Chapter title |
3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data.
|
---|---|
Chapter number | 1 |
Book title |
Bioinformatics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6613-4_1 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6611-0, 978-1-4939-6613-4
|
Authors |
Kala Bharath Pilla, Gottfried Otting, Thomas Huber |
Editors |
Jonathan M. Keith |
Abstract |
Computational modeling of proteins using evolutionary or de novo approaches offers rapid structural characterization, but often suffers from low success rates in generating high quality models comparable to the accuracy of structures observed in X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. A computational/experimental hybrid approach incorporating sparse experimental restraints in computational modeling algorithms drastically improves reliability and accuracy of 3D models. This chapter discusses the use of structural information obtained from various paramagnetic NMR measurements and demonstrates computational algorithms implementing pseudocontact shifts as restraints to determine the structure of proteins at atomic resolution. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 19 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 42% |
Student > Postgraduate | 3 | 16% |
Student > Bachelor | 3 | 16% |
Researcher | 2 | 11% |
Unspecified | 1 | 5% |
Other | 1 | 5% |
Unknown | 1 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 5 | 26% |
Chemistry | 4 | 21% |
Biochemistry, Genetics and Molecular Biology | 3 | 16% |
Environmental Science | 1 | 5% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 5% |
Other | 3 | 16% |
Unknown | 2 | 11% |