| Chapter title |
Identification of Direct Kinase Substrates Using Analogue-Sensitive Alleles.
|
|---|---|
| Chapter number | 5 |
| Book title |
Phospho-Proteomics
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3049-4_5 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3048-7, 978-1-4939-3049-4
|
| Authors |
Rothenberg, Daniel A, Gordon, Elizabeth A, White, Forest M, Lourido, Sebastian, Daniel A. Rothenberg, Elizabeth A. Gordon, Forest M. White, Sebastian Lourido, Rothenberg, Daniel A., Gordon, Elizabeth A., White, Forest M. |
| Editors |
Louise von Stechow |
| Abstract |
Identifying the substrates of protein kinases remains a major obstacle in the elucidation of eukaryotic signaling pathways. Promiscuity among kinases and their substrates coupled with the extraordinary plasticity of phosphorylation networks renders traditional genetic approaches or small-molecule inhibitors problematic when trying to determine the direct substrates of an individual kinase. Here we describe methods to label, enrich, and identify the direct substrates of analogue-sensitive kinases by exploiting their steric complementarity to artificial ATP analogues. Using calcium-dependent protein kinases of Toxoplasma gondii as a model for these approaches, this protocol brings together numerous advances that enable labeling of kinase targets in semi-permeabilized cells, quantification of direct labeling over background, and highly specific enrichment of targeted phosphopeptides. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 5% |
| France | 1 | 5% |
| Unknown | 20 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 23% |
| Researcher | 5 | 23% |
| Student > Doctoral Student | 3 | 14% |
| Professor > Associate Professor | 2 | 9% |
| Student > Master | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 32% |
| Biochemistry, Genetics and Molecular Biology | 6 | 27% |
| Computer Science | 2 | 9% |
| Unspecified | 1 | 5% |
| Immunology and Microbiology | 1 | 5% |
| Other | 2 | 9% |
| Unknown | 3 | 14% |