Identification of novel nt-MGAM inhibitors for potential treatment of type 2 diabetes: Virtual screening, atom based 3D-QSAR model, docking analysis and ADME study. (February 2018)

• Record Type: Journal Article
• Authors: Laoud, Aicha; Ferkous, Fouad; Maccari, Laura; Maccari, Giorgio; Saihi, Youcef; Kraim, Khaireddine
• Is Part Of: Computational biology and chemistry. Volume 72(2018)
• Page Start: 122
• Page End: 135
• Publication Date: 2018-02
• Subjects: Salacinol -- nt-MGAM -- Atom-based 3D QSAR -- Virtual screening -- ADME study -- Type 2 diabetes; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.12.003
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Virtual screening for novel nt- MGAM inhibitors. Highlights: The atom-based 3D-QSAR of salacinol derivatives allowed the investigation of the pharmacophoric features responsible for nt-MGAM inhibition. Important residues in the active pocket of nt-MGAM were obtained by docking. Virtual screening procedure was applied to large commercial compound database yielding to a set of 50 hits. Hits obtained have a good docking score and a good predictive. Abstract: In this study, a virtual screening procedure was applied to identify new potential nt-MGAM inhibitors as a possible medication for type 2 diabetes. To this aim, a series of salacinol analogues were first investigated by docking analysis for their binding to the X-ray structure of the biological target nt-MGAM. Key interactions for ligand binding into the receptor active site were identified which shared common features to those found for other known inhibitors, which strengthen the results of this study. 3D QSAR model was then built and showed to be statistically significant and with a good predictive power for the training (R 2 = 0.99, SD = 0.17, F = 555.3 and N = 27) and test set (Q 2 = 0.81, Pearson(r) = 0.92, RMSE = 0.52, N = 08). The model was then used to virtually screen the ZINC database with the aim of identifying novel chemical scaffolds as potential nt-MGAM inhibitors. Further, in silico predicted ADME properties were investigated for the most promising molecules. The outcome of this investigation sheds light on the molecular characteristics of the binding of salacinol analogues to nt-MGAM enzyme and identifies new possible inhibitors which have the potential to be developed into drugs, thus significantly contributing to the design and optimization of therapeutic strategies against type 2 diabetes.