Trypanosoma cruzi and Trypanosoma brucei are parasites that cause Chagas' disease and African sleeping sickness, respectively. Both parasites rely on essential cysteine proteases for survival: cruzain for T. cruzi and TbCatB/rhodesain for T. brucei. A recent quantitative high-throughput screen of cruzain identified triazine nitriles, which are known inhibitors of other cysteine proteases, as reversible inhibitors of the enzyme. Structural modifications detailed herein, including core scaffold modification from triazine to purine, improved the in vitro potency against both cruzain and rhodesain by 350-fold, while also gaining activity against T. brucei parasites. Selected compounds were screened against a panel of human cysteine and serine proteases to determine selectivity, and a cocrystal was obtained of our most potent analogue bound to cruzain.

The perceived and actual burden of false positives in high-throughput screening has received considerable attention; however, few studies exist on the contributions of distinct mechanisms of nonspecific effects like chemical reactivity, assay signal interference, and colloidal aggregation. Here, we analyze the outcome of a screen of 197861 diverse compounds in a concentration-response format against the cysteine protease cruzain, a target expected to be particularly sensitive to reactive compounds, and using an assay format with light detection in the short-wavelength region where significant compound autofluorescence is typically encountered. Approximately 1.9% of all compounds screened were detergent-sensitive inhibitors. The contribution from autofluorescence and compounds bearing reactive functionalities was dramatically lower: of all hits, only 1.8% were autofluorescent and 1.5% contained reactive or undesired functional groups. The distribution of false positives was relatively constant across library sources. The simple step of including detergent in the assay buffer suppressed the nonspecific effect of approximately 93% of the original hits.

Methylation of lysine residues on the tails of histone proteins is a major determinant of the transcription state of associated DNA coding regions. The interplay among methylation states and other histone modifications to direct transcriptional outcome is referred to as the histone code. In addition to histone methyltransferases and demethylases which function to modify the methylation state of lysine sidechains, other proteins recognize specific histone methylation marks essentially serving as code readers. While these interactions are highly specific with respect to site and methylation state of particular lysine residues, they are generally weak and therefore difficult to monitor by traditional assay techniques. Herein, we present the design and implementation of a homogeneous, miniaturizable, and sensitive assay for histone methylation-dependent interactions. We use AlphaScreen, a chemiluminescence-based technique, to monitor the interactions of chromodomains (MPP8, HP1beta and CHD1), tudor domains (JMJD2A) and plant homeodomains (RAG2) with their cognate trimethyllysine histone partners. The utility of the method was demonstrated by profiling the binding specificities of chromo- and tudor domains toward several histone marks. The simplicity of design and the sensitive and robust nature of this assay should make it applicable to a range of epigenetic studies, including the search for novel inhibitors of methylation-dependent interactions.

SAR exploration of the 2,4-diamino-6,7-dimethoxyquinazoline template led to the discovery of 8 (UNC0224) as a potent and selective G9a inhibitor. A high resolution X-ray crystal structure of the G9a-8 complex, the first cocrystal structure of G9a with a small molecule inhibitor, was obtained. The cocrystal structure validated our binding hypothesis and will enable structure-based design of novel inhibitors. 8 is a useful tool for investigating the biology of G9a and its roles in chromatin remodeling.

The cytochrome P450 (CYP) gene family catalyzes drug metabolism and bioactivation and is therefore relevant to drug development. We determined potency values for 17,143 compounds against five recombinant CYP isozymes (1A2, 2C9, 2C19, 2D6 and 3A4) using an in vitro bioluminescent assay. The compounds included libraries of US Food and Drug Administration (FDA)-approved drugs and screening libraries. We observed cross-library isozyme inhibition (30-78%) with important differences between libraries. Whereas only 7% of the typical screening library was inactive against all five isozymes, 33% of FDA-approved drugs were inactive, reflecting the optimized pharmacological properties of the latter. Our results suggest that low CYP 2C isozyme activity is a common property of drugs, whereas other isozymes, such as CYP 2D6, show little discrimination between drugs and unoptimized compounds found in screening libraries. We also identified chemical substructures that differentiated between the five isozymes. The pharmacological compendium described here should further the understanding of CYP isozymes.

A concise synthesis of the ERK inhibitor FR180204 has been developed. The synthesis consists of six operationally simple steps and can be utilized to make multi-gram quantities of FR180204.

BACKGROUND: Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the hierarchy by a so-called level selection algorithm. In 2005, a new kind of hierarchical clustering method was introduced by Palla et al. that differs in two ways from Ward's method: it can be used on data on which no full similarity matrix is defined and it can produce overlapping clusters, i.e., allow for multiple membership of items in clusters. These features are optimal for biological and chemical data sets but until now no level selection algorithm has been published for this method. RESULTS: In this article we provide a general selection scheme, the level independent clustering selection method, called LInCS. With it, clusters can be selected from any level in quadratic time with respect to the number of clusters. Since hierarchically clustered data is not necessarily associated with a similarity measure, the selection is based on a graph theoretic notion of cohesive clusters. We present results of our method on two data sets, a set of drug like molecules and set of protein-protein interaction (PPI) data. In both cases the method provides a clustering with very good sensitivity and specificity values according to a given reference clustering. Moreover, we can show for the PPI data set that our graph theoretic cohesiveness measure indeed chooses biologically homogeneous clusters and disregards inhomogeneous ones in most cases. We finally discuss how the method can be generalized to other hierarchical clustering methods to allow for a level independent cluster selection. CONCLUSION: Using our new cluster selection method together with the method by Palla et al. provides a new interesting clustering mechanism that allows to compute overlapping clusters, which is especially valuable for biological and chemical data sets.

A series of substituted 6-arylquinazolin-4-amines were prepared and analyzed as inhibitors of Clk4. Synthesis, structure-activity relationships and the selectivity of a potent analogue against a panel of 402 kinases are presented. Inhibition of Clk4 by these agents at varied concentrations of assay substrates (ATP and receptor peptide) highly suggests that this chemotype is an ATP competitive inhibitor. Molecular docking provides further evidence that inhibition is the result of binding at the kinase hinge region. Selected compounds represent novel tools capable of potent and selective inhibition of Clk1, Clk4, and Dyrk1A.

The National Toxicology Program is developing a high-throughput screening (HTS) program to set testing priorities for compounds of interest, to identify mechanisms of action, and potentially to develop predictive models for human toxicity. This program will generate extensive data on the activity of large numbers of chemicals in a wide variety of biochemical- and cell-based assays. The first step in relating patterns of response among batteries of HTS assays to in vivo toxicity is to distinguish between positive and negative compounds in individual assays. Here, the authors report on a statistical approach developed to identify compounds positive or negative in an HTS cytotoxicity assay based on data collected from screening 1353 compounds for concentration-response effects in 9 human and 4 rodent cell types. In this approach, the authors develop methods to normalize the data (removing bias due to the location of the compound on the 1536-well plates used in the assay) and to analyze for concentration-response relationships. Various statistical tests for identifying significant concentration-response relationships and for addressing reproducibility are developed and presented.

The NIH Chemical Genomics Center (NCGC) was the inaugural center of the Molecular Libraries and Screening Center Network (MLSCN). Along with the nine other research centers of the MLSCN, the NCGC was established with a primary goal of bringing industrial technology and experience to empower the scientific community with small molecule compounds for use in their research. We intend this review to serve as 1) an introduction to the NCGC standard operating procedures, 2) an overview of several of the lessons learned during the pilot phase and 3) a review of several of the innovative discoveries reported during the pilot phase of the MLSCN.