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Using Engineered Scaffold Interactions to Reshape MAP Kinase Pathway Signaling Dynamics
Abstract
Scaffold proteins link signaling molecules into linear pathways by physically assembling them into complexes. Scaffolds may also have a higher-order role as signal-processing hubs, serving as the target of feedback loops that optimize signaling amplitude and timing. We demonstrate that the Ste5 scaffold protein can be used as a platform to systematically reshape output of the yeast mating MAP kinase pathway. We constructed synthetic positive- and negative-feedback loops by dynamically regulating recruitment of pathway modulators to an artificial binding site on Ste5. These engineered circuits yielded diverse behaviors: ultrasensitive dose response, accelerated or delayed response times, and tunable adaptation. Protein scaffolds provide a flexible platform for reprogramming cellular responses and could be exploited to engineer cells with novel therapeutic and biotechnological functions.
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Cited by
The second wave of synthetic biology: from modules to systems
Purnick, P. E. M., & Weiss, R. (2009) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2698 · Cited by 1,181
Next-generation synthetic gene networks
Lu, T. K., Khalil, A. S., & Collins, J. J. (2009) · Nature Biotechnology · doi:10.1038/nbt.1591 · Cited by 365
Signaling dynamics of the KSR1 scaffold complex
McKay, M. M., Ritt, D. A., & Morrison, D. K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901590106 · Cited by 163
Metadata
- Authors
- Bashor, C. J., Helman, N. C., Yan, S., & Lim, W. A.
- Venue
- Science
- Published
- 2008-03-14
- Volume
- 319
- Issue
- 5869
- Pages
- 1539-1543
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2067330392
- Cited by
- 347
- References
- 0
- FWCI
- 14.39
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.