Sara Shamsian
1,2 
, Babak Sokouti
3, Siavoush Dastmalchi
2,3,4* 1 Student Research Committee, Tabriz University of Medical Sciences, Tabriz, 5165665931, Iran
2 Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, 5166414766, Iran
3 Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, 5165665813, Iran
4 Faculty of Pharmacy, Near East University, POBOX:99138, Nicosia, North Cyprus, Mersin 10, Turkey
Abstract
Introduction: Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem.
Methods: In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis.
Results: The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 – 40 folds.
Conclusion: The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.