To aid in the interpretation of high-throughput screening (HTS) results derived from luciferase-based assays, we used quantitative HTS, an approach that defines the concentration-response behavior of each library sample, to profile the ATP-dependent luciferase from Photinus pyralis against more than 70,000 samples. We found that approximately 3% of the library was active, containing only compounds with inhibitory concentration-responses, of which 681 (0.9%) exhibited IC 50 < 10 microM. Representative compounds were shown to inhibit purified P. pyralis as well as several commercial luciferase-based detection reagents but were found to be largely inactive against Renilla reniformis luciferase. Light attenuation by the samples was also examined and found to be more prominent in the blue-shifted bioluminescence produced by R. reniformis luciferase than in the bioluminescence produced by P. pyralis luciferase. We describe the structure-activity relationship of the luciferase inhibitors and discuss the use of this data in the interpretation of HTS results and configuration of luciferase-based assays.

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With the many protein sequences coming from the genome sequencing projects, it is unlikely that we will ever have an atomic resolution structure of every relevant protein. With high throughput crystallography, however, we will soon have representative structures for the vast majority of protein families. Thus the drug discovery and design process will rely heavily on protein modeling to address issues such as designing combinatorial libraries for an entire class of targets and engineering genome-wide selectivity over a target class. In this study we assess the value of high throughput docking into homology models. To do this we dock a database of random compounds seeded with known inhibitors into homology models of six different kinases. In five of the six cases the known inhibitors were found to be enriched by factors of 4-5 in the top 5% of the overall scored and ranked compounds. Furthermore, in the same five cases the known inhibitors were found to be enriched by factors of 2-3 in the top 5% of the scored and ranked known kinase inhibitors, thus showing that the homology models can pick up some of the crucial selectivity information.

Molecular docking studies of carbohydrate derivatives in protein binding sites are often challenging because of water-mediated interactions and the inherent flexibility of the many terminal hydroxyl groups. Using the recognition process between heat-labile enterotoxin from Escherichia coli and ganglioside GM1 as a paradigm, we developed a modeling protocol that includes incremental conformational flexibility of the ligand and predicted water interactions. The strategy employs a modified version of the Monte Carlo docking program AUTODOCK and water affinity potentials calculated with GRID. After calibration of the protocol on the basis of the known binding modes of galactose and lactose to the toxin, blind predictions were made for the binding modes of four galactose derivatives: lactulose, melibionic acid, thiodigalactoside, and m-nitrophenyl-alpha-galactoside. Subsequent crystal structure determinations have demonstrated that our docking strategy can predict the correct binding modes of carbohydrate derivatives within 1.0 A from experiment. In addition, it is shown that repeating the docking simulations in each of the seemingly identical binding sites of the multivalent toxin increases the chance of finding the correct binding mode.