Chapter title |
Directed Evolution Methods to Rewire Signaling Networks
|
---|---|
Chapter number | 20 |
Book title |
Synthetic Protein Switches
|
Published in |
Methods in molecular biology, March 2017
|
DOI | 10.1007/978-1-4939-6940-1_20 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6938-8, 978-1-4939-6940-1
|
Authors |
Raphaël B. Di Roberto, Benjamin M. Scott, Sergio G. Peisajovich |
Editors |
Viktor Stein |
Abstract |
The ability to sense and process cues about changing environments is fundamental to life. Cells have evolved elaborate signaling pathways in order to respond to both internal and external stimuli appropriately. These pathways combine protein receptors, signal transducers, and effector genes in highly connected networks. The numerous interactions found between signaling proteins are essential to maintain strict regulation and produce a suitable cellular response. As a result, a signaling protein's activity in isolation can differ greatly from its activity in a native context. This is an important consideration when studying or engineering signaling pathways. Fortunately, the difficulty of studying network interactions is fading thanks to advances in library construction and cell sorting. In this chapter, we describe two methods for generating libraries of mutant proteins that exhibit altered network interactions: whole-gene point mutagenesis and domain shuffling. We then provide a protocol for using fluorescence-activated cell sorting to isolate interesting variants in live cells by focusing on the unicellular eukaryotic model organism Saccharomyces cerevisiae, using as an example recent work that we have done on its G protein-coupled receptor Ste2. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 11 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 18% |
Student > Bachelor | 2 | 18% |
Researcher | 2 | 18% |
Student > Master | 2 | 18% |
Lecturer | 1 | 9% |
Other | 0 | 0% |
Unknown | 2 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 4 | 36% |
Agricultural and Biological Sciences | 4 | 36% |
Immunology and Microbiology | 1 | 9% |
Unknown | 2 | 18% |