FFA1-selective agonistic activity based on docking simulation using FFA1 and GPR120 homology models.

Area of Science:

• Pharmacology
• Molecular Biology
• Computational Chemistry

Background:

• Free fatty acid receptors (FFA1 and GPR120) are G protein-coupled receptors (GPCRs) activated by medium- and long-chain fatty acids.
• These receptors play crucial roles in regulating insulin and glucagon-like peptide-1 (GLP-1) secretion.
• Selective pharmacological tools are needed to differentiate the functions of FFA1 and GPR120 due to similar ligand properties.

Purpose of the Study:

• To develop and validate a computational approach for identifying selective FFA1 receptor agonists.
• To utilize docking simulations with homology models to predict compound activity and selectivity.
• To assess the biological activity of predicted agonists in cellular assays and insulin secretion.

Main Methods:

• Homology modeling and molecular docking simulations were employed for FFA1 receptor and GPR120.
• Biological activity was measured by ERK phosphorylation and intracellular calcium ([Ca(2+)]i) levels in transfected cells.
• Insulin secretion was assessed in murine pancreatic beta cells (MIN6).

Main Results:

• Docking simulation-derived hydrogen bonding energies correlated well with experimental ERK phosphorylation data (R(2) = 0.65 for FFA1, R(2) = 0.76 for GPR120).
• Compound NCG75, predicted as a selective FFA1 agonist, showed potent activation of ERK and [Ca(2+)]i, comparable to a known agonist.
• Site-directed mutagenesis confirmed distinct amino acid residues involved in FFA1 receptor recognition and activation.

Conclusions:

• Docking simulations using homology models are effective for predicting selective FFA1 receptor agonists.
• The identified compound NCG75 demonstrates potent FFA1 receptor activation and insulinotropic effects.
• This computational approach provides a valuable strategy for discovering novel pharmacological tools for studying FFA receptors.