Hi Clare,
Welcome to the relax-users mailing list. I'm sorry for not responding
sooner. I have just moved from Australia to Germany to start a
postdoc and have been struggling to find time to respond to emails.
The answer to your question is that the full_analysis.py script
implements a new protocol which requires data at minimally two field
strengths. The protocol is described in the documentation of that
script and it should hopefully be published soon. The local tm models
are the issue. These are essentially the normal model-free models
with one additional parameter added - the local tm. Therefore for the
most complex model tm8 there are 6 parameters and hence 6 data sets
are essentially for preventing over-fitting (which in model-free
analysis produces false parameter values).
The model-free analysis using models m1 to m5 can nevertheless still
be used. However there are well known problems associated with using
data from a single spectrometer. One such problem is differentiating
exchange (the Rex parameter) from an anisotropic diffusion tensor. If
the diffusion tensor is slightly incorrect, artificial Rex or
nanosecond motions can appear and these may require data at two field
strengths to differentiate (see Schurr et al., 1994 and Tjandra et
al., 1996 (see below)). This may not occur in your system though. I
hope this helps.
Cheers,
Edward
J. M. Schurr, H. P. Babcock and B. S. Fujimoto, A test of the
model-free formulas. Effects of anisotropic rotational diffusion and
dimerization, J. Magn. Reson. B, 1994, 105(3), 211–224.
N. Tjandra, P. Wingfield, S. Stahl and A. Bax, Anisotropic rotational
diffusion of perdeuterated HIV protease from 15NNMR relaxation
measurements at two magnetic fields, J. Biomol. NMR, 1996, 8(3),
273–284
On 4/29/07, Clare-Louise Evans <pcxcle@xxxxxxxxxxxxxxxx> wrote:
I'm new to NMR relaxation and was hoping you could just clarify
something for a newbie.
In the comments contained in the full_analysis.py script it is stated
"It is important that the number of parameters in a model does not
exceed the number of relaxation data sets for that residue..."
I've taken this to mean that if I have only 3 sets of relaxation data
for each residue (R1,R2 and noe) that I should only optimise the models
local_tm and sphere before proceeding with the final run as the other
diffusion models have more than 3 parameters. Have I misunderstood this?
Many thanks
Clare
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Re: full_analysis.py