A method for including protein flexibility in protein-ligand docking: improving tools for database mining and virtual screening

Abstract

Second-generation methods for docking ligands into their biological receptors, such as FLOG, provide for flexibility of the ligand but not of the receptor. Molecular dynamics based methods, such as free energy perturbation, account for flexibility, solvent effects, etc., but are very time consuming. We combined the use of statistical analysis of conformational samples from short-run protein molecular dynamics with grid-based docking protocols and demonstrated improved performance in two test cases. Our statistical analysis explores the importance of the average strength of a potential interaction with the biological target and optionally applies a weighting depending on the variability in the strength of the interaction seen during dynamics simulation. Using these methods, we improved the number of known dihydrofolate reductase ligands found in the top-ranked 10% of a database of drug-like molecules, in searches based on the three-dimensional structure of the protein. These methods are able to match the ability of manual docking to assess likely inactivity on steric grounds and indeed to rank order ligands from a homologous series of cyclooxygenase-2 inhibitors with good correlation to their true activity. Furthermore, these methods reduce the need for human intervention in setting up molecular docking experiments.