scholarly journals In-silico Identification of Novel Drug Target for Osteoarthritisinhuman using System Network Biology Approaches

2020 ◽  
Author(s):  
Neha srivastava ◽  
Nityendra Shukla ◽  
Aditya Trivedi ◽  
Dr Prachi srivastava ◽  
Prof Prahlad Kishore Seth
Keyword(s):  
Drug Target ◽  
Treatment Options ◽  
Network Biology ◽  
Ppi Network ◽  
Joint Injury ◽  
Obesity Epidemic ◽  
Protein Protein Interaction ◽  
In Silico Identification ◽  
Disease Modifying Treatment ◽  
Novel Drug

<p>Osteoarthritis (OA) is the most common form of joint disability in the world affecting a large number of persons s yet the mechanisms responsible for the disease is not well n understood. And therefore there is a lack of disease-modifying treatment options. It has several risk factors from systemic (e.g. age, sex, genetics, obesity) to biochemical factors (e.g. joint injury, muscle weakness, sport). The prevalence of OA is ever increasing due to the obesity epidemic and longevity. Since OA has strong genetic predisposition, in the study we attempted system network biology approach to identify a key candidate gene in a protein-protein interaction (PPI) network of OA, which may play an important role in disease pathogenesis and help us to understand the development and progression of the disease. This information will help in target specific development of new molecules which may eventually lead to curative solutions for OA in human.</p>

scholarly journals In-silico Identification of Novel Drug Target for Osteoarthritisinhuman using System Network Biology Approaches

2020 ◽  
Author(s):  
Neha srivastava ◽  
Nityendra Shukla ◽  
Aditya Trivedi ◽  
Dr Prachi srivastava ◽  
Prof Prahlad Kishore Seth
Keyword(s):  
Drug Target ◽  
Treatment Options ◽  
Network Biology ◽  
Ppi Network ◽  
Joint Injury ◽  
Obesity Epidemic ◽  
Protein Protein Interaction ◽  
In Silico Identification ◽  
Disease Modifying Treatment ◽  
Novel Drug

<p>Osteoarthritis (OA) is the most common form of joint disability in the world affecting a large number of persons s yet the mechanisms responsible for the disease is not well n understood. And therefore there is a lack of disease-modifying treatment options. It has several risk factors from systemic (e.g. age, sex, genetics, obesity) to biochemical factors (e.g. joint injury, muscle weakness, sport). The prevalence of OA is ever increasing due to the obesity epidemic and longevity. Since OA has strong genetic predisposition, in the study we attempted system network biology approach to identify a key candidate gene in a protein-protein interaction (PPI) network of OA, which may play an important role in disease pathogenesis and help us to understand the development and progression of the disease. This information will help in target specific development of new molecules which may eventually lead to curative solutions for OA in human.</p>

scholarly journals 4E Interacting Protein as a Potential Novel Drug Target for Nucleoside Analogues in Trypanosoma Brucei

2021 ◽  
Vol 9 (4) ◽  
pp. 826
Author(s):  
Dorien Mabille ◽  
Camila Cardoso Santos ◽  
Rik Hendrickx ◽  
Mathieu Claes ◽  
Peter Takac ◽  
...  
Keyword(s):  
Mode Of Action ◽  
Drug Target ◽  
Drug Targets ◽  
De Novo ◽  
Treatment Options ◽  
Adenosine Kinase ◽  
Nucleoside Analogues ◽  
Purine Salvage ◽  
Interacting Protein ◽  
Novel Drug

Human African trypanosomiasis is a neglected parasitic disease for which the current treatment options are quite limited. Trypanosomes are not able to synthesize purines de novo and thus solely depend on purine salvage from the host environment. This characteristic makes players of the purine salvage pathway putative drug targets. The activity of known nucleoside analogues such as tubercidin and cordycepin led to the development of a series of C7-substituted nucleoside analogues. Here, we use RNA interference (RNAi) libraries to gain insight into the mode-of-action of these novel nucleoside analogues. Whole-genome RNAi screening revealed the involvement of adenosine kinase and 4E interacting protein into the mode-of-action of certain antitrypanosomal nucleoside analogues. Using RNAi lines and gene-deficient parasites, 4E interacting protein was found to be essential for parasite growth and infectivity in the vertebrate host. The essential nature of this gene product and involvement in the activity of certain nucleoside analogues indicates that it represents a potential novel drug target.

