3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data
-
Altmetric Citations
Pilla, Kala; Otting, Gottfried; Huber, Thomas
Description
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...[Show more]
dc.contributor.author | Pilla, Kala | |
---|---|---|
dc.contributor.author | Otting, Gottfried | |
dc.contributor.author | Huber, Thomas | |
dc.contributor.editor | Keith, Jonathan | |
dc.date.accessioned | 2021-04-27T01:03:54Z | |
dc.identifier.isbn | 9781493966110 | |
dc.identifier.uri | http://hdl.handle.net/1885/231009 | |
dc.description.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 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_AU | |
dc.publisher | Humana Press Inc. | |
dc.relation.ispartof | Methods in Molecular Biology - Bioinformatics: Structure, Function and Applications | |
dc.relation.isversionof | 1st Edition | |
dc.rights | © Springer Science+Business Media New York 2017 | |
dc.title | 3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data | |
dc.type | Book chapter | |
local.description.notes | Imported from ARIES | |
dc.date.issued | 2017 | |
local.identifier.absfor | 030606 - Structural Chemistry and Spectroscopy | |
local.identifier.ariespublication | u8801298xPUB229 | |
local.publisher.url | https://link.springer.com/ | |
local.type.status | Published Version | |
local.contributor.affiliation | Pilla, Kala, College of Science, ANU | |
local.contributor.affiliation | Otting, Gottfried, College of Science, ANU | |
local.contributor.affiliation | Huber, Thomas, College of Science, ANU | |
local.description.embargo | 2099-12-31 | |
local.bibliographicCitation.startpage | 1 | |
local.bibliographicCitation.lastpage | 21 | |
local.identifier.doi | 10.1007/978-1-4939-6613-4_1 | |
local.identifier.absseo | 970103 - Expanding Knowledge in the Chemical Sciences | |
dc.date.updated | 2020-11-23T10:04:24Z | |
local.bibliographicCitation.placeofpublication | USA | |
local.identifier.scopusID | 2-s2.0-85000819227 | |
Collections | ANU Research Publications |
Download
File | Description | Size | Format | Image |
---|---|---|---|---|
01_Pilla_3D_Computational_Modeling_of_2017.pdf | 6.81 MB | Adobe PDF | Request a copy |
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.
Updated: 17 November 2022/ Responsible Officer: University Librarian/ Page Contact: Library Systems & Web Coordinator