Title |
Exploiting holistic approaches to model specificity in protein phosphorylation
|
---|---|
Published in |
Frontiers in Genetics, September 2014
|
DOI | 10.3389/fgene.2014.00315 |
Pubmed ID | |
Authors |
Antonio Palmeri, Fabrizio Ferrè, Manuela Helmer-Citterich |
Abstract |
Phosphate plays a chemically unique role in shaping cellular signaling of all current living systems, especially eukaryotes. Protein phosphorylation has been studied at several levels, from the near-site context, both in sequence and structure, to the crowded cellular environment, and ultimately to the systems-level perspective. Despite the tremendous advances in mass spectrometry and efforts dedicated to the development of ad hoc highly sophisticated methods, phosphorylation site inference and associated kinase identification are still unresolved problems in kinome biology. The sequence and structure of the substrate near-site context are not sufficient alone to model the in vivo phosphorylation rules, and they should be integrated with orthogonal information in all possible applications. Here we provide an overview of the different contexts that contribute to protein phosphorylation, discussing their potential impact in phosphorylation site annotation and in predicting kinase-substrate specificity. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 2 | 67% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Israel | 1 | 3% |
France | 1 | 3% |
Unknown | 38 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 11 | 28% |
Student > Ph. D. Student | 10 | 25% |
Student > Master | 5 | 13% |
Student > Bachelor | 3 | 8% |
Professor | 2 | 5% |
Other | 8 | 20% |
Unknown | 1 | 3% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 21 | 53% |
Biochemistry, Genetics and Molecular Biology | 8 | 20% |
Computer Science | 3 | 8% |
Medicine and Dentistry | 2 | 5% |
Neuroscience | 1 | 3% |
Other | 1 | 3% |
Unknown | 4 | 10% |