Hi,
I'm actually changing that code at the moment. If you follow my
commit messages you'll see how this will be handled in relax, very
soon. The changes are happening in the multi_structure branch where I
am introducing the distinction between different models (the same
molecules but with different conformations) verses different molecules
within one model. I'm modifying the structure.vectors() user function
now, and this is probably the one you are interested in. Using this
function, you can extract the vectors from all different models and
these will be placed in the spin container. With the 'ave' arg set to
False, each spin container will have a list of 3D vectors for all
models.
As for analysing the spread of these vectors and then doing something
with that, nothing is present in relax. If you would like to have
this functionality, a little work will need to be done and a few
algorithms designed. The eliminate() user function for deselecting
spins is close but not quite what you need. It accepts an arbitrary
function but is not the correct place for this idea. Better would be
something like:
deselect.mobile()
deselect.structural_noise()
deselect.vectors()
deselect.spread_vectors()
with an argument such as (vector_spread_cutoff=0.53), where 0.53 is
some measure of the spread. This might be expandable to B-factors.
If the name deselect.mobile() is used, this could cover a vector
spread, B-factors, low S2 values, etc. Oh, with the deselect user
functions, there should always be a counterpart in the select user
function class. This will allow the deselected spins of the first
analysis to be used in a second with local tm values rather than a
global diffusion tensor. Is this what you had in mind?
Regards,
Edward
On Thu, Jan 15, 2009 at 4:33 PM, Sébastien Morin
<sebastien.morin.1@xxxxxxxxx> wrote:
Hi,
We are currently studying a system for which many structures are available.
We thought it would be a great idea to compare X-H (N-H in our case)
orientations to spot residues for which the orientation varies in the
different structures, i.e. for which there might be important motions.
We thought this could be useful to eliminate such residues with
tremendous motions from diffusion tensor optimization and test them as
diffusing independently (local tm).
Is there a way to do that in relax ?
We saw the sample script 'xh_vector_dist.py' which allows one to plot
the vector orientations distribution. However, what we would like to get
would be a file containing the orientations which could allow us to
compare different structures quantitatively on a per residue basis...
Thanks for help !
Sébastien Morin
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Re: X-H vector orientations