scholarly journals Prediction of a potential drug target based on protein druggability for thyroid cancer

2020 ◽  
Author(s):  
YiFei Yang ◽  
Bin Yu ◽  
Xiu-Xia Zhang ◽  
Yun-Hua Zhu
Keyword(s):  
Thyroid Cancer ◽  
Targeted Therapy ◽  
Drug Target ◽  
Drug Repositioning ◽  
The Cancer Genome Atlas ◽  
Targeted Drugs ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas ◽  
Targeted Drug

Abstract Background: Thyroid cancer is a common endocrine malignancy; however, its treatment is still surgical. With the development and application of targeted therapy in cancer treatment, there are great development prospects in researching targeted drugs for thyroid cancer. Methods: Differentially expressed mRNAs between thyroid cancerous tissue and normal thyroid tissues were screened from The Cancer Genome Atlas (TCGA) database. Using weighted gene coexpression network analysis (WGCNA) to build co-expression modules and combined with differentially methylated gene (DMG) analysis. The druggability was analyzed by PockDrug-Server. Due to drug repositioning to seek targeted drugs to treat thyroid cancer we constructed a protein-protein interaction (PPI) network, and screened out a drug target of thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were used to analysis the protein enrichment of PPI network. Results: In the present study, the red module was significantly correlated with thyroid cancer. With DMG analysis, we screened out three genes: HEY2 , TNIK and LRP4 . These three genes were hypomethylation in tumors. The druggability based on PockDrug-Server predicted that only TNIK had protein pocket druggability. With PPI model for TNIK, there were ten genes interacted with TNIK. These genes were enriched in the MAPK and Wnt pathways, which are correlated with tumor proliferation, differentiation, and development. Upon searching for drugs against these 10 genes in Drugbank, it was determined that the targeted drug Binimetinib which is MEK1/2 inhibition. Therefore, we hypothesized that Binimetinib can be used as a targeted drug and TNIK can be regard as drug target for thyroid cancer therapy. Conclusion: Our research provides a bioinformatics method for screening drugs target and provides a theoretical basis for targeted therapy for thyroid cancer.

scholarly journals A Signaling Network based Computational Model to Uncover Loop as the Novel Molecular Mechanisms for Medulloblastoma

2019 ◽  
Author(s):  
Jielin Xu ◽  
Fuhai Li
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanisms ◽  
Dynamic Models ◽  
Treatment Time ◽  
Treatment Options ◽  
Post Treatment ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Recommendation Algorithms ◽  
Network Patterns

AbstractMedulloblastoma (MB) is the most common malignant brain tumor in children. Despite aggressive therapy, about one-third of patients with MB still die, and survivors suffer severe long-term side effects due to the treatments. The poor post-treatment outcomes are tightly linked to unpredictable drug resistance. Therefore, before developing robust single drug or drug combination recommendation algorithms, uncovering the underlying protein-protein interaction (PPI) network patterns that accurately explain and predict drug resistances for MB subtypes is essential and important. In this study, we hypothesize that the loop sub-structure within the PPI network can explain and predict drug resistance. Both static and dynamic models are built to evaluate this hypothesis for three MB subtypes. Specifically, a static model is created to first validate that many reported therapeutic targets are located topologically on highly deregulated loop sub-structure and then to characterize the loop for tumors without treatment. Next, with the after-treatment time-series genomics data, a dynamic hidden Markov model (HMM) with newly designed initialization scheme estimates the successful and unsuccessful occurrence probabilities for each given PPI and then re-delineates the loop for post-treatment tumors. Finally, the comparison of loop structures pre- and post-treatment distinguishes effective and ineffective treatment options, demonstrating that the loop sub-structure is capable of interpreting the mechanism of drug resistance. In summary, effective treatments show much stronger inhibition of cell cycle and DNA replication proteins when compared to ineffective treatments after considering the cross talk of multiple pathways (the loop).

scholarly journals Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yanghe Feng ◽  
Qi Wang ◽  
Tengjiao Wang
Keyword(s):  
Protein Interactions ◽  
Drug Target ◽  
Drug Targets ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Protein Protein Interaction Networks

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper.

