The key to success for computational tools used in structure-based drug design is the ability to accurately place or "dock" a ligand in the binding pocket of the target of interest. In this report we examine the effect of several factors on docking accuracy, including ligand and protein flexibility. To examine ligand flexibility in an unbiased fashion, a test set of 41 ligand-protein cocomplex X-ray structures were assembled that represent a diversity of size, flexibility, and polarity with respect to the ligands. Four docking algorithms, DOCK, FlexX, GOLD, and CDOCKER, were applied to the test set, and the results were examined in terms of the ability to reproduce X-ray ligand positions within 2.0Å heavy atom root-mean-square deviation. Overall, each method performed well (>50% accuracy) but for all methods it was found that docking accuracy decreased substantially for ligands with eight or more rotatable bonds. Only CDOCKER was able to accurately dock most of those ligands with eight or more rotatable bonds (71% accuracy rate). A second test set of structures was gathered to examine how protein flexibility influences docking accuracy. CDOCKER was applied to X-ray structures of trypsin, thrombin, and HIV-1-protease, using protein structures bound to several ligands and also the unbound (apo) form. Docking experiments of each ligand to one "average" structure and to the apo form were carried out, and the results were compared to docking each ligand back to its originating structure. The results show that docking accuracy falls off dramatically if one uses an average or apo structure. In fact, it is shown that the drop in docking accuracy mirrors the degree to which the protein moves upon ligand binding.

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BACE1 is a key protease controlling the formation of amyloid ␤, a peptide hypothesized to play a significant role in the pathogenesis of Alzheimer's disease (AD). Therefore, the development of potent and selective inhibitors of BACE1 has been a focus of many drug discovery efforts in academiaandindustry.Herein,wereportthenonclinicalandearlyclinicaldevelopmentofLY2886721,aBACE1activesiteinhibitorthatreached phase 2 clinical trials in AD. LY2886721 has high selectivity against key off-target proteases, which efficiently translates in vitro activity into robust in vivo amyloid ␤ lowering in nonclinical animal models. Similar potent and persistent amyloid ␤ lowering was observed in plasma and lumbar CSF when single and multiple doses of LY2886721 were administered to healthy human subjects. Collectively, these data add support for BACE1 inhibition as an effective means of amyloid lowering and as an attractive target for potential disease modification therapy in AD.

This paper is available online at http://dmd.aspetjournals.org ABSTRACT:The pregnane X receptor (PXR) is involved in transcriptional regulation of multiple cytochromes P450 and multidrug resistanceassociated protein (MDR1), which encodes for the drug transporter P-glycoprotein. Crystal structure analyses suggest that the ligand binding domain is highly hydrophobic and flexible, allowing molecules of differing sizes to bind in multiple orientations. Using literature data for EC 50 (half-maximal inhibitory concentration) values for PXR activation derived for 12 human PXR ligands, a pharmacophore was developed. This pharmacophore supports the hydrophobic nature of the ligand binding domain recently deduced from the X-ray crystal structure because it contains four hydrophobic regions and one hydrogen bond acceptor. These features are consistent with at least one of the three experimentally determined orientations in which SR12813 binds to PXR, as determined by overlay studies. SR12813 fulfills all of the five pharmacophore features, as does the potent ligand hyperforin. The pharmacophore was also used to predict the binding affinity for 28 molecules not in the model but known to be PXR ligands of differing potencies. The pharmacophore distinguished the most potent activators of PXR (that display >5-fold activation/deactivation), like ecteinascidin, troglitazone, nifedipine, and dexamethasone-t-butylacetate, from poor activators, such as scopoletin and kaempferol. The model could be useful in drug development, potentially acting as a highthroughput filter for identifying compounds that may bind to PXR before in vitro determination. Ultimately, this will aid in the selection of molecules with a lesser capacity to be potent PXR ligands and thus avoid induction of numerous drug-metabolizing enzymes and MDR1.

Docking methods are used to predict the manner in which a ligand binds to a protein receptor. Many studies have assessed the success rate of programs in self-docking tests, whereby a ligand is docked into the protein structure from which it was extracted. Cross-docking, or using a protein structure from a complex containing a different ligand, provides a more realistic assessment of a docking program's ability to reproduce X-ray results. In this work, cross-docking was performed with CDocker, Fred, and Rocs using multiple X-ray structures for eight proteins (two kinases, one nuclear hormone receptor, one serine protease, two metalloproteases, and two phosphodiesterases). While average cross-docking accuracy is not encouraging, it is shown that using the protein structure from the complex that contains the bound ligand most similar to the docked ligand increases docking accuracy for all methods ("similarity selection"). Identifying the most successful protein conformer ("best selection") and similarity selection substantially reduce the difference between self-docking and average cross-docking accuracy. We identify universal predictors of docking accuracy (i.e., showing consistent behavior across most protein-method combinations), and show that models for predicting docking accuracy built using these parameters can be used to select the most appropriate docking method.