Chapter title |
Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
|
---|---|
Chapter number | 4 |
Book title |
Data Mining Techniques for the Life Sciences
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Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3572-7_4 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3570-3, 978-1-4939-3572-7
|
Authors |
M. Michael Gromiha, P. Anoosha, Liang-Tsung Huang |
Editors |
Oliviero Carugo, Frank Eisenhaber |
Abstract |
Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 4% |
Unknown | 22 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 6 | 26% |
Researcher | 4 | 17% |
Student > Master | 3 | 13% |
Student > Postgraduate | 2 | 9% |
Professor | 2 | 9% |
Other | 4 | 17% |
Unknown | 2 | 9% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 5 | 22% |
Computer Science | 2 | 9% |
Chemistry | 2 | 9% |
Immunology and Microbiology | 2 | 9% |
Other | 3 | 13% |
Unknown | 3 | 13% |