scholarly journals Network Pharmacology study of Curcuma longa L.: Potential Target Proteins and their Functional Enrichment Analysis.

2020 ◽  
Author(s):  
SANGEETA KUMARI
Keyword(s):  
Signaling Pathway ◽  
Mechanism Of Action ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Objective This study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein-protein interaction network.Results We used target proteins to create protein-protein interaction network (PPIN) and identified significant node and edge attributes of PPIN. We identified the cluster of proteins in the PPIN, which were used to identify enriched pathways. . We identified closeness centrality and jaccard score as most important node and edge attribute of the PPIN respectively. The enriched pathways of various clusters were overlapped suggesting synergistic mechanism of action. The three pathways found to be common among three clusters were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway.

scholarly journals Network pharmacology study of Curcuma longa L.: potential target proteins and their functional enrichment analysis

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Sangeeta Kumari ◽  
Hosahalli S. Subramanya
Keyword(s):  
Signaling Pathway ◽  
Mechanism Of Action ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Objective This study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein–protein interaction network. Results We used target proteins to create protein–protein interaction network (PPIN) and identified significant node and edge attributes of PPIN. We identified the cluster of proteins in the PPIN, which were used to identify enriched pathways. We identified closeness centrality and jaccard score as most important node and edge attribute of the PPIN respectively. The enriched pathways of various clusters were overlapped suggesting synergistic mechanism of action. The three pathways found to be common among three clusters were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway.

scholarly journals Network Pharmacology study of Curcuma longa L.: Potential Target Proteins and their Functional Enrichment Analysis. 

2020 ◽  
Author(s):  
SANGEETA KUMARI ◽  
Hosahalli S. Subramanya
Keyword(s):  
Signaling Pathway ◽  
Mechanism Of Action ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract ObjectiveThis study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein-protein interaction network.ResultsWe used target proteins to create protein-protein interaction network (PPIN) and identified significant node and edge attributes of PPIN. We identified the cluster of proteins in the PPIN, which were used to identify enriched pathways. We identified closeness centrality and jaccard score as most important node and edge attribute of the PPIN respectively. The enriched pathways of various clusters were overlapped suggesting synergistic mechanism of action. The three pathways found to be common among three clusters were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway.

scholarly journals Network Pharmacology Study of Curcuma Longa: Potential Target Proteins and their Functional Enrichment Analysis.

2020 ◽  
Author(s):  
SANGEETA KUMARI
Keyword(s):  
Signaling Pathway ◽  
Mechanism Of Action ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Function Module ◽  
Protein Protein Interaction Network

Abstract Objective: This study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein-protein interaction network.Results: We used target proteins to create protein-protein interaction network (PPI) and identified significant node and edge attributes of PPI. To find the function module, we identified the cluster of proteins which were further queried to GO enrichment analysis. Closeness centrality and jaccard score identified as most important node and edge attribute of the protein-protein interaction network respectively. The mapped pathways of various function module of the network were overlapped and showed synergistic mechanism of action. Three most important identified pathways were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway.

scholarly journals An Effective Statistical Integrative Algorithm (Aeiapp) for Protein Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 132-137 ◽  
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Network ◽  
Protein Identification ◽  
Research Work ◽  
Interaction Network ◽  
Integrative Approach ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

The task of predicting target proteins for new drug discovery is typically difficult. Target proteins are biologically most important to control a keen functional process. The recent research of experimental and computational -based approaches has been widely used to predict target proteins using biological networks analysis techniques. Perhaps with available methods and statistical algorithm needs to be modified and should be clearer to tag the main target. Meanwhile identifying wrong protein leads to unwanted molecular interaction and pharmacological activity. In this research work, a novel method to identify essential target proteins using integrative graph coloring algorithm has been proposed. The proposed integrative approach helps to extract essential proteins in protein-protein interaction network (PPI) by analyzing neighborhood of the active target protein. Experimental results reviewed based on protein-protein interaction network for homosapiens showed that AEIAPP based approach shows an improvement in the essential protein identification by assuming the source protein as biologically proven protein. The AEIAPP statistical model has been compared with other state of art approaches on human PPI for various diseases to produce good accurate outcome in faster manner with little memory consumption.

scholarly journals The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins

2019 ◽  
Author(s):  
Jarmila Nahálková
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Age Related ◽  
P53 Signaling Pathway ◽  
Protein Protein Interaction Network

The protein-protein interaction network of seven pleiotropic proteins (PIN7) contains proteins with multiple functions in the aging and age-related diseases (TPPII, CDK2, MYBBP1A, p53, SIRT6, SIRT7, and BSG). At the present work, the pathway enrichment, the gene function prediction and the protein node prioritization analysis were applied for the examination of main molecular mechanisms driving PIN7 and the extended network. Seven proteins of PIN7 were used as an input for the analysis by GeneMania, a Cytoscape application, which constructs the protein interaction network. The software also extends it using the interactions retrieved from databases of experimental and predicted protein-protein and genetic interactions. The analysis identified the p53 signaling pathway as the most dominant mediator of PIN7. The extended PIN7 was also analyzed by Cytohubba application, which showed that the top-ranked protein nodes belong to the group of histone acetyltransferases and histone deacetylases. These enzymes are involved in the reverse epigenetic regulation mechanisms linked to the regulation of PTK2, NFκB, and p53 signaling interaction subnetworks of the extended PIN7. The analysis emphasized the role of PTK2 signaling, which functions upstream of the p53 signaling pathway and its interaction network includes all members of the sirtuin family. Further, the analysis suggested the involvement of molecular mechanisms related to metastatic cancer (prostate cancer, small cell lung cancer), hemostasis, the regulation of the thyroid hormones and the cell cycle G1/S checkpoint. The additional data-mining analysis showed that the small protein interaction network MYBBP1A-p53-TPPII-SIRT6-CD147 controls Warburg effect and MYBBP1A-p53-TPPII-SIRT7-BSG influences mTOR signaling and autophagy. Further investigations of the detail mechanisms of these interaction networks would be beneficial for the development of novel treatments for aging and age-related diseases.

