Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations

Abstract

11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) is a potential target for the treatment of numerous human disorders, such as diabetes, obesity, and metabolic syndrome. In this work, molecular modeling studies combining molecular docking, 3D-QSAR, MESP, MD simulations and free energy calculations were performed on pyridine amides and 1,2,4-triazolopyridines as 11β-HSD1 inhibitors to explore structure-activity relationships and structural requirement for the inhibitory activity. 3D-QSAR models, including CoMFA and CoMSIA, were developed from the conformations obtained by docking strategy. The derived pharmacophoric features were further supported by MESP and Mulliken charge analyses using density functional theory. In addition, MD simulations and free energy calculations were employed to determine the detailed binding process and to compare the binding modes of inhibitors with different bioactivities. The binding free energies calculated by MM/PBSA showed a good correlation with the experimental biological activities. Free energy analyses and per-residue energy decomposition indicated the van der Waals interaction would be the major driving force for the interactions between an inhibitor and 11β-HSD1. These unified results may provide that hydrogen bond interactions with Ser170 and Tyr183 are favorable for enhancing activity. Thr124, Ser170, Tyr177, Tyr183, Val227, and Val231 are the key amino acid residues in the binding pocket. The obtained results are expected to be valuable for the rational design of novel potent 11β-HSD1 inhibitors.

Antihyperglycemic Potential of Spondias mangifera Fruits via Inhibition of 11β-HSD Type 1 Enzyme: In Silico and In Vivo Approach

“…Therefore, 11β-HSD1 inhibition may offer a new therapeutic approach for type 2 diabetes mellitus. 11β-HSD1 active site consists of highly conserved residues (SER170, TYR177, VAL180, and TYR183) in addition to LEU217 and TYR284, which stabilize the C-terminal loop of the protein during interaction [ 32 , 33 ].…”

Recent Developments and Applications of the MMPBSA Method

“…In a search for compounds for the treatment of chronic kidney disease, Vitamin D receptor and cytochrome P450 were analyzed (Nagamani et al, 2016 ). Malonyl-CoA decarboxylase (Ling et al, 2016 ), sirtuins (Karaman and Sippl, 2015 ), adipocyte fatty-acid binding protein (Chen et al, 2014b ), 11β-hydroxysteroid dehydrogenase type 1 (Qian H. Y. et al, 2016 ), and protein tyrosine phosphatase 1B (Kocakaya, 2014 ) were reported for treatments of other metabolic diseases. For other diseases and disorders, targets such as pyrroline-5-carboxylate reductase for cutis laxa (Sang et al, 2017 ), renin complexes for hypertension (Tzoupis et al, 2015 ), and the type 1 receptor of TGF beta for wound healing (Gesteira et al, 2017 ) were examined.…”

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

“…In recent years, MM-PBSA has become a popular method to estimate the ligand binding affinities and it has several applications 31 . Few examples include, development of potential anticancer compounds 31 , 32 , understanding resistance mechanism of drugs 33 , neural disorder 34 , blood disorder 35 , immune disorder 36 , inflammatory disorder 37 , metabolic disorder 38 , and many other major diseases 39 , 40 . Apart from these PL interactions, MM-PBSA calculations also play a major role in other biomolecular studies such as protein folding, protein-protein interaction 41 , and others 42 .…”