Georgia B. McGaughey

A representative set of high resolution x-ray crystal structures of nonhomologous proteins have been examined to determine the preferred positions and orientations of noncovalent interactions between the aromatic side chains of the amino acids phenylalanine, tyrosine, histidine, and tryptophan. To study the primary interactions between aromatic amino acids, care has been taken to examine only isolated pairs (dimers) of amino acids because trimers and higher order clusters of aromatic amino acids behave differently than their dimer counterparts. We find that pairs (dimers) of aromatic side chain amino acids preferentially align their respective aromatic rings in an off-centered parallel orientation. Further, we find that this parallel-displaced structure is 0.5-0.75 kcal/mol more stable than a T-shaped structure for phenylalanine interactions and 1 kcal/mol more stable than a T-shaped structure for the full set of aromatic side chain amino acids. This experimentally determined structure and energy difference is consistent with ab initio and molecular mechanics calculations of benzene dimer, however, the results are not in agreement with previously published analyses of aromatic amino acids in proteins. The preferred orientation is referred to as parallel displaced pi-stacking.

Virtual screening benchmarking studies were carried out on 11 targets to evaluate the performance of three commonly used approaches: 2D ligand similarity (Daylight, TOPOSIM), 3D ligand similarity (SQW, ROCS), and protein structure-based docking (FLOG, FRED, Glide). Active and decoy compound sets were assembled from both the MDDR and the Merck compound databases. Averaged over multiple targets, ligand-based methods outperformed docking algorithms. This was true for 3D ligand-based methods only when chemical typing was included. Using mean enrichment factor as a performance metric, Glide appears to be the best docking method among the three with FRED a close second. Results for all virtual screening methods are database dependent and can vary greatly for particular targets.

Despite increased understanding of the biological basis for sleep control in the brain, few novel mechanisms for the treatment of insomnia have been identified in recent years. One notable exception is inhibition of the excitatory neuropeptides orexins A and B by design of orexin receptor antagonists. Herein, we describe how efforts to understand the origin of poor oral pharmacokinetics in a leading HTS-derived diazepane orexin receptor antagonist led to the identification of compound 10 with a 7-methyl substitution on the diazepane core. Though 10 displayed good potency, improved pharmacokinetics, and excellent in vivo efficacy, it formed reactive metabolites in microsomal incubations. A mechanistic hypothesis coupled with an in vitro assay to assess bioactivation led to replacement of the fluoroquinazoline ring of 10 with a chlorobenzoxazole to provide 3 (MK-4305), a potent dual orexin receptor antagonist that is currently being tested in phase III clinical trials for the treatment of primary insomnia.

The eight contributions here provide ample evidence that shape as a volume or as a surface is a vibrant and useful concept when applied to drug discovery. It provides a reliable scaffold for "decoration" with chemical intuition (or bias) for virtual screening and lead optimization but also has its unadorned uses, as in library design, ligand fitting, pose prediction, or active site description. Computing power has facilitated this evolution by allowing shape to be handled precisely without the need to reduce down to point descriptors or approximate metrics, and the diversity of resultant applications argues for this being an important step forward. Certainly, it is encouraging that as computation has enabled our intuition, molecular shape has consistently surprised us in its usefulness and adaptability. The first Aurelius question, "What is the essence of a thing?", seems well answered, however, the third, "What do molecules do?", only partly so. Are the topics covered here exhaustive, or is there more to come? To date, there has been little published on the use of the volumetric definition of shape described here as a QSAR variable, for instance, in the prediction or classification of activity, although other shape definitions have been successful applied, for instance, as embodied in the Compass program described above in "Shape from Surfaces". Crystal packing is a phenomenon much desired to be understood. Although powerful models have been applied to the problem, to what degree is this dominated purely by the shape of a molecule? The shape comparison described here is typically of a global nature, and yet some importance must surely be placed on partial shape matching, just as the substructure matching of chemical graphs has proved useful. The approach of using surfaces, as described here, offers some flavor of this, as does the use of metrics that penalize volume mismatch less than the Tanimoto, e.g., Tversky measures. As yet, there is little to go on as to how useful a paradigm this will be because there is less software and fewer concrete results.Finally, the distance between molecular shapes, or between any shapes defined as volumes or surfaces, is a metric property in the mathematical sense of the word. As yet, there has been little, if any, application of this observation. We cannot know what new application to the design and discovery of pharmaceuticals may yet arise from the simple concept of molecular shape, but it is fair to say that the progress so far is impressive.