TBC1D7 is a third subunit of the TSC1-TSC2 complex upstream of mTORC1.

The tuberous sclerosis complex (TSC) tumor suppressors form the TSC1-TSC2 complex, which limits cell growth in response to poor growth conditions. Through its GTPase-activating protein (GAP) activity toward Rheb, this complex inhibits the mechanistic target of rapamycin (mTOR) complex 1 (mTORC1), a key promoter of cell growth. Here, we identify and biochemically characterize TBC1D7 as a stably associated and ubiquitous third core subunit of the TSC1-TSC2 complex. We demonstrate that the TSC1-TSC2-TBC1D7 (TSC-TBC) complex is the functional complex that senses specific cellular growth conditions and possesses Rheb-GAP activity. Sequencing analyses of samples from TSC patients suggest that TBC1D7 is unlikely to represent TSC3. TBC1D7 knockdown decreases the association of TSC1 and TSC2 leading to decreased Rheb-GAP activity, without effects on the localization of TSC2 to the lysosome. Like the other TSC-TBC components, TBC1D7 knockdown results in increased mTORC1 signaling, delayed induction of autophagy, and enhanced cell growth under poor growth conditions.

Chemical substructures that enrich for biological activity.

MOTIVATION: Certain chemical substructures are present in many drugs. This has led to the claim of 'privileged' substructures which are predisposed to bioactivity. Because bias in screening library construction could explain this phenomenon, the existence of privilege has been controversial. RESULTS: Using diverse phenotypic assays, we defined bioactivity for multiple compound libraries. Many substructures were associated with bioactivity even after accounting for substructure prevalence in the library, thus validating the privileged substructure concept. Determinations of privilege were confirmed in independent assays and libraries. Our analysis also revealed 'underprivileged' substructures and 'conditional privilege'-rules relating combinations of substructure to bioactivity. Most previously reported substructures have been flat aromatic ring systems. Although we validated such substructures, we also identified three-dimensional privileged substructures. Most privileged substructures display a wide variety of substituents suggesting an entropic mechanism of privilege. Compounds containing privileged substructures had a doubled rate of bioactivity, suggesting practical consequences for pharmaceutical discovery.

Loss of the tumor suppressor Snf5 leads to aberrant activation of the Hedgehog-Gli pathway.

Aberrant activation of the Hedgehog (Hh) pathway can drive tumorigenesis. To investigate the mechanism by which glioma-associated oncogene family zinc finger-1 (GLI1), a crucial effector of Hh signaling, regulates Hh pathway activation, we searched for GLI1-interacting proteins. We report that the chromatin remodeling protein SNF5 (encoded by SMARCB1, hereafter called SNF5), which is inactivated in human malignant rhabdoid tumors (MRTs), interacts with GLI1. We show that Snf5 localizes to Gli1-regulated promoters and that loss of Snf5 leads to activation of the Hh-Gli pathway. Conversely, re-expression of SNF5 in MRT cells represses GLI1. Consistent with this, we show the presence of a Hh-Gli-activated gene expression profile in primary MRTs and show that GLI1 drives the growth of SNF5-deficient MRT cells in vitro and in vivo. Therefore, our studies reveal that SNF5 is a key mediator of Hh signaling and that aberrant activation of GLI1 is a previously undescribed targetable mechanism contributing to the growth of MRT cells.

Query Chem: a Google-powered web search combining text and chemical structures.

Query Chem (www.QueryChem.com) is a Web program that integrates chemical structure and text-based searching using publicly available chemical databases and Google's Web Application Program Interface (API). Query Chem makes it possible to search the Web for information about chemical structures without knowing their common names or identifiers. Furthermore, a structure can be combined with textual query terms to further restrict searches. Query Chem's search results can retrieve many interesting structure-property relationships of biomolecules on the Web.

Using high-throughput screening data to discriminate compounds with single-target effects from those with side effects.

The most desirable compound leads from high-throughput assays are those with novel biological activities resulting from their action on a single biological target. Valuable resources can be wasted on compound leads with significant 'side effects' on additional biological targets; therefore, technical refinements to identify compounds that primarily have effects resulting from a single target are needed. This study explores the use of multiple assays of a chemical library and a statistic based on entropy to identify lead compound classes that have patterns of assay activity resulting primarily from small molecule action on a single target. This statistic, called the coincidence score, discriminates with 88% accuracy compound classes known to act primarily on a single target from compound classes with significant side effects on nonhomologous targets. Furthermore, a significant number of the compound classes predicted to have primarily single-target effects contain known bioactive compounds. We also show that a compound's known biological target or mechanism of action can often be suggested by its pattern of activities in multiple assays.

Identifying biologically active compound classes using phenotypic screening data and sampling statistics.

Scoring the activity of compounds in phenotypic high-throughput assays presents a unique challenge because of the limited resolution and inherent measurement error of these assays. Techniques that leverage the structural similarity of compounds within an assay can be used to improve the hit-recovery rate from screening data. A technique is presented that uses clustering and sampling statistics to predict likely compound activity by scoring entire structural classes. A set of phenotypic assays performed against a commercially available compound library was used as a test set. Using the class-scoring technique, the resultant activity prediction scores were more reproducible than individual assay measurements, and class scoring recovered known active compounds more efficiently than individual assay measurements because class scoring had fewer false positives. Known biologically active compounds were recovered 87% of the time using class scores, suggesting a low false-negative rate that compared well to individual assay measurements. In addition, many weak and potentially novel classes of active compounds, overlooked by individual assay measurements, were suggested.