Abstract
In the present study, we utilized virtual screening to identify LPA(3) antagonists. We have developed a three-point structure-based pharmacophore model based on known LPA(3) antagonists. This model was used to mine the NCI database. Docking, pharmacophore development, and database mining produced new, non-lipid leads. Experimental testing of seven computationally selected pharmacophore hits produced one potentiator and three antagonists, one of which displays both LPA(3) selectivity and nanomolar potency. Similarity searching in the ChemBridge database using the most promising lead as the search target produced four additional LPA(3) antagonists and a potent dual LPA(1&2) antagonist.
Publication types
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Research Support, N.I.H., Extramural
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Research Support, Non-U.S. Gov't
MeSH terms
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Animals
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CHO Cells
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Calcium / chemistry
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Calcium / metabolism
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Calcium Signaling / physiology
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Cattle
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Cell Line, Tumor
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Computer Simulation*
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Cricetinae
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Cricetulus
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Diglycerides / chemistry
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Diglycerides / pharmacology
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Isoxazoles / chemistry
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Isoxazoles / pharmacology
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Lysophospholipids / agonists
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Lysophospholipids / antagonists & inhibitors*
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Lysophospholipids / metabolism
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Models, Chemical*
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Models, Molecular*
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Propionates / chemistry
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Propionates / pharmacology
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Protein Isoforms / antagonists & inhibitors
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Protein Isoforms / metabolism
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Rats
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Receptors, Lysophosphatidic Acid / antagonists & inhibitors
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Receptors, Lysophosphatidic Acid / biosynthesis
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Receptors, Lysophosphatidic Acid / genetics
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Rhodopsin / chemistry
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Structural Homology, Protein
Substances
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3-(4-(4-((1-(2-chlorophenyl)ethoxy)carbonyl amino)-3-methyl-5-isoxazolyl) benzylsulfanyl) propanoic acid
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Diglycerides
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Isoxazoles
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Lysophospholipids
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Propionates
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Protein Isoforms
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Receptors, Lysophosphatidic Acid
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Rhodopsin
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lysophosphatidic acid
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Calcium