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

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).

Tryptophan and thiosemicarbazide derivatives: design, synthesis, and biological evaluation as potential β-d-galactosidase and β-d-glucosidase inhibitors

“…Substrate concentrations were selected to approximate K m values for each enzyme. The percentage inhibition was determined from the residual activity for each compound by comparing the glycosidase activity with and without the synthesized compound Abu Khalaf et al, 2011).…”

“…The optimal shape-complemented pharmacophores were then used as 3D search queries to screen several available virtual molecular databases for new b-D-galactosidase and b-D-glucosidase inhibitors Abu Khalaf et al, 2011).…”

“…Pharmacophore models developed for b-D-glucosidase (three models) and b-D-galactosidase (five models) inhibitors were employed to screen our in house built structural database of natural compounds (contains 3,000 compounds) Abu Khalaf et al, 2011). One of the captured hits, 4a (Fig.…”

Mixing Pharmacophore Modeling and Classical QSAR Analysis as Powerful Tool for Lead Discovery

“…CATALYST pharmacophores have been used as 3D queries for database searching and in 3D-QSAR studies. [54][55][56][57][58][59][60][61][62][63][64][65][66] Although pharmacophore modeling employing HYPOGEN has been heavily reviewed in the literature, [68][69][70][71][72][73][74][75][76] a brief discussion of this algorithm is provided herein to allow better readability of the chapter.…”

“…We previously reported the successful use of this combination to probe the induced fit flexibilities of activated factor X 53 and towards the discovery of new inhibitory leads against glycogen synthase kinase-3β, 54 bacterial MurF, 55 protein tyrosine phosphatase, 56 DPP IV, 57 hormone sensitive lipase, 58 β-secretase, 59 influenza neuraminidase, 60 cholesteryl ester transfer protein, 61 cycline dependent kinase, 62 Heat shock protein, 63 estrogen receptor β, 64 β-D-Glucosidase, 65 and β-D-Galactosidase. 66 The author intends in this chapter to discuss the basic theoretical principles of this successful ligand-based approach and to provide interested audiences with experimental details related to this approach.…”