Hi Edward,
Did you run relax with Gary Thompson's multi-processor
framework to take advantage of all your CPU cores?
Yes, I used mpi4py. And in fact it's a hexacore Intel Core i7, not an
octocore. I'm not sure if I should run mpi4py with the six physical number of
cores or with the double of that numbers (12 hyperthreaded cores).
Now I tried a big protein–one with >240 assigned residues. It took 2 days
and 23 rounds to find a optimized spherical diffusion model, and since
yesterday it churned out 35 prolate diffusion models!
This is quite possible. I would highly recommend you create plots of
the progression of optimisation such as in: [d'A&G:2008b]
You can obtain the data for the plots by manually opening the results
files in the 'opt' directories and then manually creating the graphs.
I stopped the process after relax made >100 prolate models and tried to
extract the mentioned parameters (tm, total number of parameters k, global
AIC, chi2) from the results files in round_x/opt/ to see where it is heading.
Turns out this appears to be more difficult than expected. I can see the chi2
values and all the model-free parameters like S^2 and R_ex, but how can I get
the AIC or the total number of parameters from?
Is there a relax function which lists all the parameters of the current data
pipe?
[...] It could be that you are in almost perpetual motion orbiting around
two solutions, sliding in and
out of different optimisation spaces or universes, one day colliding with
one of them (see below).
If the complex exhibits domain motions, then this could explain the
long optimisation times (though not definitively).
Actually I don't expect anything like that.
I hope this description is not too abstract.
Your explanations are – as always – very verbose and comprehensible in the
most positive way!
Cheers
Martin
--
Martin Ballaschk
AG Schmieder
Leibniz-Institut für Molekulare Pharmakologie
Robert-Rössle-Str. 10
13125 Berlin
ballaschk@xxxxxxxxxxxxx
Tel.: +49-30-94793-234/315
Büro: A 1.26
Labor: C 1.10