scholarly journals The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins

2019 ◽  
Author(s):  
Jarmila Nahálková
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Age Related ◽  
P53 Signaling Pathway ◽  
Protein Protein Interaction Network

The protein-protein interaction network of seven pleiotropic proteins (PIN7) contains proteins with multiple functions in the aging and age-related diseases (TPPII, CDK2, MYBBP1A, p53, SIRT6, SIRT7, and BSG). At the present work, the pathway enrichment, the gene function prediction and the protein node prioritization analysis were applied for the examination of main molecular mechanisms driving PIN7 and the extended network. Seven proteins of PIN7 were used as an input for the analysis by GeneMania, a Cytoscape application, which constructs the protein interaction network. The software also extends it using the interactions retrieved from databases of experimental and predicted protein-protein and genetic interactions. The analysis identified the p53 signaling pathway as the most dominant mediator of PIN7. The extended PIN7 was also analyzed by Cytohubba application, which showed that the top-ranked protein nodes belong to the group of histone acetyltransferases and histone deacetylases. These enzymes are involved in the reverse epigenetic regulation mechanisms linked to the regulation of PTK2, NFκB, and p53 signaling interaction subnetworks of the extended PIN7. The analysis emphasized the role of PTK2 signaling, which functions upstream of the p53 signaling pathway and its interaction network includes all members of the sirtuin family. Further, the analysis suggested the involvement of molecular mechanisms related to metastatic cancer (prostate cancer, small cell lung cancer), hemostasis, the regulation of the thyroid hormones and the cell cycle G1/S checkpoint. The additional data-mining analysis showed that the small protein interaction network MYBBP1A-p53-TPPII-SIRT6-CD147 controls Warburg effect and MYBBP1A-p53-TPPII-SIRT7-BSG influences mTOR signaling and autophagy. Further investigations of the detail mechanisms of these interaction networks would be beneficial for the development of novel treatments for aging and age-related diseases.

A Computational Framework to Identify Cross Association between Complex Disorders by Protein-Protein Interaction Network Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Nikhila T Suresh ◽  
Vimina E R ◽  
U. Krishnakumar
Keyword(s):  
Blood Pressure ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Complex Disorders ◽  
Protein Protein Interaction ◽  
Associated Proteins ◽  
Key Genes ◽  
Protein Protein Interaction Network

Objective: It is a known fact that numerous complex disorders do not happen in isolation indicating the plausible set of shared causes common to several different sicknesses. Hence, analysis of comorbidity can be utilized to explore association between several disorders. In this study, we have proposed a network-based computational approach, in which genes are organized based on the topological characteristics of the constructed Protein-Protein Interaction Network (PPIN) followed by a network prioritization scheme, to identify distinctive key genes and biological pathways shared among diseases. Methods: The proposed approach is initiated from constructed PPIN of any randomly chosen disease genes in order to infer its associations with other diseases in terms of shared pathways, co-expression, co-occurrence etc. For this, initially proteins associated to any disease based on random choice were identified. Secondly, PPIN is organized through topological analysis to define hub genes. Finally, using a prioritization algorithm a ranked list of newly predicted multimorbidity-associated proteins is generated. Using Gene Ontology (GO), cellular pathways involved in multimorbidity-associated proteins are mined. Result and Conclusion: The proposed methodology is tested using three disorders namely Diabetes, Obesity and blood pressure at an atomic level and the results suggest the comorbidity of other complex diseases that have associations with the proteins included in disease of present study through shared proteins and pathways. For diabetes, we have obtained key genes like GAPDH, TNF, IL6, AKT1, ALB, TP53, IL10, MAPK3, TLR4 and EGF with key pathways like P53 pathway, VEGF signaling pathway, Ras Pathway, Interleukin signaling pathway, Endothelin signaling pathway, Huntington disease etc. Study on other disorders such as obesity and blood pressure also revealed promising results.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein-protein interaction network

2021 ◽  
Author(s):  
Backiyarani Suthanthiram ◽  
Sasikala Rajendran ◽  
Sharmiladevi Simeon ◽  
Uma Subbaraya
Keyword(s):  
Candidate Genes ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Banana, one of the most important staple, delicious fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein-protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By exploring the PPI of candidate genes from the putative network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLV-WUSHEL signaling pathway in addition to gibberellin mediated auxin signaling pathway in parthenocarpy. Further validation of candidate genes in seeded and seedless accession of Musa spp using qRT-PCR put forward AGL8, MADS16, IAA (GH3.8), RGA1, EXPA1, GID1C, HK2 and BAM1 as possible target genes in natural parthenocarpy. In contrary, expression profile of ACLB-2 and ZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through protein-protein interaction network.

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