Computational protein-ligand docking and virtual drug screening with the AutoDock suite

Abstract

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.

Figure 1. AutoDockTools

ADT is built within the Python Molecule Viewer. ADT commands for coordinate preparation, docking, and analysis are available through menus on the lower toolbar. PMV commands for higher level visualization are available on the upper tool bar. The Dashboard controls representation, colors, and labeling of molecular objects, which are displayed in the Interactive Viewer panel. For more information on the capabilities of PMV and ADT, see http://mgltools.scripps.edu/documentation.

Figure 2. Results of docking imatinib to its receptor in bound and apo conformations

a. Redocking of flexible imatinib to rigid Abl (PDB entry 1iep) using AutoDock (purple) and AutoDock Vina (green). The X-ray crystallographic ligand position is in silver. b. Cross docking of flexible imatinib to Abl (PDB entry 1fpu) with a single flexible residue side chain using AutoDock Vina (green). Note that Vina does not retain hydrogen atom positions during docking, so the threonine hydroxyl hydrogen is placed in a random position in the docked coordinate set.

Figure 3. Raccoon2

Tabs at the top choose each of the steps for setting up, running and analyzing a virtual screen.

Figure 4. Raccoon Results Filtering

The “Data Source” window (top) has several options for filtering the results of a virtual screen. Two interaction filters are applied here. The “Visualization” window (bottom) allows visualization of the filtered results, and checkboxes can be used to select the desired compounds for export.

Figure 5. AutoLigand Results

c-Abl is analyzed with AutoLigand. a) c-Abl with imatinib; b) carbon affinity map calculated around the protein--notice the many disconnected areas of strong affinity; c) 100 point envelope identifies the regions of the active site that provide the strongest affinity for the drug; d) 400 point envelope includes the region of high affinity and also extends into adjacent solvent-accessible regions on the protein surface.

Figure 6. Docking with Explicit Hydration

a) crystallographic structure of acetylcholine binding protein with nicotine. An ordered water (red sphere) mediates interaction with the protein. b) The default protocol in AutoDock finds two conformation of the pyridine ring with equal predicted energy. c) hydrated docking predicts the observed conformation. The second water molecule marked with an X is included during the docking, but is removed because it clashes with the protein.