Date of this Version
Elucidating details of the relationship between molecular structure and a particular biological end point is essential for successful, rational drug discovery. Molecular docking is a widely accepted tool for lead identification however, navigating the intricacies of the software can be daunting. Our objective was therefore to provide a step-by-step guide for those interested in incorporating contemporary basic molecular docking and homology modelling into their design strategy. Three molecular docking programs, AutoDock4, SwissDock and Surflex-Dock, were compared in the context of a case study where a set of steroidal and non-steroidal ligands were docked into the human androgen receptor (hAR) using both rigid and flexible target atoms. Metrics for comparison included how well each program predicted the X-ray structure orientation via root mean square deviation (rmsd), predicting known actives via ligand ranking and comparison to biological data where available. Benchmarking metrics were discussed in terms of identifying accurate and reliable results. For cases where no three dimensional structure exists, we provided a practical example for creating a homology model using Swiss-Model. Results showed an rmsd between X-ray ligands from wild-type and mutant receptors and docked poses were 4.15Å and 0.83Å (SwissDock), 2.69Å and 8.80Å (AutoDock4) and 0.39Å and 0.71Å (Surflex-Dock) respectively. Surflex-Dock performed consistently well in pose prediction (less than 2Å) while Auto- Dock4 predicted known active non-steroidal antiandrogens most accurately. Introducing flexibility into target atoms produced the largest degree of change in ligand ranking in Surflex-Dock. We produced a viable homology model of the P2X1 purireceptor for subsequent docking analysis.
This document has been peer reviewed.