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Discovery of new β-d-glucosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening

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Abstract

Glycosidases, including β-d-glucosidase, are involved in a variety of metabolic disorders such as diabetes, viral or bacterial infections and cancer. Accordingly, we were prompted to find new β-d-glucosidase inhibitors. Towards this end we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Three pharmacophores emerged in the QSAR equations, suggesting the existence of more than one binding mode accessible to ligands within the β-d-glucosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic (ROC) curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were established experimentally by the identification of several β-d-glucosidase inhibitors retrieved via in silico search of two structural databases, namely the National Cancer Institute (NCI) list of compounds, and our in-house structural database of established drugs and agrochemicals (DAC).

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Acknowledgment

This work has been financially supported by the Deanship of Scientific Research at the University of Jordan; this support is highly acknowledged.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, Faculty of Pharmacy, Al Zaytoonah Private University of Jordan, Amman, Jordan

    Reema Abu Khalaf

  2. Department of Chemistry, Faculty of Science, University of Jordan, Amman, Jordan

    Ahmed Mutanabbi Abdula & Mohammad S. Mubarak

  3. Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan

    Mutasem O. Taha

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  1. Reema Abu Khalaf
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  2. Ahmed Mutanabbi Abdula
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  3. Mohammad S. Mubarak
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  4. Mutasem O. Taha
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Correspondence to Mutasem O. Taha.

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Khalaf, R.A., Abdula, A.M., Mubarak, M.S. et al. Discovery of new β-d-glucosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening. J Mol Model 17, 443–464 (2011). https://doi.org/10.1007/s00894-010-0737-1

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  • DOI: https://doi.org/10.1007/s00894-010-0737-1

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