Getting Docking into Shape Using Negative Image-Based Rescoring
datasetposted on 24.07.2019, 15:42 by Sami T. Kurkinen, Sakari Lätti, Olli T. Pentikäinen, Pekka A. Postila
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.
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docking softwaresGLIDEalternative dockingdrug targetsR-NiBPLANTSscreening usageimage-based rescoringNegative Image-Based RescoringAUTODOCK VINAbenchmark setspostprocessing schemesstructure-based drug discoveryscreening enrichmentsimilarity comparisonbinding pocketdefault dockingdocking algorithmsdocking performance