Scoring ligand similarity in structure‐based virtual screening

Scoring to identify high‐affinity compounds remains a challenge in virtual screening. On one hand, protein–ligand scoring focuses on weighting favorable and unfavorable interactions between the two molecules. Ligand‐based scoring, on the other hand, focuses on how well the shape and chemistry of each ligand candidate overlay on a three‐dimensional reference ligand. Our hypothesis is that a hybrid approach, using ligand‐based scoring to rank dockings selected by protein–ligand scoring, can ensure that high‐ranking molecules mimic the shape and chemistry of a known ligand while also complementing the binding site. Results from applying this approach to screen nearly 70 000 National Cancer Institute (NCI) compounds for thrombin inhibitors tend to support the hypothesis. EON ligand‐based ranking of docked molecules yielded the majority (4/5) of newly discovered, low to mid‐micromolar inhibitors from a panel of 27 assayed compounds, whereas ranking docked compounds by protein–ligand scoring alone resulted in one new inhibitor. Since the results depend on the choice of scoring function, an analysis of properties was performed on the top‐scoring docked compounds according to five different protein–ligand scoring functions, plus EON scoring using three different reference compounds. The results indicate that the choice of scoring function, even among scoring functions measuring the same types of interactions, can have an unexpectedly large effect on which compounds are chosen from screening. Furthermore, there was almost no overlap between the top‐scoring compounds from protein–ligand versus ligand‐based scoring, indicating the two approaches provide complementary information. Matchprint analysis, a new addition to the SLIDE (Screening Ligands by Induced‐fit Docking, Efficiently) screening toolset, facilitated comparison of docked molecules' interactions with those of known inhibitors. The majority of interactions conserved among top‐scoring compounds for a given scoring function, and from the different scoring functions, proved to be conserved interactions in known inhibitors. This was particularly true in the S1 pocket, which was occupied by all the docked compounds. Copyright © 2009 John Wiley & Sons, Ltd.     

Virtual screening for inhibitors of human aldose reductase

The inhibition of aldose reductase (AR) provides an interesting strategy to prevent the complications of chronic diabetes. Although a large number of different AR inhibitors are known, very few of these compounds exhibit sufficient efficacy in clinical trials. We performed a virtual screening based on the ultrahigh resolution crystal structure of the inhibitor IDD594 in complex with human AR. AR operates on a large scale of structurally different substrates. To achieve this pronounced promiscuity, the enzyme can adapt rather flexibly to its substrates. Likewise, it has a similar adaptability for the binding of inhibitors. We applied a protocol of consecutive hierarchical filters to search the Available Chemicals Directory. In the first selection step, putative ligands were chosen that exhibit functional groups to anchor the anion-binding pocket of AR. Subsequently, a pharmacophore model based on the binding geometry of IDD594 and the mapping of the binding pocket in terms of putative "hot spots" of binding was applied as a second consecutive filter. In a third and final filtering step, the remaining candidate molecules were flexibly docked into the binding pocket of IDD594 with FlexX and ranked according to their estimated DrugScore values. Out of 206 compounds selected by this search and complemented by a cluster analysis and visual inspection, 9 compounds were selected and subjected to biological testing. Of these, 6 compounds showed IC50 values in the micromolar range. According to the proposed binding mode, the two inhibitors BTB02809 (IC50 = 2.4 +/- 0.5 microM) and JFD00882 (IC50 = 4.1 +/- 1.0 microM) both place a nitro group into the hydrophobic specificity pocket of human AR in an orientation coinciding with the position of the bromine atom of IDD594. The interaction of this Br with Thr113 has been identified as a key feature that is responsible for selectivity enhancement.     

Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials

A new approach, MOBILE, is presented that models protein binding-sites including bound ligand molecules as restraints. Initially generated, homology models of the target protein are refined iteratively by including information about bioactive ligands as spatial restraints and optimising the mutual interactions between the ligands and the binding-sites. Thus optimised models can be used for structure-based drug design and virtual screening. In a first step, ligands are docked into an averaged ensemble of crude homology models of the target protein. In the next step, improved homology models are generated, considering explicitly the previously placed ligands by defining restraints between protein and ligand atoms. These restraints are expressed in terms of knowledge-based distance-dependent pair potentials, which were compiled from crystallographically determined protein-ligand complexes. Subsequently, the most favourable models are selected by ranking the interactions between the ligands and the generated pockets using these potentials. Final models are obtained by selecting the best-ranked side-chain conformers from various models, followed by an energy optimisation of the entire complex using a common force-field. Application of the knowledge-based pair potentials proved efficient to restrain the homology modelling process and to score and optimise the modelled protein-ligand complexes. For a test set of 46 protein-ligand complexes, taken from the Protein Data Bank (PDB), the success rate of producing near-native binding-site geometries (rmsd<2.0A) with MODELLER is 70% when the ligand restrains the homology modelling process in its native orientation. Scoring these complexes with the knowledge-based potentials, in 66% of the cases a pose with rmsd <2.0A is found on rank 1. Finally, MOBILE has been applied to two case studies modelling factor Xa based on trypsin and aldose reductase based on aldehyde reductase.