Atomic insight into designed carbamate-based derivatives as acetylcholine esterase (AChE) inhibitors: a computational study by multiple molecular docking and molecular dynamics simulation

<p>Over 100 variants have been designed and studied, using multiple docking methods such as Autodock Vina, ArgusLab, Molegro Virtual Docker, and Hex-Cuda, to study the effect of alteration in the structure of carbamate-based acetylcholyne esterase (AChE) inhibitors. Sixteen selected systems were then subjected to 14 ns molecular dynamics (MD) simulations. Results from all the docking methods are in agreement. Variants that involved biphenyl substituents possess the most negative binding energies in the −37.64 to −39.31 kJ mol⁻¹ range due to their π–π interactions with AChE aromatic residues. The root mean square deviation values showed that all of these components achieved equilibration after 6 ns. Gyration radius (<i>R</i>_g) and solvent accessibility surface area were calculated to further investigate the AChE conformational changes in the presence of these components. MD simulation results suggested that these components might interact with AChE, possibly with no major changes in AChE secondary and tertiary structures.</p

A computational study to identify the key residues of peroxisome proliferator-activated receptor gamma in the interactions with its antagonists

<p>Peroxisome proliferator-activated receptors (PPARs) compose a family of nuclear receptors, PPARα, PPARβ, and PPARγ, which mediate the effects of lipidic ligands at the transcriptional level. Among these, the PPARγ has been known to regulate adipocyte differentiation, fatty acid storage and glucose metabolism, and is a target of antidiabetic drugs. In this work, the interactions between PPARγ and its six known antagonists were investigated using computational methods such as molecular docking, molecular dynamics (MD) simulations, and the hybrid quantum mechanics/molecular mechanics (QM/MM). The binding energies evaluated by molecular docking varied between −22.59 and −35.15 kJ mol^− 1. In addition, MD simulations were performed to investigate the binding modes and PPARγ conformational changes upon binding of antagonists. Analysis of the root-mean-square fluctuations (<i>RMSF</i>) of backbone atoms shows that H3 of PPARγ has a higher mobility in the absence of antagonists and moderate conformational changes were observed. The interaction energies between antagonists and each PPARγ residue involved in the interactions were studied by QM/MM calculations. These calculations reveal that antagonists with different structures show different interaction energies with the same residue of PPARγ. Therefore, it can be concluded that the key residues vary depending on the structure of the ligand, which binds to PPARγ.</p