Computational investigation for exploring botanical ingredients as potential negative allosteric modulators targeting metabotropic glutamate receptor 5

Background and purpose: Metabotropic Glutamate Receptors are class C G-protein-coupled receptors that respond to the neurotransmitter glutamate. In particular, metabotropic glutamate receptor 5 (mGluR5) has been a valuable drug target for the management of psychiatric and neurodegenerative disorders such as fragile X syndrome, autism, Depression, anxiety, addiction, and movement disorders through negative allosteric modulators (NAMs). Despite the discovery of several NAMs, limited clinical efficacy was observed, thus preventing them from reaching the clinical stage. Natural products are indispensable, with a plethora of pharmacological actions. This study aimed to identify potential natural compounds as novel natural NAMs modulating mGluR5 using a comprehensive in silico approach.

Experimental approach: Utilizing receptor-based pharmacophore modelling, we virtually screened a natural library comprised of ~1.2 million compounds to study their molecular binding behavior against mGluR5. Furthermore, the best hits were evaluated using the end-point method (molecular mechanics Poisson-Boltzmann surface area), molecular dynamics (MD) simulations, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling.

Findings/results: Based on the augmented computational modelling, we identified four promising natural hits-3'-HPS, gigantol, isorhapontigenin, and coclaurine that demonstrated higher affinities than the standard clinical candidate 2-methyl-6-phenylethynyl-pyridine (MPEP), and co-crystal 2-[2-(3-methoxyphenyl)ethynyl]-6-methyl-pyridine (M-MPEP). The top selected hits demonstrated optimal binding energies, molecular interactions, and favorable ADMET properties with a few toxicity liabilities, warranting further lead optimization and experimental validations. MD simulations validated their structural stability through RMSD, RMSF, and principal component analysis.

Conclusion and implications: Our findings provided compelling evidence for a structure-based drug design approach in developing potent small-molecule natural modulators for the treatment of neurodegenerative disorders.