Chapter title |
Prediction of Protein Secondary Structure
|
---|---|
Chapter number | 15 |
Book title |
Prediction of Protein Secondary Structure
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6406-2_15 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6404-8, 978-1-4939-6406-2
|
Authors |
Walia, Rasna R, El-Manzalawy, Yasser, Honavar, Vasant G, Dobbs, Drena, Rasna R. Walia, Yasser EL-Manzalawy, Vasant G. Honavar, Drena Dobbs, Walia, Rasna R., EL-Manzalawy, Yasser, Honavar, Vasant G. |
Abstract |
Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 32 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 7 | 22% |
Student > Ph. D. Student | 6 | 19% |
Researcher | 4 | 13% |
Student > Bachelor | 2 | 6% |
Other | 2 | 6% |
Other | 7 | 22% |
Unknown | 4 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 16 | 50% |
Agricultural and Biological Sciences | 3 | 9% |
Computer Science | 2 | 6% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 6% |
Engineering | 2 | 6% |
Other | 2 | 6% |
Unknown | 5 | 16% |