Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions
to ensure virtual screening
enrichment is a persistent problem for the molecular docking algorithms
used in structure-based drug discovery. To remedy this problem, elaborate
rescoring and postprocessing schemes have been developed with a varying
degree of success, specificity, and cost. The negative image-based
rescoring (R-NiB) has been shown to improve the flexible docking performance
markedly with a variety of drug targets. The yield improvement is
achieved by comparing the alternative docking poses against the negative
image of the target protein’s ligand-binding cavity. In other
words, the shape and electrostatics of the binding pocket is directly
used in the similarity comparison to rank the explicit docking poses.
Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six
popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK,
and AUTODOCK VINA, using five validated benchmark sets. Overall, the
results indicate that R-NiB outperforms the default docking scoring
consistently and inexpensively, demonstrating that the methodology
is ready for wide-scale virtual screening usage.