ABSTRACT
We present the results of a comprehensive study in which we explored how the docking procedure affects the performance of a virtual screening approach. We used four docking engines and applied 10 scoring functions to the top-ranked docking solutions of seeded databases against six target proteins. The scores of the experimental poses were placed within the total set to assess whether the scoring function required an accurate pose to provide the appropriate rank for the seeded compounds. This method allows a direct comparison of library ranking efficacy. Our results indicate that the LigandFit/Ligscore1 and LigandFit/GOLD docking/scoring combinations, and to a lesser degree FlexX/FlexX, Glide/Ligscore1, DOCK/PMF (Tripos implementation), LigandFit1/Ligscore2 and LigandFit/PMF (Tripos implementation) were able to retrieve the highest number of actives at a 10% fraction of the database when all targets were looked upon collectively. We also show that the scoring functions rank the observed binding modes higher than the inaccurate poses provided that the experimental poses are available. This finding stresses the discriminatory ability of the scoring algorithms, when better poses are available, and suggests that the number of false positives can be lowered with conformers closer to bioactive ones.
Subject(s)
Computer Simulation , Databases, Factual , Drug Design , Enzyme Inhibitors/chemistry , Proteins/chemistry , Algorithms , Carbonic Anhydrases/chemistry , Carbonic Anhydrases/drug effects , Enzyme Inhibitors/pharmacology , HIV Protease/chemistry , HIV Protease/drug effects , Ligands , Matrix Metalloproteinase 3/chemistry , Matrix Metalloproteinase Inhibitors , Models, Molecular , Molecular Conformation , Protein Binding , Proteins/antagonists & inhibitors , Tetrahydrofolate Dehydrogenase/chemistry , Tetrahydrofolate Dehydrogenase/drug effects , Thermolysin/antagonists & inhibitors , Thermolysin/chemistry , Thymidine Kinase/antagonists & inhibitors , Thymidine Kinase/chemistryABSTRACT
Docking molecules into their respective 3D macromolecular targets is a widely used method for lead optimization. However, the best known docking algorithms often fail to position the ligand in an orientation close to the experimental binding mode. It was reported recently that consensus scoring enhances the hit rates in a virtual screening experiment. This methodology focused on the top-ranked pose, with the underlying assumption that the orientation/conformation of the docked compound is the most accurate. In an effort to eliminate the scoring function bias, and assess the ability of the docking algorithms to provide solutions similar to the crystallographic modes, we investigated the most known docking programs and evaluated all of the resultant poses. We present the results of an extensive computational study in which five docking programs (FlexX, DOCK, GOLD, LigandFit, Glide) were investigated against 14 protein families (69 targets). Our findings show that some algorithms perform consistently better than others, and a correspondence between the nature of the active site and the best docking algorithm can be found.
