Author: bugman
Date: Mon Jun 2 18:59:18 2008
New Revision: 6310
URL: http://svn.gna.org/viewcvs/relax?rev=6310&view=rev
Log:
For the new relax design, 'residue' was renamed to 'spin' and 'protein'
renamed to 'molecule'.
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=6310&r1=6309&r2=6310&view=diff
==============================================================================
--- 1.3/sample_scripts/full_analysis.py (original)
+++ 1.3/sample_scripts/full_analysis.py Mon Jun 2 18:59:18 2008
@@ -41,7 +41,7 @@
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 protein 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.
+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.
The value of the variable DIFF_MODEL will determine the behaviour of this
script. The five diffusion models used in this script are:
@@ -61,15 +61,15 @@
This approach has the advantage of eliminating the need for an initial
estimate of a global diffusion tensor and removing all the problems
associated with the initial estimate.
-It is important that the number of parameters in a model does not exceed the
number of relaxation data sets for that residue. If this is the case, the
list of models in the MF_MODELS and LOCAL_TM_MODELS variables will need to be
trimmed.
+It is important that the number of parameters in a model does not exceed the
number of relaxation data sets for that spin. If this is the case, the list
of models in the MF_MODELS and LOCAL_TM_MODELS variables will need to be
trimmed.
Model I - Local tm
~~~~~~~~~~~~~~~~~~
-This will optimise the diffusion model whereby all residues of the protein
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 residue is fitted to the multiple model-free models separately, where
the parameter tm is included in each model.
-
-AIC model selection is used to select the models for each residue.
+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.
+
+AIC model selection is used to select the models for each spin.
Model II - Sphere
@@ -77,7 +77,7 @@
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:
-The model-free models and parameter values for each residue are set to those
of diffusion model MI.
+The model-free models and parameter values for each spin are set to those of
diffusion model MI.
The local tm parameter is removed from the models.
@@ -86,9 +86,9 @@
For the repetitive optimisation, each minimisation is named from 'round_1'
onwards. The initial 'round_1' optimisation will extract the diffusion
tensor from the results file in './sphere/init/', and the results will be
placed in the directory './sphere/round_1/'. Each successive round will take
the diffusion tensor from the previous round. The following steps are used:
-The global diffusion tensor is fixed and the multiple model-free models are
fitted to each residue.
-
-AIC model selection is used to select the models for each residue.
+The global diffusion tensor is fixed and the multiple model-free models are
fitted to each spin.
+
+AIC model selection is used to select the models for each spin.
All model-free and diffusion parameters are allowed to vary and a global
optimisation of all parameters is carried out.
@@ -150,7 +150,7 @@
['NOE', '500', 500.208 * 1e6, 'noe.500.out']
]
-# The file containing the list of unresolved residues to exclude from the
analysis (set this to None if no residue is to be excluded).
+# The file containing the list of unresolved spins to exclude from the
analysis (set this to None if no spin is to be excluded).
UNRES = 'unresolved'
# A file containing a list of spins which can be dynamically excluded at any
point within the analysis (when set to None, this variable is not used).
r6310 - /1.3/sample_scripts/full_analysis.py