Author: bugman
Date: Sun Oct 5 21:09:56 2008
New Revision: 7524
URL: http://svn.gna.org/viewcvs/relax?rev=7524&view=rev
Log:
Restructured the introductory docstring and added the references for all
techniques in the script.
Modified:
1.3/sample_scripts/full_analysis.py
Modified: 1.3/sample_scripts/full_analysis.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/sample_scripts/full_analysis.py?rev=7524&r1=7523&r2=7524&view=diff
==============================================================================
--- 1.3/sample_scripts/full_analysis.py (original)
+++ 1.3/sample_scripts/full_analysis.py Sun Oct 5 21:09:56 2008
@@ -22,12 +22,43 @@
"""Script for black-box model-free analysis.
+This script is designed for those who appreciate black-boxes or those who
appreciate complex code. Importantly data at multiple magnetic field
strengths is essential for this analysis. The script will need to be heavily
tailored to the molecule in question by changing the variables just below
this documentation. If you would like to change how model-free analysis is
performed, the code in the class Main can be changed as needed. For a
description of object-oriented coding in python using classes,
functions/methods, self, etc., see the python tutorial.
+
+
+References
+==========
+
The model-free optimisation methodology herein is that of:
-d'Auvergne, E. J. and Gooley, P. R. (2008). Optimisation of NMR dynamic
models II. A new methodology for the dual optimisation of the model-free
parameters and the Brownian rotational diffusion tensor. J. Biomol. NMR,
40(2), 121-133
-
-
-This script is designed for those who appreciate black-boxes or those who
appreciate complex code. Importantly data at multiple magnetic field
strengths is essential for this analysis. The script will need to be heavily
tailored to the molecule in question by changing the variables just below
this documentation. If you would like to change how model-free analysis is
performed, the code in the class Main can be changed as needed. For a
description of object-oriented coding in python using classes,
functions/methods, self, etc., see the python tutorial.
+ d'Auvergne, E. J. and Gooley, P. R. (2008b). Optimisation of NMR dynamic
models II. A new methodology for the dual optimisation of the model-free
parameters and the Brownian rotational diffusion tensor. J. Biomol. NMR,
40(2), 121-133
+
+Other references for features of this script include model-free model
selection using Akaike's Information Criterion:
+
+ dâAuvergne, E. J. and Gooley, P. R. (2003). The use of model selection
in the model-free analysis of protein dynamics. J. Biomol. NMR, 25(1), 25-39.
+
+The elimination of failed model-free models and Monte Carlo simulations:
+
+ dâAuvergne, E. J. and Gooley, P. R. (2006). Model-free model
elimination: A new step in the model-free dynamic analysis of NMR relaxation
data. J. Biomol. NMR, 35(2), 117-135.
+
+Significant model-free optimisation improvements:
+
+ dâAuvergne, E. J. and Gooley, P. R. (2008a). Optimisation of NMR
dynamic models I. Minimisation algorithms and their performance within the
model-free and Brownian rotational diffusion spaces. J. Biomol. NMR, 40(2),
107-109.
+
+Rather than searching for the lowest chi-squared value, this script searches
for the model with the lowest AIC criterion. This complex multi-universe,
multi-dimensional search is formulated using set theory as the universal
solution:
+
+ dâAuvergne, E. J. and Gooley, P. R. (2007). Set theory formulation of
the model-free problem and the diffusion seeded model-free paradigm. 3(7),
483-494.
+
+The basic three references for the original and extended model-free theories
are:
+
+ Lipari, G. and Szabo, A. (1982a). Model-free approach to the
interpretation of nuclear magnetic-resonance relaxation in macromolecules I.
Theory and range of validity. J. Am. Chem. Soc., 104(17), 4546-4559.
+
+ Lipari, G. and Szabo, A. (1982b). Model-free approach to the
interpretation of nuclear magnetic-resonance relaxation in macromolecules II.
Analysis of experimental results. J. Am. Chem. Soc., 104(17), 4559-4570.
+
+ Clore, G. M., Szabo, A., Bax, A., Kay, L. E., Driscoll, P. C., and
Gronenborn, A.M. (1990). Deviations from the simple 2-parameter model-free
approach to the interpretation of N-15 nuclear magnetic-relaxation of
proteins. J. Am. Chem. Soc., 112(12), 4989-4991.
+
+
+How to use this script
+======================
The value of the variable DIFF_MODEL will determine the behaviour of this
script. The five diffusion models used in this script are:
@@ -51,7 +82,7 @@
Model I - Local tm
-==================
+~~~~~~~~~~~~~~~~~~
This will optimise the diffusion model whereby all spin of the molecule have
a local tm value, i.e. there is no global diffusion tensor. This model needs
to be optimised prior to optimising any of the other diffusion models. Each
spin is fitted to the multiple model-free models separately, where the
parameter tm is included in each model.
@@ -59,7 +90,7 @@
Model II - Sphere
-=================
+~~~~~~~~~~~~~~~~~
This will optimise the isotropic diffusion model. Multiple steps are
required, an initial optimisation of the diffusion tensor, followed by a
repetitive optimisation until convergence of the diffusion tensor. Each of
these steps requires this script to be rerun. For the initial optimisation,
which will be placed in the directory './sphere/init/', the following steps
are used:
@@ -80,26 +111,26 @@
Model III - Prolate spheroid
-============================
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The methods used are identical to those of diffusion model MII, except that
an axially symmetric diffusion tensor with Da >= 0 is used. The base
directory containing all the results is './prolate/'.
Model IV - Oblate spheroid
-==========================
+~~~~~~~~~~~~~~~~~~~~~~~~~~
The methods used are identical to those of diffusion model MII, except that
an axially symmetric diffusion tensor with Da <= 0 is used. The base
directory containing all the results is './oblate/'.
Model V - Ellipsoid
-===================
+~~~~~~~~~~~~~~~~~~~
The methods used are identical to those of diffusion model MII, except that
a fully anisotropic diffusion tensor is used (also known as rhombic or
asymmetric diffusion). The base directory is './ellipsoid/'.
Final run
-=========
+~~~~~~~~~
Once all the diffusion models have converged, the final run can be executed.
This is done by setting the variable DIFF_MODEL to 'final'. This consists
of two steps, diffusion tensor model selection, and Monte Carlo simulations.
Firstly AIC model selection is used to select between the diffusion tensor
models. Monte Carlo simulations are then run solely on this selected
diffusion model. Minimisation of the model is bypassed as it is assumed that
the model is already fully optimised (if this is not the case the final run
is not yet appropriate).
r7524 - /1.3/sample_scripts/full_analysis.py