Dear Chris,
Thanks for the help! Can I do both i.e. Minimise all parameters and Monte Carlo
Simulations? I am still a bit confused the procedure of using relax. In
modelfree, we usually fit data into three models e.g. isotropic, axially
symetric or totally anistropic, and in each model for example isotropic, the
procedure will be
1. estimate tm from T1/T2 or other programs
2 fit each residue into five models
3 select the best model for each residue
4 Fitting and simulating tm and all model free parameters simmutanueously using
the selected model for each residue (with error analysis)
What about relax? Do I need to run 4? I am trying to Minimise all parameters
first and then Monte Carlo Simulations, but it seems very slow.
Cheers!
Hongyan
Quoting Chris MacRaild <[EMAIL PROTECTED]>:
> 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
> >
> >
> > _______________________________________________
> > relax (http://nmr-relax.com)
> >
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> >
>
Dr. Hongyan Li
Department of Chemistry
The University of Hong Kong
Pokfulam Road
Hong Kong
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