to the western drugs and ameliorate their side effects, which may help in the development of new drugs.

scholarly journals Analysis of Protein-Protein Interaction Network of Laminopathy Based on Topological Properties

2018 ◽  
Vol 11 (2) ◽  
pp. 1091-1103
Author(s):  
Sapana Singh Yadav ◽  
Usha Chouhan
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Drug Targets ◽  
Genetic Disorders ◽  
Seed Proteins ◽  
Interaction Network ◽  
Large Degree ◽  
Closeness Centrality ◽  
Ppi Network ◽  
Protein Protein Interaction

Laminopathy is a group of rare genetic disorders, including EDMD, HGPS, Leukodystrophy and Lipodystrophy, caused by mutations in genes, encoding proteins of the nuclear lamina. Analysis of protein interaction network in the cell can be the key to understand; how complex processes, lead to diseases. Protein-protein interaction (PPI) in network analysis provides the possibility to quantify the hub proteins in large networks as well as their interacting partners. A comprehensive genes/proteins dataset related to Laminopathy is created by analysing public proteomic data and text mining of scientific literature. From this dataset the associated PPI network is acquired to understand the relationships between topology and functionality of the PPI network. The extended network of seed proteins including one giant network consisted of 381 nodes connected via 1594 edges (Fusion) and 390 nodes connected via 1645 edges (Coexpression), targeted for analysis. 20 proteins with high BC and large degree have been identified. LMNB1 and LMNA with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from giant network with high BC proteins presents a clear and visual overview which shows all important proteins of Laminopathy and the crosstalk between them. Finally, the robustness of central proteins and accuracy of backbone are validated by 248 test networks. Based on the network topological parameters such as degree, closeness centrality, betweenness centrality we found out that integrated PPIN is centred on LMNB1 and LMNA. Although finding of other interacting partners strongly represented as novel drug targets for Laminopathy.

scholarly journals The Protein-Protein Interaction Network of First Episode Psychosis and Schizophrenia Reveals Possible Trigger Factors and New Drug Targets among Intracellular Signal Transduction Pathways and Neurotoxicity Processes

2021 ◽  
Author(s):  
Michael Maes ◽  
Kitiporn Plaimas ◽  
Apichat Suratanee ◽  
Cristiano Noto ◽  
Buranee Kanchanatawan
Keyword(s):  
Protein Interaction ◽  
Drug Targets ◽  
Interaction Network ◽  
Molecular Complexes ◽  
First Episode Psychosis ◽  
First Episode ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Episode Psychosis ◽  
Molecular Patterns

There is evidence that schizophrenia is characterized by activation of the immune-inflammatory response (IRS) and compensatory immune-regulatory (CIRS) systems and lowered neuroprotection. Studies performed on antipsychotic-naïve first episode psychosis (AF-FEP) and schizophrenia (FES) patients are important as they may disclose the pathogenesis of the disease. However, the interactome of FEP/FES is not well delineated. The aim of the current study was to delineate the characteristics of the protein-protein interaction (PPI) network of AN-FEP and its transition to FES and the biological functions, pathways, and molecular patterns, which are over-represented in FEP/FES. PPI network analysis shows that FEP and FEP/FES are strongly associated with a response to a bacterium, TNF, NFκB, RELA, SP1, JAK-STAT, death receptor and TLR4 signaling, and tyrosine phosphorylation of STAT proteins. Specific molecular complexes of the peripheral immune response are associated with microglial activation, neuroinflammation and gliogenesis. FEP/FES is accompanied by lowered protection against inflammation in part attributable to dysfunctional miRNA maturation, deficits in neurotrophin/Trk, RTK and Wnt/catenin signaling and adherens junction organization. Lowered neuroprotection due to reduced neurotrophin/Trk and Wnt/catenin signaling, and DISC1 expression and multiple interactions between lowered BDNF, CDH1, CTNNB, and DISC1 expression, increase the vulnerability to the neurotoxic effects of immune products including cytokines and complement factors. All pathways or molecular patterns enriched in the interactome of FEP/FES are directly or indirectly affected by LPS. In summary: FEP appears to be triggered by a biotic stimulus (e.g. Gram-negative bacteria) which may induce neuro-immune toxicity cascades especially when anti-inflammatory and neurotrophic protections are deficient.

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