α‐Amylase Modulation: Discovery of Inhibitors Using a Multi‐Pharmacophore Approach for Virtual Screening

Abstract

Better control of postprandial hyperglycemia can be achieved by delaying the absorption of glucose resulting from carbohydrate digestion. Because α-amylase initiates the hydrolysis of polysaccharides, the design of α-amylase inhibitors can lead to the development of new treatments for metabolic disorders such as type II diabetes and obesity. In this study, a rational computer-aided approach was developed to identify novel α-amylase inhibitors. Three-dimensional pharmacophores were developed based on the binding mode analysis of six different families of compounds that bind to this enzyme. In a stepwise virtual screening workflow, seven molecules were selected from a library of 1.4 million. Five out of seven biologically tested compounds showed α-amylase inhibition, and the two most potent compounds inhibited α-amylase with IC values of 17 and 27 μm. The scaffold benzylideneacetohydrazide was shared by four of the discovered inhibitors, emerging as a novel drug-like non-carbohydrate fragment and constituting a promising lead scaffold for α-amylase inhibition.

Virtual Screening Against Carbohydrate-Binding Proteins: Evaluation and Application to Bacterial Burkholderia ambifaria Lectin

“…However, many published virtual screening studies against carbohydrate-binding proteins have not evaluated the applied method nor used negative controls/decoys to establish retrospective enrichment. − Validation is critical for the improvement of screening methods against these targets and allows users to judge the transferability of reported successes. Where retrospective screening has been performed, enrichment indicators used include the raw number of hits returned, the ratio of retrieved actives to total actives, , 1% Enrichment Factor, and ROC AUC …”

“…Where retrospective screening has been performed, enrichment indicators used include the raw number of hits returned, 84 the ratio of retrieved actives to total actives, 85,86 1% Enrichment Factor, 87 and ROC AUC. 88 In this work, a virtual screening protocol for bacterial lectins was iteratively developed and evaluated using the ROC Enrichment Factor and ROC AUC metrics, with calculation of standard errors. The influence of decoy types on retrospective virtual screening enrichment was also examined.…”

Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

“…Pharmacophore modeling is a computationally efficient and pragmatic strategy for the discovery and optimization of biologically active compounds [ 2 , 3 , 4 , 5 ], as well as the analysis of intermolecular interactions in silico [ 6 , 7 ]. Because of their simplicity, these intuitively understandable models can support binding event prediction for a selected group of molecules or to conduct high-throughput virtual screening of large compound libraries [ 8 ].…”