Author: bugman
Date: Tue Feb 23 23:43:16 2010
New Revision: 10878
URL: http://svn.gna.org/viewcvs/relax?rev=10878&view=rev
Log:
Added the DOI number for the d'Auvergne and Gooley 2003 ref.
Modified:
1.3/info.py
Modified: 1.3/info.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/info.py?rev=10878&r1=10877&r2=10878&view=diff
==============================================================================
--- 1.3/info.py (original)
+++ 1.3/info.py Tue Feb 23 23:43:16 2010
@@ -521,6 +521,7 @@
abstract = "Model-free analysis of NMR relaxation data, which is
widely used for the study of protein dynamics, consists of the separation of
the global rotational diffusion from internal motions relative to the
diffusion frame and the description of these internal motions by amplitude
and timescale. Five model-free models exist, each of which describes a
different type of motion. Model-free analysis requires the selection of the
model which best describes the dynamics of the NH bond. It will be
demonstrated that the model selection technique currently used has two
significant flaws, under-fitting, and not selecting a model when one ought to
be selected. Under-fitting breaks the principle of parsimony causing bias in
the final model-free results, visible as an overestimation of S2 and an
underestimation of taue and Rex. As a consequence the protein falsely appears
to be more rigid than it actually is. Model selection has been extensively
developed in other fields. The techniques known as Akaike's Information
Criteria (AIC), small sample size corrected AIC (AICc), Bayesian Information
Criteria (BIC), bootstrap methods, and cross-validation will be compared to
the currently used technique. To analyse the variety of techniques, synthetic
noisy data covering all model-free motions was created. The data consists of
two types of three-dimensional grid, the Rex grids covering single motions
with chemical exchange [S2,taue,Rex], and the Double Motion grids covering
two internal motions [S f 2,S s 2,tau s ]. The conclusion of the comparison
is that for accurate model-free results, AIC model selection is essential. As
the method neither under, nor over-fits, AIC is the best tool for applying
Occam's razor and has the additional benefits of simplifying and speeding up
model-free analysis."
authoraddress = "Department of Biochemistry and Molecular Biology,
University of Melbourne, Melbourne, Victoria 3010, Australia.
ejdauv@xxxxxxxxxxxxxxxxxxxx"
keywords = "Amines ; Diffusion ; *Models, Molecular ; Motion ;
Nuclear Magnetic Resonance, Biomolecular/*methods ; Proteins/*chemistry ;
Research Support, Non-U.S. Gov't ; Rotation"
+ doi = "10.1023/A:1021902006114"
pubmed_id = 12566997
year = 2003
r10878 - /1.3/info.py