de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Structure determination from heterogeneous NMR data

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83949

Habeck,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Rieping, W., Habeck, M., & Nilges, M. (2004). Structure determination from heterogeneous NMR data. In 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (pp. 268-275). Melville, New York: American Institute of Physics.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D7AB-E
Abstract
The principal difficulty in using nuclear magnetic resonance (NMR) data for biomolecular structure determination is not so much experimental imperfections but approximate theories relating structure to measurands. Furthermore, these theories are incomplete as they involve auxiliary parameters which are not measurable. In order to give a reliable picture of a biomolecule, structure determination methods need to determine unknown parameters from definite rules and ought to provide the uncertainty of the derived coordinates. Conventional approaches neglect uncertainties of any kind and therefore by definition fail to give an estimate of structural reliability. In order to deal with auxiliary parameters, they resort to heuristics which renders an objective interpretation of the generated atom positions impossible. Recently, we have introduced a fully probabilistic approach to structure determination from NMR data. We describe here an extension of this approach which incorporates inconsistent nuclear Overhauser effect and J-coupling measurements. Auxiliary parameters are estimated along with the atomic coordinates using Markov Chain Monte Carlo. We apply the method to data sets for two small proteins.