Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

Abstract

Adenosine Receptor Type 2A (A2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A2AAR. For the search for A2AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC50 = 7.54407; ZINC04257548 pEC50 = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan®), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to A2AAR.

Identification of Potential COX-2 Inhibitors for the Treatment of Inflammatory Diseases Using Molecular Modeling Approaches

“…The characteristics were analyzed with the aid of the Statistica ® software, where the most relevant ones were used to predict the inhibitory activity as a function of the pIC 50 value to decrease statistical inconsistencies. This software is capable of predicting the relationship between the inhibitor structure and its inhibitory activity, with a Pearson correlation cut-off of 0.4, obtaining a training set with n = 20 structures (methodology adopted by Santos, Cruz and Santos) [ 12 , 13 , 14 ]. Table 1 shows the selected descriptors.…”

“…Subsequently, the input was made to the PharmaGist Web Server 15 to determine the following characteristics: atoms (ATM), spatial characteristics (SF), characteristics (F), aromatic (ARO), hydrophobic (HYD), acceptor (ACC), and donor of hydrogen (DONN). The initial set presented 25 molecules, which were aligned according to the similarity with the selected pivot molecule, allowing the generation of pharmacophore models with the aid of the Discovery Studio ® v. 4.0 program, following the methodology developed by us [ 10 , 12 , 14 , 57 , 58 , 59 ].…”

“…The calculation of binding affinity (∆G) was also performed in order to compare the actual data obtained and the values predicted in silico, which was the same methodology adopted by Santos et al, 2020 [ 14 ], according to Equation (5). where R (gas constant) is 1.987.10 −3 kcal·mol −1 ·K −1 , the temperature is 310 K for rofecoxib/celecoxib, and K i is 310.10 −9 M for rofecoxib and 340.10 −9 M for celecoxib [ 28 , 32 , 52 ].…”

“…Per-residue free energy decomposition was decomposed using the approach of MM/GBSA according to the following equation [ 14 , 81 , 82 ]: ΔG MM-GBSA = ΔE vdW + ΔE elec + ΔE pol + ΔE np . …”

Identification of New Rofecoxib-Based Cyclooxygenase-2 Inhibitors: A Bioinformatics Approach

“…In addition, the fluctuation of the residues from the protein backbone was evaluated, for this, the Cα atoms were also used. This analysis was performed to evaluate the difference in the structural fluctuation of the protein during the interaction with the different ligands, throughout the 150 ns MD simulation (see Figure 6 ) [ 25 , 79 , 80 , 81 , 82 , 83 ].…”