Author: bugman
Date: Sat Feb 6 02:23:23 2010
New Revision: 10681
URL: http://svn.gna.org/viewcvs/relax?rev=10681&view=rev
Log:
Merged revisions 10676,10680 via svnmerge from
svn+ssh://bugman@xxxxxxxxxxx/svn/relax/1.3
........
r10676 | bugman | 2010-02-06 01:34:06 +0100 (Sat, 06 Feb 2010) | 3 lines
Fixes for some journal titles. Periods have been added.
........
r10680 | bugman | 2010-02-06 02:16:35 +0100 (Sat, 06 Feb 2010) | 6 lines
Created the module for the automatic relaxation curve fitting protocol.
This should better help define the API interface between the UIs and the
core, allowing all UIs to
benefit from advances in this code base.
........
Added:
branches/bieri_gui/auto_analyses/relax_fit.py
- copied unchanged from r10680, 1.3/auto_analyses/relax_fit.py
Modified:
branches/bieri_gui/ (props changed)
branches/bieri_gui/info.py
Propchange: branches/bieri_gui/
------------------------------------------------------------------------------
--- svnmerge-integrated (original)
+++ svnmerge-integrated Sat Feb 6 02:23:23 2010
@@ -1,1 +1,1 @@
-/1.3:1-10674
+/1.3:1-10680
Modified: branches/bieri_gui/info.py
URL:
http://svn.gna.org/viewcvs/relax/branches/bieri_gui/info.py?rev=10681&r1=10680&r2=10681&view=diff
==============================================================================
--- branches/bieri_gui/info.py (original)
+++ branches/bieri_gui/info.py Sat Feb 6 02:23:23 2010
@@ -255,7 +255,7 @@
author = "d'Auvergne, E. J. and Gooley, P. R."
title = "Optimisation of NMR dynamic models I. Minimisation
algorithms and their performance within the model-free and Brownian
rotational diffusion spaces."
- journal = "J Biomol NMR"
+ journal = "J. Biomol. NMR"
volume = "40"
number = "2"
pages = "107-119"
@@ -273,11 +273,11 @@
author = "d'Auvergne, E. J. and Gooley, P. R."
title = "Optimisation of NMR dynamic models II. A new
methodology for the dual optimisation of the model-free parameters and the
Brownian rotational diffusion tensor."
- journal = "J Biomol NMR"
+ journal = "J. Biomol. NMR"
volume = "40"
number = "2"
pages = "121-133"
- abstract = "Finding the dynamics of an entire macromolecule is a
complex problem as the model-free parameter values are intricately linked to
the Brownian rotational diffusion of the molecule, mathematically through the
autocorrelation function of the motion and statistically through model
selection. The solution to this problem was formulated using set theory as an
element of the universal set [formula: see text]-the union of all model-free
spaces (d'Auvergne EJ and Gooley PR (2007) Mol BioSyst 3(7), 483-494). The
current procedure commonly used to find the universal solution is to
initially estimate the diffusion tensor parameters, to optimise the
model-free parameters of numerous models, and then to choose the best model
via model selection. The global model is then optimised and the procedure
repeated until convergence. In this paper a new methodology is presented
which takes a different approach to this diffusion seeded model-free
paradigm. Rather than starting with the diffusion tensor this iterative
protocol begins by optimising the model-free parameters in the absence of any
global model parameters, selecting between all the model-free models, and
finally optimising the diffusion tensor. The new model-free optimisation
protocol will be validated using synthetic data from Schurr JM et al. (1994)
J Magn Reson B 105(3), 211-224 and the relaxation data of the
bacteriorhodopsin (1-36)BR fragment from Orekhov VY (1999) J Biomol NMR
14(4), 345-356. To demonstrate the importance of this new procedure the NMR
relaxation data of the Olfactory Marker Protein (OMP) of Gitti R et al.
(2005) Biochem 44(28), 9673-9679 is reanalysed. The result is that the
dynamics for certain secondary structural elements is very different from
those originally reported."
+ abstract = "Finding the dynamics of an entire macromolecule is a
complex problem as the model-free parameter values are intricately linked to
the Brownian rotational diffusion of the molecule, mathematically through the
autocorrelation function of the motion and statistically through model
selection. The solution to this problem was formulated using set theory as an
element of the universal set [formula: see text]-the union of all model-free
spaces (d'Auvergne EJ and Gooley PR (2007) Mol. BioSyst. 3(7), 483-494). The
current procedure commonly used to find the universal solution is to
initially estimate the diffusion tensor parameters, to optimise the
model-free parameters of numerous models, and then to choose the best model
via model selection. The global model is then optimised and the procedure
repeated until convergence. In this paper a new methodology is presented
which takes a different approach to this diffusion seeded model-free
paradigm. Rather than starting with the diffusion tensor this iterative
protocol begins by optimising the model-free parameters in the absence of any
global model parameters, selecting between all the model-free models, and
finally optimising the diffusion tensor. The new model-free optimisation
protocol will be validated using synthetic data from Schurr JM et al. (1994)
J. Magn. Reson. B 105(3), 211-224 and the relaxation data of the
bacteriorhodopsin (1-36)BR fragment from Orekhov VY (1999) J. Biomol. NMR
14(4), 345-356. To demonstrate the importance of this new procedure the NMR
relaxation data of the Olfactory Marker Protein (OMP) of Gitti R et al.
(2005) Biochem. 44(28), 9673-9679 is reanalysed. The result is that the
dynamics for certain secondary structural elements is very different from
those originally reported."
authoraddress = "Department of NMR-based Structural Biology, Max Planck
Institute for Biophysical Chemistry, Am Fassberg 11, Goettingen, D-37077,
Germany"
keywords = "Algorithms ; Amides/chemistry ;
Bacteriorhodopsins/chemistry ; Crystallography, X-Ray ; Diffusion ; *Models,
Molecular ; Nuclear Magnetic Resonance, Biomolecular/*methods ; Olfactory
Marker Protein/chemistry ; Peptide Fragments/chemistry ; Protein Structure,
Secondary ; *Rotation"
language = "eng"
r10681 - in /branches/bieri_gui: ./ auto_analyses/relax_fit.py info.py