Abstract
Human leukocyte antigen-related (PTP-LAR) is a receptor-like transmembrane phosphatase and a potential target for diabetes, obesity and cancer. In the present study, a sequence of in silico strategies (pharmacophore mapping, a 3D database searching, SADMET screening, and docking and toxicity studies) was performed to identify eight novel nontoxic PTP-LAR inhibitors. Twenty different pharmacophore hypotheses were generated using two methods; the best (hypothesis 2) consisted of three hydrogen-bond acceptor (A), one ring aromatic (R), and one hydrophobic aliphatic (Z) features. This hypothesis was used to screen molecules from several databases, such as Specs, IBS, MiniMaybridge, NCI, and an in-house PTP inhibitor database. In order to overcome the general bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinski’s rule of five and SADMET properties and validated by molecular docking studies using the available crystal structure 1LAR. These docking studies suggested the ligand binding pattern and interactions required for LAR inhibition. The docking analysis also revealed that sulfonylurea derivatives with an isoquinoline or naphthalene scaffold represent potential LAR drugs. The screening protocol was further validated using ligand pharmacophore mapping studies, which showed that the abovementioned interactions are indeed crucial and that the screened molecules can be presumed to possess potent inhibitory activities.
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Abbreviations
- PTP-LAR:
-
Human leukocyte antigen-related phosphatase
- PTPs:
-
Protein tyrosine phosphatases
- PTKs:
-
Protein tyrosine kinases
- IR:
-
Insulin receptor
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Acknowledgments
The authors thank the Department of Pharmaceuticals, the Minister of Chemicals and Fertilizers, the Department of Science and Technology (DST), and the Council of Scientific and Industrial Research (CSIR) New Delhi for financial assistance.
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Ajay, D., Sobhia, M.E. Identification of novel, less toxic PTP-LAR inhibitors using in silico strategies: pharmacophore modeling, SADMET-based virtual screening and docking. J Mol Model 18, 187–201 (2012). https://doi.org/10.1007/s00894-011-1037-0
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DOI: https://doi.org/10.1007/s00894-011-1037-0