Abstract
Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.
Highlights
Fragment-based screening approaches have emerged as effective and complementary alternatives to high throughput screening (HTS), opening new avenues for drug design [1]
We explore the rescoring performance on fragment pose prediction of three rescoring approaches based on the 3D-structure of reference complexes: similarity of interaction fingerprints (IFP) [22], graph matching of interaction patterns (GRIM) [23] and rapid overlay of chemical structures (ROCS) [24] according to shape and pharmacophoric properties
Statistics on the success rate in pose prediction suggest that the IFP method shows inferior performance than GRIM, which in turn is inferior to ROCS
Summary
Fragment-based screening approaches have emerged as effective and complementary alternatives to high throughput screening (HTS), opening new avenues for drug design [1]. Computational approaches have a special place, as they have been pioneers in the mapping of sites by very small molecules [3, 4]. Methods developed to predict binding of a ligand to a target protein constitute a cost-effective way to virtually screen large chemical libraries. In addition they are not limited to the previously synthesized molecules, presenting the advantage of enabling the screening of new chemotypes [5].
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