Hi Edward,
thanks for your reply.
On 07.02.2013, at 10:57, Edward d'Auvergne <edward@xxxxxxxxxxxxx> wrote:
Neighbouring regions have a lot of "m0" (in 62 of ~220 assigned residues
minus 28 unresolved) and in the ellipsoidal diffusion model there is also
a lot of strange Rex = 0.0000 terms, the other models show Rex of around
10^-18 (=nearly zero). Convergence is reached in 20-30 rounds for each
diffusion model, no oscillations are visible.
Is this Rex in the results file or in the extracted version? Note
that relax stores Rex internally as the field strength independent
value of:
sigma_ex = Rex / omega**2
I extract Rex with the following command, I guess this is then the already
field-corrected value?
> value.write( param = 'rex', file = 'example/rex.txt')
I also forgot to mention, that while for the spherical and spheroid models
convergence was reached pretty soon, the ellipsoidal calculations are still
ongoing for two days now, but I can see that the parameter values are not
really changing anymore, and chi^2 and AIC values do not converge but differ
by only a factor of 10^10 from each other in the last ~20 rounds. I guess it
is safe to pull the plug here?
Will the "final" run of relax still be able to see the final optimization
results?
Also note that
currently in all model-free software, Rex is assumed to be fast and
hence scales quadratically with field strength - this might be another
source of problems for your analysis.
You mean, because my Rex is too slow or because I have too few fields
available (600 & 750 MHz)?
R1 temperature compensation is generally not needed as it is quite a
cold experiment, hence will almost always match the normal
spectrometer calibration.
No, what I meant is off-resonance "heating pulses" that make my R1 experiment
just as warm as the R2 experiment which heats the sample due to the CPMG
train in the relaxation period. In practice I just irradiate for ~100 ms
(which is the mean of the delay times I have during my R2 experiments) before
the actual pulse sequence begins, i.e. during d0.
Single-scan interleaved is standardly employed, of course. This is true for
all our experiments: R1, R2, NOE.
Everything else seems fine.
Hey, that's something! At least the procedure as such seems ok.
Relaxation dispersion might be interesting, but from what you describe
I don't think dispersion data will tell you much other than what you
already see with weak peaks. Actually, as your system is 45 kDa, I
would not expect that you would see much dispersion at all - your weak
peaks are due to protein size and not Rex.
I don't get that – certainly the protein size is determining signal intensity
but Rex is an additional factor, right? And I have 4 highly similar complexes
– two different proteins which are mutated at one position which each bind
two identical peptides. X-ray crystal data show virtually identical
structures. Chemical shifts are also mostly very, very similar, but – with
everything else totally the same – strips of signals go missing in one set of
the complexes, and *not* the other.
So there are definitely differences between highly similar complexes, which
is breathtakingly interesting (and which no-one showed before with
experimental data), but what I reached out for is to quantify these
differences. S2 or Rex values from mf analysis seemed like a good bet ;)
As for a guide about
relaxation dispersion, I know no equivalent to Seb's guide. [...] If you do
find something, I'd be interested to have the
reference.
I asked around at Researchgate and got a few reviews by Loria and Palmer. I
feel that the Kay papers usually are so leading edge, they do not cover the
basics and all the practical stuff. The other papers look promising
(especially 1 and 4) but I didn't have the time to look at yet. Here they are:
1. Palmer AG, Grey MJ, Wang C (2005) Solution NMR spin relaxation
methods for characterizing chemical exchange in high-molecular-weight
systems. Meth Enzymol 394: 430–465. doi:10.1016/S0076-6879(05)94018-4.
2. Loria JP, Berlow RB, Watt ED (2008) Characterization of enzyme
motions by solution NMR relaxation dispersion. Acc Chem Res 41: 214–221.
doi:10.1021/ar700132n.
3. Palmer AG, Kroenke CD, Loria JP (2001) Nuclear magnetic resonance
methods for quantifying microsecond-to-millisecond motions in biological
macromolecules. Meth Enzymol 339: 204–238. doi:10.1016/S0076-6879(01)39315-1.
4. Kovrigin EL, Kempf JG, Grey MJ, Loria JP (2006) Faithful estimation
of dynamics parameters from CPMG relaxation dispersion measurements. Journal
of Magnetic Resonance 180: 93–104. doi:10.1016/j.jmr.2006.01.010.
Maybe this relates to model m9 in relax. Sometimes the very weak
peaks, broadened by chemical exchange, are too noisy to extract
model-free motions from. This is visible in relax as the selection of
model m9. In such a case, model m0 will probably not be picked.
I excluded the really noisy/weak peaks beforehand and m9 gets picked
sometimes (9 times m9 opposed to 62 times m0 out of ~220 picked signals).
