On Wed, 2006-12-27 at 16:49 +0800, Hongyan Li wrote:
Dear relax users,
I have tried to run relax with my dynamic data. Using the simplest isotropic
model, I haved run mf-multimodel.py (without Monte Carlo simulations) and
modsel.py to select a best model for each residue. I would like to use
selected
model to run again with Monte Carlo simulations like what I did in
Modelfree.
The simplest way to do this is probably to insert the Monte Carlo
simulations into the modsel.py script, immediately after doing the model
selection. So the last few lines of the script should look like:
...
# Model selection.
run.create('aic', 'mf')
model_selection('AIC', 'aic')
# Monte Carlo Simulations
monte_carlo.setup('aic', number=100)
monte_carlo.create_data('aic')
monte_carlo.initial_values('aic')
minimise('newton', run='aic')
eliminate(run='aic')
monte_carlo.error_analysis('aic')
# Write the results.
state.save('save', force=1)
results.write(run='aic', file='results', force=1)
I
wonder if there is a script for this purpose and how to float tm value which
was estimated accoring from T1/T2ratio, so that relax can also simulate it.
Again, this can be done by simple modification of the end of the
modsel.py script. Something like:
...
# Model selection.
run.create('aic', 'mf')
model_selection('AIC', 'aic')
# Minimise all parameters.
fix('aic', 'all', fixed=0)
minimise('newton', run='aic')
# Write the results.
state.save('save', force=1)
results.write(run='aic', file='results', force=1)
Note that because of the dimensionality of the function being minimised
here, grid search is not possible. Minimisation is likely to find only a
very local minimum. It is therefore important to do this only after
optimising dynamic parameters with respect to a good estimate of tm.
It is good practice to iterate the whole proceedure until the result
converges.
Chris
Any suggestion would be highly appreciated!
Cheers!
Hongyan
Dr. Hongyan Li
Department of Chemistry
The University of Hong Kong
Pokfulam Road
Hong Kong
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Re: scripts