Dataset Information

In Silico Prediction of the Mode of Action of Viola odorata in Diabetes.

ABSTRACT:

Background

The metabolic syndrome increases the risk of different diseases such as type 2 diabetes. The prevalence of metabolic syndrome has rapidly grown and affected more than 230 million people worldwide. Viola odorata is a traditionally used plant for the treatment of diabetes; however, its mechanism to manage diabetes is still unknown.

Purpose

This study was designed to systematically assess the mechanism of action of Viola odorata in diabetes.

Methods

An extensive literature search was made to establish an ingredient-target database of Viola odorata. Of these, targets related to diabetes were identified and used to develop a protein-protein interaction network (PPIN) by utilizing the STITCH database. The obtained PPIN was assessed through Gene Ontology (GO) enrichment analysis based on ClueGO plugin.

Results

According to the acquired data, there were about 143 chemical constituents present in Viola odorata having 119 protein targets. Of these, 31 targets were established to give the pharmacological effect against diabetes. The UniProt database was used for screening of 31 targets, out of which Homo sapiens contained 22 targets. Ultimately, 207 GO terms, grouped into 41 clusters, were found by gene analysis, and most of them were found to be linked with diabetes. According to findings, several proteins including TP53, BCL2, CDKN1A, 1L6, CCND1, CDKN2A, and RB1 have a significant role in the treatment of diabetes by Viola odorata.

Conclusion

The possible activity of Viola odorata in the management of diabetes may be mediated by several molecular mechanisms, including the glutamine metabolic process, IRE1-mediated unfolded protein response, and pentose metabolic process.

SUBMITTER: Buabeid MA

PROVIDER: S-EPMC7803256 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

<i>In Silico</i> Prediction of the Mode of Action of <i>Viola odorata</i> in Diabetes.

Buabeid Manal Ali MA Buabeid Manal Ali MA Arafa El-Shaimaa A EA Hassan Waseem W Murtaza Ghulam G

BioMed research international 20201031

<h4>Background</h4>The metabolic syndrome increases the risk of different diseases such as type 2 diabetes. The prevalence of metabolic syndrome has rapidly grown and affected more than 230 million people worldwide. <i>Viola odorata</i> is a traditionally used plant for the treatment of diabetes; however, its mechanism to manage diabetes is still unknown.<h4>Purpose</h4>This study was designed to systematically assess the mechanism of action of <i>Viola odorata</i> in diabetes.<h4>Methods</h4>An ...[more]

PMID: 33490239

Similar Datasets

Project description:The utilization of medicinal plants has long been explored for the discovery of antibacterial agents and the most effective mechanisms or new targets that can prevent and control the spread of antibiotic resistance. One kind of bacterial cell wall inhibition is the inactivation of the MurA enzyme that contributes to the formation of peptidoglycan. Another approach is to interfere with the cell-cell communication of bacteria called the Quorum sensing (QS) system. The blocking of auto-inducer such as gelatinase biosynthesis-activating pheromone (GBAP) can also suppress the virulence factors of gelatinase and serine protease. This research, in particular, aims to analyze lead compounds as antibacterial and anti-QS agents from Gambir (Uncaria gambir Roxburgh) through protein inhibition by in silico study. Antibacterial agents were isolated by bioactivity-guided isolation using a combination of chromatographic methods, and their chemical structures were determined by spectroscopic analysis methods. The in vitro antibacterial activity was evaluated by disc diffusion methods to determine inhibitory values. Meanwhile, in the in silico analysis, the compound of Uncaria gambir was used as ligand and compared with fosfomycin, ambuic acid, quercetin, and taxifolin as the standard ligand. These ligands were attached to MurA, GBAP, gelatinase, and serine proteases using Autodock Vina in PyRx 0.8 followed by PYMOL for combining the ligand conformation and proteins. plus programs to explore the complex, and visualized by Discovery Studio 2020 Client program. The antibacterial agent was identified as catechin that showed inhibitory activity against Enterococcus faecalis ATCC 29212 with inhibition zones of 11.70 mm at 10%, together with MIC and MBC values of 0.63 and 1.25 μg/mL, respectively. In the in silico study, the molecular interaction of catechin with MurA, GBAP, and gelatinase proteins showed good binding energy compared with two positive controls, namely fosfomycin and ambuic acid. It is better to use catechin-MurA (-8.5 Kcal/mol) and catechin-gelatinase (-7.8 Kcal/mol), as they have binding energies which are not marginally different from quercetin and taxifolin. On the other hand, the binding energy of serine protease is lower than quercetin, taxifolin, and ambuic acid. Based on the data, catechin has potency as an antibacterial through the inhibition of GBAP proteins, gelatinase, and serine protease that play a role in the QS system. This is the first discovery of the potential of catechin as an alternative antibacterial agent with an effective mechanism to prevent and control oral disease affected by antibiotic resistance.

Project description:TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype-phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype-phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype-phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.