Comparison of Molecular Recognition in Docking Versus Experimental CSD and PDB Data

Molecular
docking is a widely used technique in structure-based
drug design for generating poses of small molecules in a protein receptor
structure. These poses are then ranked to prioritize compounds for
experimental validation. Numerous approaches to assessing the structural
fit of a ligand exist, ranging from simple scoring functions to more
elaborate free energy calculations. Regardless of the prioritization
method chosen, its accuracy is limited by the quality of the protein–ligand
pose. Here, we apply two established statistical approaches for quantifying
atomic interaction preferences and torsional ligand strain, respectively,
to compare poses generated by the docking algorithm Vina with crystallographic
data from the PDB and CSD. This analysis allows us to identify potential
deficiencies in the docking algorithm, such as underestimated electrostatic
repulsion or high-energy hydroxyl conformations. By highlighting such
inaccuracies, we aim to inspire improvements in future docking algorithms.
Finally, a pose scoring approach is proposed that significantly improves
the retrieval of the experimental pose from a set of docked poses.