The endocannabinoid signaling system influences of a wide range of physiological processes and is the target for potential therapeutics to treat important medical conditions including pain, drug abuse disorders, obesity, metabolic syndrome, and neurodegenerative/neuroinflammatory diseases. The balance between endocannabinoid production and degradation is a decisive regulator of endocannabinoid signaling. 2- arachidonoylglycerol (2-AG), a principal endocannabinoid, is hydrolyzed mainly by monoacylglycerol lipase (MGL). An increase of 2-AG following MGL inhibition is considered therapeutic against pain, inflammation, and neurodegenerative/neuroinflammatory disorders including Alzheimer's and Parkinson's disease. This proposal advocates a high-throughput virtual screening method which will identify potent inhibitors selective for MGL over vs. brain serine hydrolases including fatty acid amide hydrolase (FAAH). Initially, the 3D atomic structures of MGL and related off-target enzymes will be generated and refined. Molecular dynamics simulations based on these structures will be used to obtain an ensemble of conformations for each enzyme. Small-molecule probes will be used to elucidate binding hot-spots that are conserved across the ensemble. These hot-spots will be represented as pharmacophore models, wherein the size of each pharmacophore element will reflect the flexibility of that binding region. The pharmacophore models will then be incorporated into a parallel screening platform which will enable rapid mining of large virtual databases for potent and selective MGL inhibitors. Acquisition and biochemical verification of identified hits will determine the potency and selectivity of the selected compounds. This work will generate a drug-discovery tool that can identify promising therapeutic hits for modulating endocannabinoid signaling and test a paradigm that may be applied to other, non-cannabinoid, targets.
2-arachidonoylglycerol (2-AG) is a signaling lipid which is degraded by monoacylglycerol lipase (MGL). Compounds which inhibit MGL, and thereby increase the level of 2-AG, are considered therapeutic towards pain, inflammation, and neurodegenerative/neuroinflammatory disorders including Alzheimer's and Parkinson's disease. This proposal aims to develop a fast, computational approach to search large databases of compounds in order to identify novel, potent and selective MGL inhibitors.
|Bowman, Anna L; Makriyannis, Alexandros (2013) Highly predictive ligand-based pharmacophore and homology models of ABHD6. Chem Biol Drug Des 81:382-8|
|Bowman, Anna L; Makriyannis, Alexandros (2011) Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH). J Chem Inf Model 51:3247-53|