Are Carbonic Anhydrases Suitable Targets to Fight Protozoan Parasitic Diseases?

2019 ◽  
Vol 25 (39) ◽  
pp. 5266-5278 ◽  
Author(s):  
Katia D'Ambrosio ◽  
Claudiu T. Supuran ◽  
Giuseppina De Simone
Keyword(s):  
Drug Resistance ◽  
Animal Models ◽  
Carbonic Anhydrase ◽  
Drug Target ◽  
Treatment Options ◽  
Parasitic Diseases ◽  
Carbonic Anhydrases ◽  
Extensive Drug Resistance ◽  
Protozoan Infections

Protozoans belonging to Plasmodium, Leishmania and Trypanosoma genera provoke widespread parasitic diseases with few treatment options and many of the clinically used drugs experiencing an extensive drug resistance phenomenon. In the last several years, the metalloenzyme Carbonic Anhydrase (CA, EC 4.2.1.1) was cloned and characterized in the genome of these protozoa, with the aim to search for a new drug target for fighting malaria, leishmaniasis and Chagas disease. P. falciparum encodes for a CA (PfCA) belonging to a novel genetic family, the η-CA class, L. donovani chagasi for a β-CA (LdcCA), whereas T. cruzi genome contains an α-CA (TcCA). These three enzymes were characterized in detail and a number of in vitro potent and selective inhibitors belonging to the sulfonamide, thiol, dithiocarbamate and hydroxamate classes were discovered. Some of these inhibitors were also effective in cell cultures and animal models of protozoan infections, making them of considerable interest for the development of new antiprotozoan drugs with a novel mechanism of action.

Identification of Antineoplastic Targets with Systems Approaches, Using Resveratrol as an In-Depth Case Study

2017 ◽  
Vol 23 (32) ◽  
pp. 4773-4793 ◽  
Author(s):  
Nivedita Singh ◽  
Sherry Freiesleben ◽  
Olaf Wolkenhauer ◽  
Yogeshwer Shukla ◽  
Shailendra K. Gupta
Keyword(s):  
Drug Target ◽  
Complex Disease ◽  
Biological Targets ◽  
Systems Approaches ◽  
Anticancer Mechanism ◽  
Genetic And Environmental Factors ◽  
Key Steps ◽  
Network Approaches ◽  
Novel Drug

The identification and validation of novel drug–target combinations are key steps in the drug discovery processes. Cancer is a complex disease that involves several genetic and environmental factors. High-throughput omics technologies are now widely available, however the integration of multi-omics data to identify viable anticancer drug-target combinations, that allow for a better clinical outcome when considering the efficacy-toxicity spectrum, is challenging. This review article provides an overview of systems approaches which help to integrate a broad spectrum of technologies and data. We focus on network approaches and investigate anticancer mechanism and biological targets of resveratrol using reverse pharmacophore mapping as an in-depth case study. The results of this case study demonstrate the use of systems approaches for a better understanding of the behavior of small molecule inhibitors in receptor binding sites. The presented network analysis approach helps in formulating hypotheses and provides mechanistic insights of resveratrol in neoplastic transformations.

Protein-Protein Interaction (PPI) Network: Recent Advances in Drug Discovery

2017 ◽  
Vol 18 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Alexiou Athanasios ◽  
Vairaktarakis Charalampos ◽  
Tsiamis Vasileios ◽  
Ghulam Ashraf
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Recent Advances

In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

2019 ◽  
Vol 19 (2) ◽  
pp. 146-155 ◽  
Author(s):  
Renu Chaudhary ◽  
Meenakshi Balhara ◽  
Deepak Kumar Jangir ◽  
Mehak Dangi ◽  
Mrridula Dangi ◽  
...  
Keyword(s):  
Network Analysis ◽  
Invasive Aspergillosis ◽  
Protein Interaction ◽  
Drug Target ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Homology Model ◽  
Ppi Network ◽  
Virulence Proteins ◽  
Hub Proteins

<P>Background: Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. </P><P> Materials & Method: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.Conclusion:Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.</P>

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