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Stuart L Schreiber

Publications and source records attributed to Stuart L Schreiber.

76 records · Page 5Linked to original sources

ATR is not required for p53 activation but synergizes with p53 in the replication checkpoint.

ATR (ataxia telangiectasia and Rad-3-related) is a protein kinase required for survival after DNA damage. A critical role for ATR has been hypothesized to be the regulation of p53 and other cell cycle checkpoints. ATR has been shown to phosphorylate p53 at Ser(15), and this damage-induced phosphorylation is diminished by expression of a catalytically inactive (ATR-kd) mutant. p53 function could not be examined directly in prior studies of ATR, however, because p53 was mutant or because cells expressed the SV40 large T antigen that blocks p53 function. To test the interactions of ATR and p53 directly we generated human U2OS cell lines inducible for either wild-type or kinase-dead ATR that also have an intact p53 pathway. Indeed, ATR-kd expression sensitized these cells to DNA damage and caused a transient decrease in damage-induced serine 15 phosphorylation of p53. However, we found that the effects of ATR-kd expression do not result in blocking the response of p53 to DNA damage. Specifically, prior ATR-kd expression had no effect on DNA damage-induced p53 protein up-regulation, p53-DNA binding, p21 mRNA up-regulation, or G(1) arrest. Instead of promoting survival via p53 regulation, we found that ATR protects cells by delaying the generation of mitotic phosphoproteins and inhibiting premature chromatin condensation after DNA damage or hydroxyurea. Although p53 inhibition (by E6 or MDM2 expression) had little effect on premature chromatin condensation, when combined with ATR-kd expression there was a marked loss of the replication checkpoint. We conclude that ATR and p53 can function independently but that loss of both leads to synergistic disruption of the replication checkpoint.

Ataxia Telangiectasia Mutated Proteins↗

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.

Actins↗

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.

Drug-Related Side Effects and Adverse Reactions↗