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  • Importance of glucokinase in glucose homeostasis and

    2021-09-09

    Importance of glucokinase in glucose homeostasis and a possibility to change its activity using the activators define the scientific interest in the development of new allosteric activators of GK (Liu et al., 2012, Pfefferkorn et al., 2011, Waring et al., 2013, Behera et al., 2015). Activation of glucokinase can be induced by reducing S0.5 (the glucose concentration at GK’s half-maximal phosphorylation rate), and/or by raising the maximal velocity, Vmax. GKAs decrease the level of glucose required for GK activation, and different compounds demonstrate distinct activation profiles. The bioactivity of activators (potency) is characterized as the concentration of the ligand that activates GK enzyme by 50% i.e. EC50 (Taha et al., 2014). GKAs described in the literature reduce the S0.5 to varying extents; and bioactivities of activators change in wide range as well (Bebernitz et al., 2009, Mitsuya et al., 2009, Takahashi et al., 2009). Although allosteric activators of human glucokinase receive considerable attention as potential diabetes therapeutic agents, their mechanism of action is not fully understood. Interaction of glucokinase with GKA is a first step of the complicated mechanism of the regulation of the GK functioning by allosteric activators. In present study, we use molecular docking to investigate the interactions involved in the binding of GKAs to glucokinase and to analyze the relationship between the bioactivities of activators and the computed binding energies. Docking studies are frequently used to predict Mevastatin sale and conformations of ligands into target macromolecules. Protein-ligand interaction is a balance of several competing components: electrostatic forces, hydrophobic and solvation effects, entropy; and success of scoring functions for prediction of the correct ligand poses is known to depend on the protein system (Warren et al., 2006). Here, AutoDock and Vina programs were used to reproduce the structure of the GK-ligands complexes and to estimate the free energy of binding. AutoDock program is one of the most popular molecular docking programs and is successfully applied to the investigation of many protein-ligand complexes. Vina is a more recent release of docking programs and is also widely used for docking and virtual screening.
    Materials and methods The 3D coordinates of three conformations of GK (closed, open and semi-open) retrieved from the Protein Data Bank (Berman et al., 2000) were used for simulations. Structures of activators A1-A20 were extracted from the X-ray structures of the complexes GK with activators (PDB codes are shown in Table 1). Structures of activators A21–A36 were constructed using the activators with known structure as templates. Structures of all GKAs were optimized in vacuum using semi-empirical PM3 method (Stewart, 1989). Molecular docking simulations were performed with AutoDock 4.3 (AutoDock) (Morris et al., 1998) and AutoDock Vina 1.1.2 (Vina) programs (Trott and Olson, 2010). Partial atomic charges were assigned using the Gasteiger–Marsili method (Gasteiger and Marsili, 1980) implemented in AutoDockTools (Morris et al., 2009). Semi-flexible docking method was used, where protein was treated as a rigid body; for ligands, rotation around the single bonds and translation and rotation of the ligands in the grid were allowed. The AutoDock estimates free energies of binding based on empirical weighting factors:where ΔGvdw, ΔGhbond, ΔGelec are the typical molecular mechanics energy terms for dispersion/repulsion, hydrogen bonding, and electrostatics interactions (Huey et al., 2007). ΔGsol displays the desolvation upon the ligand binding and corresponding hydrophobic effect, ΔGtor characterizes the loss of torsional entropy upon binding. The AutoDock approach utilizes a Monte Carlo simulated annealing technique with a rapid energy evaluation using the grid-based molecular affinity potentials. The docking of activators was performed in four steps. The flowchart shown in Supplementary summarizes the steps of the simulation. At the beginning, a blind docking was performed for three conformations of GK: closed (pdb code 1v4s (Kamata et al., 2004)), open (apo) (pdb code 1v4t (Kamata et al., 2004)) and semi-open conformations (pdb code 4dch (Liu et al., 2012)) using the Lamarckian Genetic Algorithm (LGA) (Morris et al., 1998). A docking grid map with 126×126×126 points and grid point spacing of 0.5Å was centered on the glucokinase with AutoGrid program (Sanner, 1999). Specific grids were constructed for each type of ligand atoms. Mevastatin sale Initially, each docking computation consisted of 250 independent runs with an initial population of 150 individuals, a maximum number of 2,500,000 energy evaluations and a maximum number of 27,000 generations. Each run gives one best pose of the ligand on the GK surface. At the end of the docking experiment with multiple runs, a cluster analysis was performed. Resulting conformations that have root mean square deviation (RMSD) less than 2Å were clustered. AutoDock program ranges conformations according to ligand-protein free energy of binding (ΔG).