| Chapter title |
Identification of Posttranslational Modification-Dependent Protein Interactions Using Yeast Surface Displayed Human Proteome Libraries.
|
|---|---|
| Chapter number | 10 |
| Book title |
Yeast Surface Display
|
| Published in |
Methods in molecular biology, January 2015
|
| DOI | 10.1007/978-1-4939-2748-7_10 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-2747-0, 978-1-4939-2748-7
|
| Authors |
Bidlingmaier, Scott, Liu, Bin, Scott Bidlingmaier, Bin Liu |
| Abstract |
The identification of proteins that interact specifically with posttranslational modifications such as phosphorylation is often necessary to understand cellular signaling pathways. Numerous methods for identifying proteins that interact with posttranslational modifications have been utilized, including affinity-based purification and analysis, protein microarrays, phage display, and tethered catalysis. Although these techniques have been used successfully, each has limitations. Recently, yeast surface-displayed human proteome libraries have been utilized to identify protein fragments with affinity for various target molecules, including phosphorylated peptides. When coupled with fluorescently activated cell sorting and high throughput methods for the analysis of selection outputs, yeast surface-displayed human proteome libraries can rapidly and efficiently identify protein fragments with affinity for any soluble ligand that can be fluorescently detected, including posttranslational modifications. In this review we compare the use of yeast surface display libraries to other methods for the identification of interactions between proteins and posttranslational modifications and discuss future applications of the technology. |
X Demographics
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| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 19% |
| Student > Doctoral Student | 3 | 19% |
| Student > Ph. D. Student | 2 | 13% |
| Lecturer > Senior Lecturer | 1 | 6% |
| Student > Master | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 5 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
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| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 6% |
| Chemical Engineering | 1 | 6% |
| Computer Science | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 6 | 38% |