The design of biological active compounds from the apoenzyme is still a challenging task. Herein a simple yet efficient technique is reported to generate a receptor based pharmacophore solely using a ligand-free protein crystal structure. Human leukocyte antigen-related phosphatase (PTP-LAR) is an apoenzyme and a receptor like transmembrane phosphatase that has emerged as a drug target for diabetes, obesity and cancer. The prior knowledge of the active residues responsible for the mechanism of action of the protein was used to generate the LUDI interaction map. Then, the complement negative image of the binding site was used to generate the pharmacophore features. A unique strategy was followed to design a pharmacophore query maintaining crucial interactions with all the active residues, essential for the enzyme inhibition. The same query was used to screen several databases consisting of the Specs, IBS, MiniMaybridge, NCI and an in-house PTP inhibitor databases. In order to overcome the common bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinskis Rule of Five, SADMET properties and validated by docking studies in Glide and GOLD. These docking studies not only suggest the essential ligand binding interactions but also the binding patterns necessary for the LAR inhibition. The ligand pharmacophore mapping studies further validated the screened protocol and supported that the final screened molecules, presumably, showed potent inhibitory activity. Subsequently, these molecules were subjected to Derek toxicity predictions and nine new molecules with different scaffold were obtained as non-toxic PTP-LAR inhibitors. The present prospective strategy is a powerful technique to identify potent inhibitors using the protein 3D structure alone and is a valid alternative to other structure-based and random docking approaches.
Keywords: Human leukocyte antigen-related phosphatase (PTP-LAR), receptor-based pharmacophore model, SADMET based virtual screening, inhibitors, docking, apoenzyme, diabetes, Lipinski's Rule of Five, QSAR, in silico studies
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