Biased Docking for Protein-Ligand Pose Prediction

Abstract

The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.

Keywords: AutoDock; AutoDock Bias; Biased docking; Cosolvent; Docking; Guided docking; Knowledge-based docking; Mixed-solvents.

Figure 1.

Energy map modification to perform biased docking with AutoDock Bias.

Figure 2.

Ligand PDBQT structure file.

Figure 3.

Protein structure (yellow lines), co-crystallized ligand (orange sticks) and squared grid centered on the co-crystallized ligand (white box).

Figure 4.

Hinge region of CDK2 from PDB ID 1y91 (cylinders). The reference ligand from PDB ID 3sw4 is superimposed (balls and sticks). Arrows indicate protein interactors obtained from ligand-derived pharmacophore.

Figure 5.

(Left) Ideal interaction sites for the specified residue (Leu83) shown as cylinders. The receptor is depicted as gray ribbons. The calculated interaction sites are shown as spheres: blue = hydrogen bond acceptor, gray = hydrogen bond donor. (Right) Cocrystallized ligand from PDB ID 1y91 superposed to bias sites shown as spheres (blue = hydrogen bond acceptor, gray = hydrogen bond donor).

Figure 6.

Grid Control Panel of ADT, showing receptor.NA.biased.map, at an isoenergetic value of −1.5 kcal/mol. The wired representation is for the bias sites.

Figure 7.

Binding energy distribution for 100 docking runs.

Figure 8.

Best ranked predicted poses (in cylinders) compared to reference co-crystal structure (in CPK) for docking with ligand-derived pharmacophore bias (left) and conventional docking with AutoDock4 (right).

Figure 9.

Cluster population vs. docking score for the ligand-derived biased docking (left) and AutoDock4 conventional docking (right) methods. RMSD (in Å) of the first ranked pose against the reference structure is indicated in blue inside the plot.

Figure 10.

Hydrophobic solvent sites derived from ethanol/water MD depicted as cyan spheres overlapping the reference co-crystallized ligand.

Figure 11.

Grid Control Panel of ADT, showing receptor.AC.biased.map, at an isoenergetic value of −1.0 kcal/mol depicted in red. The gray sphere representation is for the aromatic bias sites.

Figure 12.

(Left panel) Binding energy distribution for poses obtained from 100 docking runs. (Right panel) Best ranked predicted pose (in cylinders) compared to reference cocrystal structure (in balls and sticks) for the biased docking method including solvent sites.

Figure 13.

Cluster population vs. docking score for the biased docking. RMSD (in Å) of the first ranked pose against the reference is indicated in blue.