Chapter title |
Protein Crystallizability.
|
---|---|
Chapter number | 17 |
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_17 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3570-3, 978-1-4939-3572-7
|
Authors |
Pawel Smialowski, Philip Wong |
Editors |
Oliviero Carugo, Frank Eisenhaber |
Abstract |
Obtaining diffracting quality crystals remains a major challenge in protein structure research. We summarize and compare methods for selecting the best protein targets for crystallization, construct optimization and crystallization condition design. Target selection methods are divided into algorithms predicting the chance of successful progression through all stages of structural determination (from cloning to solving the structure) and those focusing only on the crystallization step. We tried to highlight pros and cons of different approaches examining the following aspects: data size, redundancy and representativeness, overfitting during model construction, and results evaluation. In summary, although in recent years progress was made and several sequence properties were reported to be relevant for crystallization, the successful prediction of protein crystallization behavior and selection of corresponding crystallization conditions continue to challenge structural researchers. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Mexico | 1 | 4% |
Unknown | 23 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 7 | 29% |
Researcher | 5 | 21% |
Student > Bachelor | 5 | 21% |
Student > Postgraduate | 2 | 8% |
Lecturer | 1 | 4% |
Other | 3 | 13% |
Unknown | 1 | 4% |
Readers by discipline | Count | As % |
---|---|---|
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Biochemistry, Genetics and Molecular Biology | 4 | 17% |
Chemistry | 4 | 17% |
Medicine and Dentistry | 2 | 8% |
Immunology and Microbiology | 2 | 8% |
Other | 3 | 13% |
Unknown | 1 | 4% |