ArticleBMC bioinformatics2014
Computational identification of novel natural inhibitors of glucagon receptor for checking type II diabetes mellitus.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 23 citations in OpenAlex.
- GC-MS profiling and computational analysis of Balanites aegyptiaca phytoconstituents for antidiabetic activity: insights from network pharmacology and molecular docking.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
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- Gold nanoparticle-based beacon to detect STAT5b mRNA expression in living cells: a case optimized by bioinformatics screen.International journal of nanomedicine · 2015Article
- Interaction of Glucagon G-Protein Coupled Receptor with Known Natural Antidiabetic Compounds: Multiscoring In Silico Approach.Evidence-based complementary and alternative medicine : eCAM · 2015Article
- InCoB2014: bioinformatics to tackle the data to knowledge challenge. Introduction.BMC bioinformatics · 2014Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInteraction of the small peptide hormone glucagon with glucagon receptor (GCGR) stimulates the release of glucose from the hepatic cells during fasting; hence GCGR performs a significant function in glucose homeostasis. Inhibiting the interaction between glucagon and its receptor has been reported to control hepatic glucose overproduction and thus GCGR has evolved as an attractive therapeutic target for the treatment of type II diabetes mellitus.
resultsIn the present study, a large library of natural compounds was screened against 7 transmembrane domain of GCGR to identify novel therapeutic molecules that can inhibit the binding of glucagon with GCGR. Molecular dynamics simulations were performed to study the dynamic behaviour of the docked complexes and the molecular interactions between the screened compounds and the ligand binding residues of GCGR were analysed in detail. The top scoring compounds were also compared with already documented GCGR inhibitors- MK-0893 and LY2409021 for their binding affinity and other ADME properties. Finally, we have reported two natural drug like compounds PIB and CAA which showed good binding affinity for GCGR and are potent inhibitor of its functional activity.
conclusionThis study contributes evidence for application of these compounds as prospective small ligand molecules against type II diabetes. Novel natural drug like inhibitors against the 7 transmembrane domain of GCGR have been identified which showed high binding affinity and potent inhibition of GCGR.
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