How many did you exclude? There is no need to exclude such peaks as
the protocol I developed will handle this.
I excluded 6 where the peaks have been so weak that it was essentially only
noise and a fit should give something close to a straight line.
I don't know if this is completely relevant to your question, but
noise is another issue which affects the reliability of the te
parameters. As te increases, so does the errors. [...]
So do you think if my data are too noisy this could be a consequence? I
already reached the limit in terms of scans, protein concentration and
measuring time. Maybe I should write a grant for two new magnets ...
For the spins where m0 are selected, do their errors look larger than
the other spins?
I wouldn't say so:
https://dl.dropbox.com/u/4019316/boxplot.error.pdf
Or if you plot the I0 values from the relaxation
exponential curve-fitting, are these residues much lower than the
rest?
There definitely seems to be a tendency:
https://dl.dropbox.com/u/4019316/boxplot.pdf
Maybe you could try out reduced spectral density mapping (very
easy in relax) and compare errors for the J(w) values.
I will try that in the following days.
As for new spectrometers, having more data would certainly help.
Especially if you have this mixed diffusion tensor problem and have derived
a
solution to test - then having data at 3 or 4 fields would be
incredibly powerful. Maybe you should get your boss to talk to
Griesinger ;) But if you have data at two field strengths, that
should be sufficient.
Ha, we even would have 4 fields at hand around Berlin: We have a a bunch of
600MHz magnets, but also an 750MHz and a 900 MHz field, and the Free
University would have a 800 MHz field. Not all of them are capable of
recording single-scan interleaved pseudo-3D spectra, and that's where the
problems are starting already ... So if anyone should be interested to devote
his life to this question, Berlin might be not the worst place to start
working on it ;)
[HetNOE, deuterated 45 KDa protein]
Oh, it's deuterated. Ok, then you'll need much more time. Though as
it is 45 kDa, the relaxation should be nevertheless relatively fast.
You can quite easily test this if you are curious. You can simply
compare 1D versions of your 2D experiments.
Should be interesting to see. It's always nice to save a bit measuring time –
I need a lot of scans due to the insensitive experiment, a lot of points in
the indirect dimension due to overlap and a lot of d0 time due to
deuteration. One sample sits in the spectrometer for almost an entire week to
get decent HetNOE data. And then I need a second field. And then I two
different proteins, two ligands and the protein complex is a trimer, with
each component labeled individually (twelve differently labeled sample
types). That means I need many weeks measuring time only for all HetNOE data
if I decide to measure absolutely every component from the beginning to the
end ... shaving a bit off isn't probably the worst idea.
Actually, the 45 kDa size could be the reason for the m0 model being
selected. It could simply be too big, the relaxation from tumbling
could be so fast that the internal motions have been hidden in the
data.
So if I see differences between samples that should still mean something. One
protein behaves properly, the other doesn't – there is something going on.
I can only recommend switching to Sparky for this type of analysis.
The main reason for not using sparky was that it cannot read Bruker pseudo 3D
data and converting the individual planes from all the different data sets
without mixing up the different delay times I tried is a incredible pain.
Also, I'd have duplicate data where I could easily mix up file names and lose
information where a specific set of data originally came from. Additionally,
there is the assignment that has been done in CCPN, and which has to be
exported into Sparky. I hope that it is possible, I guess I have to write and
read and validate shift lists.
*But* I will give it a try. It seems like CCPN can't help me here.
Sorry for the long sermon. I appreciate that you always read my stuff and
also answer in a really helpful and extensive manner.
Well, I hope some of my long answers helped.
They do!
If I was you, I would
first redo the relaxation analysis using Sparky (and relax to fit the
exponentials) and compare the data to CCPN.
That's the first thing I'll try.
Then if m0 is still
present, consider if you should blame it on the size of your system
hiding data (more field strengths should then help uncover the
dynamics).
Too bad the console for our 900MHz magnet is so old. I'd have to measure
non-interleaved spectra, which is probably as good as just not doing it at
all.
As you are working with complexes, then maybe an issue is that a
single diffusion tensor is not an adequate representation of the
system, resulting in the model m0 appearing more than it should. This
might be the case if the complex is not tight and you have a mixture
of complex and free monomers. [...]
Finally, you should consider if your complex is tight or
not. If you think it is not [...]
I think it is a pretty tight complex. If one of the components leaves the
heterotrimer, it falls apart, and the "primary" subunit unfolds and
precipitates. But the other component (which I did not look at yet) is still
soluble, which could cause trouble as soon as I start recording data for it
...
Regarding solving a maybe unsolvable statistical / applied physics /
mathematical problem: I'm not sure if I'm the right person to go after it,
I'd need somebody with better stat / physics / math skills ;)
Regards,
Martin
Re: m0 models