Scaffold Proteins Regulate Signal Transduction in Diverse Ways

Jason Locasale, MIT

Abstract

The sequential activation of multiple protein kinases constitutes a highly conserved intermediate step in eukaryotic cell signaling pathways, and is crucial for the regulation of numerous cellular decisions. In many instances (e.g. in several MAPK cascades), these kinase cascades are associated with scaffolding proteins that assemble multiple components of the signaling cascade in sequence.

A systematic variation of the many factors that may influence the mechanisms through which spatial localization of kinases on a scaffold may affect signal propagation is currently not experimentally tractable. For these reasons, we used computer models to investigate whether, how, and under what conditions, assembling a sequence of kinases on a scaffold affects signal propagation through a multi-tiered kinase cascade.

Monte-Carlo simulations of a model kinase cascade are employed to investigate how the characteristics of signaling cascades are influenced by the presence of scaffold proteins. Our studies identify regulatory properties of scaffold proteins that allow them to both amplify and attenuate incoming signals in different biological contexts. These simulations also suggest that scaffold proteins carefully regulate the speed and duration of a signal propagating along a kinase cascade. Furthermore, scaffolds can influence the dynamics of signal propagation by controlling the number of time scales involved in the cell signaling process and thus how signal propagation is distributed over time. Such adaptable behavior illustrates some aspects of the vast array of control properties that scaffolds may confer to the cellular signaling process.

These results bring coherence to seemingly paradoxical observations, and suggest that cells have evolved design rules that enable scaffold proteins to regulate widely disparate cellular functions. It is our hope that these simulations provide a roadmap for future experiments – in particular, several key predictions obtained from our model can be tested with single-molecule technologies.

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