On 27 October 2016 at 18:10, Jeremy Anderson <jande118@xxxxxxx> wrote:
Hi Edward and Troels,
Thanks for pointing me in the right direction. I had dug around a bit in
the test_suite directory but wanted to make sure I was looking in the right
place before I descended into the rabbit hole.
I got the back calculation to work using the
./test_suite/shared_data/dispersion/ns_mmq_3site_linear/relax_results/solution.py
script pretty much as-is, just changing the spin parameters to my liking,
calculating the curve, and outputting the values (ignoring the data and
residuals in the output file).
Something I didn't mention is that the reason I've been importing the models
into ipython is so I can hold parameters constant through my own grid search
and minimization functions, which I had found somewhere in the documentation
was not possible inside relax for the minimization. I originally thought
this would be easier outside of relax.
The reason for this is because I'm in a situation where I can observe HSQC
peaks in slow exchange in one variant and skewed populations of one or the
other peaks in two other variants. I've been working on using the
complementary information, in this case the observed dw and the kex from ZZ
exchange experiments, to investigate multi-state exchange in all variants.
The chem. shift differences of the two skewed variants match the measured
nicely but the rates from CPMG are ~20 fold higher. Therefore I wanted to
check and see if a 3-state model with some parameters held constant would
have infinite solutions (my assumption) or pop out something interesting and
be able to distinguish between a couple models of the conformational process
that I have in mind, which seems like a long shot.
Sorry if thats too much information/way too open-ended but I figured I would
give some context to the greater situation I have found myself in. Thanks
again!
Hi Jeremy,
It is true that you cannot fix a parameter in relax and optimise the
others. The reason is two-fold. Firstly the minfx library (
https://gna.org/projects/minfx/ ) does support this functionality.
Secondly, this functionality would be highly abused and a lot of
rubbish results will appear in the scientific literature, with a
detrimental effect on the reputation of the whole NMR field.
Also, I didn't think it was worth the time investment compared to
expanding relax to handle multiple data types at the same time, and
then optimising one set of parameters for all experimental data
simultaneously. In your case, that would be loading the ZZ exchange
and CPMG data at the same time, and optimising the single model. This
would be interesting, as the two experiment types contain both
complementary and overlapping information content. So saying that the
overlapping content should only come from the ZZ experiment might
over-constrain the CPMG experiment due to any biases or experimental
noise from that experiment. Are you able to set up the problem in
this alternative way in iPython?
Regards,
Edward
Re: Dispersion Back Calculation