Title |
CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK
|
---|---|
Published in |
PLOS ONE, March 2011
|
DOI | 10.1371/journal.pone.0017695 |
Pubmed ID | |
Authors |
Sjoerd J. de Vries, Alexandre M. J. J. Bonvin |
Abstract |
Macromolecular complexes are the molecular machines of the cell. Knowledge at the atomic level is essential to understand and influence their function. However, their number is huge and a significant fraction is extremely difficult to study using classical structural methods such as NMR and X-ray crystallography. Therefore, the importance of large-scale computational approaches in structural biology is evident. This study combines two of these computational approaches, interface prediction and docking, to obtain atomic-level structures of protein-protein complexes, starting from their unbound components. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 292 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Portugal | 1 | <1% |
Germany | 1 | <1% |
France | 1 | <1% |
Ireland | 1 | <1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
United Kingdom | 1 | <1% |
Japan | 1 | <1% |
United States | 1 | <1% |
Other | 0 | 0% |
Unknown | 283 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 53 | 18% |
Student > Bachelor | 51 | 17% |
Researcher | 39 | 13% |
Student > Master | 36 | 12% |
Student > Doctoral Student | 14 | 5% |
Other | 39 | 13% |
Unknown | 60 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 97 | 33% |
Agricultural and Biological Sciences | 66 | 23% |
Chemistry | 14 | 5% |
Pharmacology, Toxicology and Pharmaceutical Science | 8 | 3% |
Computer Science | 7 | 2% |
Other | 30 | 10% |
Unknown | 70 | 24% |