Glutamine: fructose-6-phosphate amidotransferase (GFAT): homology modelling and designing of new inhibitors using pharmacophore and docking based hierarchical virtual screening protocol

• Published in: SAR and QSAR in environmental research Vol. 24; no. 9; pp. 733 - 752
• Main Authors: Vyas, B., Silakari, O., Singh Bahia, M., Singh, B.
• Format: Journal Article
• Language: English
• Published: England Routledge 01.09.2013
• Subjects: Animals; Catalytic Domain; Diabetes Mellitus, Type 2 - drug therapy; diabetic complications; Enzyme Inhibitors - chemistry; Enzyme Inhibitors - isolation & purification; GFAT; Glutamine-Fructose-6-Phosphate Transaminase (Isomerizing) - antagonists & inhibitors; Glutamine-Fructose-6-Phosphate Transaminase (Isomerizing) - chemistry; hierarchical virtual screening; homology modelling; Humans; Hypoglycemic Agents - chemistry; Hypoglycemic Agents - isolation & purification; induced-fit docking; Molecular Docking Simulation; pharmacophore; Protein Conformation
• ISSN: 1062-936X; 1029-046X
• DOI: 10.1080/1062936X.2013.797493

Glutamine: fructose-6-phosphate amidotransferase (GFAT), also termed GFPT1 and GFAT1, catalyzes the first committed step of the hexosamine biosynthesis pathway in mammals and consequently plays an important role in type 2 diabetes. In the present study, a combination of pharmacophore modelling, homology modelling, and molecular docking analysis was performed to design new glutamine competitive inhibitors of human GFAT, and to investigate important interaction details of inhibitor molecules. A pharmacophore model of GFAT inhibitors was developed, subsequently validated, and utilized for the screening by the PHASE database to identify new molecules. Afterwards, homology modelling was performed to construct the glutamine-binding site of the GFAT protein. The modelled active site was utilized to dock the studied molecules to investigate important receptor-ligand interactions and to scrutinize database-screened molecules on the basis of essential interactions. This systematic in silico protocol helped us to identify new molecules that would be explored for the treatment of type 2 diabetes and its complications.