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Deciphering the retinoblastoma protein phosphorylation code.

ABSTRACT: Multisite phosphorylation modulates the function of regulatory proteins with complex signaling properties and outputs. The retinoblastoma tumor suppressor protein (Rb) is inactivated by cyclin-dependent kinase (Cdk) phosphorylation in normal and cancer cell cycles, so understanding the molecular mechanisms and effects of Rb phosphorylation is imperative. Rb functions in diverse processes regulating proliferation, and it has been speculated that multisite phosphorylation might act as a code in which discrete phosphorylations control specific activities. The idea of an Rb phosphorylation code is evaluated here in light of recent studies of Rb structure and function. Rb inactivation is discussed with an emphasis on how multisite phosphorylation changes Rb structure and associations with protein partners.

SUBMITTER: Rubin SM

PROVIDER: S-EPMC3529988 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Deciphering the retinoblastoma protein phosphorylation code.

Rubin Seth M SM

Trends in biochemical sciences 20121203 1

Multisite phosphorylation modulates the function of regulatory proteins with complex signaling properties and outputs. The retinoblastoma tumor suppressor protein (Rb) is inactivated by cyclin-dependent kinase (Cdk) phosphorylation in normal and cancer cell cycles, so understanding the molecular mechanisms and effects of Rb phosphorylation is imperative. Rb functions in diverse processes regulating proliferation, and it has been speculated that multisite phosphorylation might act as a code in wh ...[more]

PMID: 23218751

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Project description:BackgroundAlthough DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiation. To fill this gap, we employed two types of deep convolutional neural networks (CNNs) constructed for each of differentially related cell types and for each of histone marks measured in the cells, to learn the sequence determinants of various histone modification patterns in each cell type.ResultsWe applied our models to four differentially related human CD4+ T cell types and six histone marks measured in each cell type. The cell models can accurately predict the histone marks in each cell type, while the mark models can also accurately predict the cell types based on a single mark. Sequence motifs learned by both the cell or mark models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Both the unique histone mark patterns in each cell type and the different patterns of the same histone mark in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of sharing motifs learned in the different cell models reflects the lineage relationships of the cells, while the level of sharing motifs learned in the different histone mark models reflects their functional relationships. These models can also enable the prediction of the importance of learned motifs and their interactions in determining specific histone mark patterns in the cell types.ConclusionSequence determinants of various histone modification patterns in different cell types can be revealed by comparative analysis of motifs learned in the CNN models for multiple cell types and histone marks. The learned motifs are interpretable and may provide insights into the underlying molecular mechanisms of establishing the unique epigenomes in different cell types. Thus, our results support the hypothesis that DNA sequences ultimately determine the unique epigenomes of different cell types through their interactions with transcriptional factors, epigenome remodeling system and extracellular cues during cell differentiation.

Dataset Information

Deciphering the retinoblastoma protein phosphorylation code.

Publications

Deciphering the retinoblastoma protein phosphorylation code.

Similar Datasets

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