Ligand- and structure-based virtual screening from 16-((diisobutylamino)methyl)-6α-hydroxyivouacapane-7β,17β-lactone a compound with potential anti-prostate cancer activity

Silva, Rai Campos; Poiani, João Gabriel Curtolo; Ramos, Ryan da Silva; Costa, Josivan da Silva; Silva, Carlos H. T. Paula; Brasil, Davi do Socorro Barros; Santos, Cleydson Breno Rodrigues dos

doi:10.2298/jsc180129047s

Cited by 15 publications

(31 citation statements)

References 44 publications

(48 reference statements)

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“…The rofecoxib molecule was used as a comparison model with each of the molecules in the databases looking for chemical similarity [ 16 , 31 ], according to the structural characteristics and molecular volume fractions of the pivot molecule, observing the maximum overlap in relation to the shape (chemical structure), using as a parameter the Gaussian functions [ 32 ] implemented in the ROCS software. The compounds were selected and classified by means of an algorithm that generated relative scores for the overlapping of forms in the databases according to the pharmacophoric characteristics of rofecoxib [ 15 , 33 ]. This stage of virtual screening identified the most similar two thousand (2000) molecules in each database (Top_2000), resulting in twelve thousand (12,000) tracked structures, which exhibited highest scores of chemical similarities.…”

Section: Resultsmentioning

confidence: 99%

“…This electrostatic potential is calculated using OpenEye’s Poisson-Boltzmann (PB) electrostatic calculation [ 33 , 36 ]. The Top 100 molecules by database (Top_100), led to six hundred structures (600) hits with best alignment based on the electrostatic potential [ 15 ].…”

Section: Resultsmentioning

confidence: 99%

“…To validate the molecular docking protocol, the crystallographic ligand was re-docked in the h COX-2 with the Protein Data Bank (PDB) ID 5KIR structure with resolution 2.69 Å [ 14 ]. The root mean square deviation (RMSD) obtained by re-docking, and the bonding pose found in the complex was 0.98 Å [ 15 ]. The comparison between the crystallographic ligand and the pose predicted by docking overlap of the ligand can be visualized in Figure 3 .…”

Section: Resultsmentioning

confidence: 99%

“…For each molecule in the database, we obtained 300 conformers using the MMFF94 Molecular Force Fields were generated [ 94 ], running on OMEGA v3.3.1.2 software (Open Eye Scientific Software, Santa Fe, NM, ) for Windows 7 operating system and Intel Core i7 machine of 2.4 GHz. Initially, for each molecule in the database, the fast conformer generation method was used with a maximum energy tolerance of 9 kcal.mol −1 and mean square deviation (RMSD) of 0.6 Å [ 15 , 16 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

“…The virtual screening strategy was chosen once it has been widely applied in the early phase of drug discovery, being able to accelerate hit discovery and reducing drug development costs. Thus, the similarity and electrostatic potential of the selected structures were performed using computer programs and commercial databases of compounds [ 15 ] and then we performed the filtering of the results considering the pharmacokinetic and toxicological properties [ 16 , 17 , 18 , 19 , 20 ]. Furthermore, the docking simulation evaluated the binding affinity of compounds to COX-2 in comparison with rofecoxib [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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The cyclooxygenase-2 receptor is a therapeutic target for planning potential drugs with anti-inflammatory activity. The selective cyclooxygenase-2 (COX-2) inhibitor rofecoxib was selected as a pivot molecule to perform virtual ligand-based screening from six commercial databases. We performed the search for similarly shaped Rapid Overlay of Chemical Structures (ROCS) and electrostatic (EON) compounds. After, we used pharmacokinetic and toxicological parameters to determine the best potential compounds, obtained through the softwares QikProp and Derek, respectively. Then, the compounds proceeded to the molecular anchorage study, which showed promising results of binding affinity with the hCOX-2 receptor: LMQC72 (∆G = −11.0 kcal/mol), LMQC36 (∆G = −10.6 kcal/mol), and LMQC50 (∆G = −10.2 kcal/mol). LMQC72 and LMQC36 showed higher binding affinity compared to rofecoxib (∆G = −10.4 kcal/mol). Finally, molecular dynamics (MD) simulations were used to evaluate the interaction of the compounds with the target hCOX-2 during 150 ns. In all MD simulation trajectories, the ligands remained interacting with the protein until the end of the simulation. The compounds were also complexing with hCOX-2 favorably. The compounds obtained the following affinity energy values: rofecoxib: ΔGbind = −45.31 kcal/mol; LMQC72: ΔGbind = −38.58 kcal/mol; LMQC36: ΔGbind = −36.10 kcal/mol; and LMQC50: ΔGbind = −39.40 kcal/mol. The selected LMQC72, LMQC50, and LMQC36 structures showed satisfactory pharmacokinetic results related to absorption and distribution. The toxicological predictions of these compounds did not display alerts for possible toxic groups and lower risk of cardiotoxicity compared to rofecoxib. Therefore, future in vitro and in vivo studies are needed to confirm the anti-inflammatory potential of the compounds selected here with bioinformatics approaches based on rofecoxib ligand.

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