Chapter title |
Computational Protein Design
|
---|---|
Chapter number | 14 |
Book title |
Computational Protein Design
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6637-0_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6635-6, 978-1-4939-6637-0
|
Authors |
Wei, Qing, La, David, Kihara, Daisuke, Qing Wei, David La, Daisuke Kihara |
Editors |
Ilan Samish |
Abstract |
Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ . |
Twitter Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 3 | 60% |
United States | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 60% |
Scientists | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Professor | 2 | 25% |
Researcher | 2 | 25% |
Student > Ph. D. Student | 1 | 13% |
Student > Master | 1 | 13% |
Unknown | 2 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 2 | 25% |
Business, Management and Accounting | 1 | 13% |
Biochemistry, Genetics and Molecular Biology | 1 | 13% |
Immunology and Microbiology | 1 | 13% |
Engineering | 1 | 13% |
Other | 0 | 0% |
Unknown | 2 | 25% |