Network Pharmacology- and Molecular Dynamics Simulation-Based Bioprospection of Aspalathus linearis for Type-2 Diabetes Care

Abstract

The medicinal herb Aspalathus linearis (rooibos) is globally recognized in type-2 diabetes mellitus (T2DM) treatment due to its known and distinctive compounds. This work utilized network pharmacology (NP) coupled with molecular dynamics simulation in gaining new insight into the anti-diabetic molecular mechanism of action of rooibos teas. It looked at the interactions between rooibos constituents with various relevant protein receptors and signaling routes associated with T2DM progression. The initial analysis revealed 197 intersecting gene targets and 13 bioactive rooibos constituents linked to T2DM. The interactions between proteins and compounds to the target matrix were generated with the Cystoscope platform and STRING database. These analyses revealed intersecting nodes active in T2DM and hypoxia-inducible factor 1 (HIF-1) as an integral receptors target. In addition, KEGG analysis identified 11 other pathways besides the hub HIF-1 signaling route which may also be targeted in T2DM progression. In final molecular docking and dynamics simulation analysis, a significant binding affinity was confirmed for key compound-protein matrices. As such, the identified rooibos moieties could serve as putative drug candidates for T2DM control and therapy. This study shows rooibos constituents’ interaction with T2DM-linked signaling pathways and target receptors and proposes vitexin, esculin and isovitexin as well as apigenin and kaempferol as respective pharmacologically active rooibos compounds for the modulation of EGFR and IGF1R in the HIF-1 signaling pathway to maintain normal homeostasis and function of the pancreas and pancreatic β-cells in diabetics.

Insights into the Mechanism of Action of Helianthus annuus (Sunflower) Seed Essential Oil in the Management of Type-2 Diabetes Mellitus Using Network Pharmacology and Molecular Docking Approaches

“…This means that the most negative docking value will present the best orientation and inhibition of the enzyme [67]. Hence, the highest docking scores of phylloquinone (against PPARA), linoleic acid (FABP4), tricosylic acid (PPARD), lignoceric acid (PPARG), and stearic acid (CPT2) are indications of significant binding affinity towards the respective targets [68], thus promoting better complex stabilities. Consistent with observation on the binding affinity, the understanding of the kind of interaction existing between the five targets and respective phytoconstituents is key to providing information on the mechanism of action of the latter against T2D [57].…”

“…Summarily, the highest docking scores of identified compounds (phylloquinone, linoleic acid, tricosylic acid, lignoceric acid, and stearic acid) revealed the best affinities against respective targets (PPARA, FABP4, PPARD, and PPARG CPT2), as also corroborated by their high number of interactions (except stearic acid, as replaced by tricosylic acid against CPT2) in comparison with other compounds and standards, indicating their superiority. However, since the PPAR signaling pathway is concerned with diabetes and obesity emergence via the downregulation of the PPARA, FABP4, PPARD, PPARG, and CPT2 genes, and coupled with the fact that phylloquinones, linoleic acid, tricosylic acid, and lignoceric acid maintained good stabilities with these targets or genes based on molecular docking evaluation, that these four compounds could serve as probable PPAR ligands and as potential therapeutic choices against T2D, obesity, and insulin resistance [50,73] brought about by the impairment of insulin signaling [68], thereby suggesting them as probable compounds that could be further developed into drug candidates for insulin sensitization and T2D management [74]. Notwithstanding the aforementioned, the number of genes attributed to a signaling pathway is measured by its rich factor or strength [75]; thus, the higher the rich factor, the greater the degree of enrichment [68].…”

“…However, since the PPAR signaling pathway is concerned with diabetes and obesity emergence via the downregulation of the PPARA, FABP4, PPARD, PPARG, and CPT2 genes, and coupled with the fact that phylloquinones, linoleic acid, tricosylic acid, and lignoceric acid maintained good stabilities with these targets or genes based on molecular docking evaluation, that these four compounds could serve as probable PPAR ligands and as potential therapeutic choices against T2D, obesity, and insulin resistance [50,73] brought about by the impairment of insulin signaling [68], thereby suggesting them as probable compounds that could be further developed into drug candidates for insulin sensitization and T2D management [74]. Notwithstanding the aforementioned, the number of genes attributed to a signaling pathway is measured by its rich factor or strength [75]; thus, the higher the rich factor, the greater the degree of enrichment [68]. Intriguingly, the PPAR pathway established the highest degree of enrichment compared with other signaling routes; therefore, the present work buttresses the mechanism of action associated with the PPAR route and PPARA gene.…”

Two-carbon tethered artemisinin–isatin hybrids: design, synthesis, anti-breast cancer potential, and in silico study

“…The solvent-accessible surface area (SASA) is calculated by the van der Waals forces interacting with solvent molecules to calculate the solute area ( Akoonjee et al, 2022 ). The lower SASA can be explained by stronger hydrophobic interactions and less inter-complex solvent water, that is, the more compact binding between the 4a -key target protein complexes ( VAN DAN BURG et al, 1994 ).…”

Translation of preclinical ethnomedicine data in LMICs: the example of rooibos

“…The robustness of the comprehensive preclinical data set generated by this group, is evident from the similarly positive data by other research groups illustrating benefits of rooibos in other rodent models of diabetes ( Son et al, 2013 ; Kamakura et al, 2015 ), as well as in L6 myoblasts and pancreatic β-cells ( Kamakura et al, 2015 ; Himpe et al, 2016 ). New technology was recently used to confirm known effects and mechanisms, e.g., via targeted cellomics screening ( Pringle et al, 2021 ) and network pharmacology- and molecular dynamics simulation-based bioprospecting ( Akoonjee et al, 2022 ). However, despite the robust preclinical data, translation into human models are still lacking.…”