| Chapter title |
Predicting Conformational Disorder.
|
|---|---|
| Chapter number | 14 |
| Book title |
Data Mining Techniques for the Life Sciences
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3572-7_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3570-3, 978-1-4939-3572-7
|
| Authors |
Philippe Lieutaud, François Ferron, Sonia Longhi |
| Editors |
Oliviero Carugo, Frank Eisenhaber |
| Abstract |
In the last two decades, it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded at the amino acid sequence level, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting disorder and identifying regions involved in induced folding. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 27% |
| Student > Bachelor | 3 | 20% |
| Student > Ph. D. Student | 2 | 13% |
| Other | 1 | 7% |
| Student > Master | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 3 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 33% |
| Agricultural and Biological Sciences | 2 | 13% |
| Chemistry | 2 | 13% |
| Neuroscience | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 4 | 27% |