Kinase inhibitors show great promise as a new class of therapeutics. Here we describe an efficient way to determine kinase inhibitor specificity by measuring binding of small molecules to the ATP site of kinases. We have profiled 20 kinase inhibitors, including 16 that are approved drugs or in clinical development, against a panel of 119 protein kinases. We find that specificity varies widely and is not strongly correlated with chemical structure or the identity of the intended target. Many novel interactions were identified, including tight binding of the p38 inhibitor BIRB-796 to an imatinib-resistant variant of the ABL kinase, and binding of imatinib to the SRC-family kinase LCK. We also show that mutations in the epidermal growth factor receptor (EGFR) found in gefitinib-responsive patients do not affect the binding affinity of gefitinib or erlotinib. Our results represent a systematic small molecule-protein interaction map for clinical compounds across a large number of related proteins.

Although genetic analysis has demonstrated that members of the winged helix, or forkhead, family of transcription factors play pivotal roles in the regulation of cellular differentiation and proliferation, both during development and in the adult, little is known of the mechanisms underlying their regulation. Here we show that the activation of phosphatidylinositol 3 (PI3) kinase by extracellular growth factors induces phosphorylation, nuclear export, and transcriptional inactivation of FKHR1, a member of the FKHR subclass of the forkhead family of transcription factors. Protein kinase B (PKB)͞Akt, a key mediator of PI3 kinase signal transduction, phosphorylated recombinant FKHR1 in vitro at threonine-24 and serine-253. Mutants FKHR1(T24A), FKHR1(S253A), and FKHR1(T24A͞S253A) were resistant to both PKB͞Akt-mediated phosphorylation and PI3 kinasestimulated nuclear export. These results indicate that phosphorylation by PKB͞Akt negatively regulates FKHR1 by promoting export from the nucleus.

SUMMARY The presence of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is the most important predictor of liver mortality. There are limited data on the diagnostic accuracy of gut microbiota derived signature for predicting the presence of advanced fibrosis. In this prospective study, we characterized the gut microbiome compositions using whole-genome shotgun sequencing of DNA extracted from stool samples. This study included 86 uniquely well-characterized patients with biopsy-proven NAFLD, 72 of which had mild/moderate (stage 0–2 fibrosis) NAFLD, and 14 had advanced fibrosis (stage 3 or 4 fibrosis). We identified a set of forty features (p-value <0.006), which included 37 bacterial species that were used to construct a Random Forest classifier model to distinguish mild/moderate NAFLD from advanced fibrosis. The model had a robust diagnostic accuracy (AUC 0.936) for detecting advanced fibrosis. This study provides preliminary evidence for a novel fecal-microbiome derived metagenomic signature to detect advanced fibrosis in NAFLD.