Hi Edward, thank you for your extensive comments. This helps me a lot.
On 05.02.2013, at 11:21, Edward d'Auvergne <[email protected]> wrote: > >> It cannot mean that the "m0"-residues behave like a static body (S^2 would >> be 1). > > Statistically, yes. Physically, no. You just can't see it from the > data you have. That is the meaning of this model. A good analogy is > as follows - you could have a picture of an elephant but, if you only > have 4 pixels in that picture, you probably won't be able to tell that > your picture is of an elephant. Haha! Grey pixels, you say? :) I'm not done with the analysis of all of my complexes, but I fear that even with everything done "correctly" there will be "m0" all over the place and I don't know how to interpret this in terms of mobility. Judging from the runs I did until now, especially the interesting (i.e. probably more mobile) regions of the more interesting protein show this behaviour. As I said, I have quite large areas that disappear from my spectra from one protein variant to the other – so this is an indication for exchange mobility in this regions which is interesting for itself! 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. The current data are not perfect, as the necessary (!) R1 temperature compensation was not used yet and also no soft pulses. So obviously I have re-record some of the data. I used only one single sample, which was pretty stable over the time I measured (no visible precipitation, but very slight decreasing TROSY intensity). The temperature is off by less than 1 K (remember our fucked-up but-now-apparently-fixed calibration procedure). The consistency tests returned a fairly centered distribution (ratio of j0 at different fields: 0.993 +/- 0.174) of moderate consistency ("j0 test" (field1-field2)/field2 = 0.08). That said, I don't see so overwhelmingly much of these stark m0 effects in the protein I expect to be more rigid, although I have only a dataset wich is highly inconsistent due to large temperature diffences, that was much less stable used only old-school experiments with hard pulses have been used. My SH3 testing data don't show this kind of behaviour (no m0 at all), but these have incredibly fat signal. Having a "real" protein changes a few things I guess, especially in terms of S/N. Maybe it's because of more complex motions. Maybe I should have gone for relaxation dispersion in the first place. But "one step after another" seemed reasonable at that time. (I'm currently quite desperately looking for an introductory review like Séb Morin's "practical guide" for relaxation dispersion – do you know one?) > 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). > 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. [...] > Whereas > noise shifts parameter values around randomly and governs which > motions are statistically significant, bias on the other hand shifts > everything in one direction. 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 ... > Bias could probably in some cases > hide motions, but more likely will result in artificial motions. Bias > could also be introduced if the spherical, spheroidal, or ellipsoidal > diffusion is too simplified for the system or if partial dimerisation > is occurring. That would be the jackpot of course – throwing over all the work we did just to find out that the simplistic diffusion models don't fit our system. :D > The 10 seconds for the NOE seems a little excessive (unless this is an > IDP or very small protein). Have you quantified the time required for > recovery? I didn't really "quantify" in terms which delay length is the minimum I can use, but tested the recommendation of 10s by Lakomek/Bax for TROSY-based sequences and deuterated proteins (from the paper I cited earlier). There they reported that for their system they got identical values for HSQC-based readouts and TROSY-based readouts if they used the mentioned precautions. I tried "traditional" non-selective pulses and 3s interscan delay vs. selective pulses and 10s with the same 45 kDa protein complex and saw large differences in HetNOE values. Before, I tested also SH3 with different combinations of soft/hard pulses and length of interscan delay and the trend was that with non-selective hard pulses you get higher HetNOE ratios (sometimes > 1) and the longer the delay is the lower the HetNOE ratios values get. > As for the peak picking and fitting, [...] I do it quite similarly, except that CCPN analysis always searches for maxima and I always pick positive noise (except there is no maximum, then it picks noise at the reference position). My workaround is to set the boundaries of the "search box" to 0 that the crazy searching algorithm doesn't let the peaks wander around too much. Contrary to what one would expect the routine still looks for maxima. If you asked me, I'd say that's pretty broken, but on the mailing list they weren't really open for discussion on that matter. But after all the difference should be tiny and not significant for my problems. > One other thing you need to be > very careful with is sample concentration. If you require multiple > samples then you should aim to have identical protein concentrations > (volume does not matter). Slight concentration differences can have a > large effect on the global tumbling of you system, hence the data > cannot be combined. What do you say – how much concentration difference is still OK? I measured samples between 320-330 µM (which is the maximum that is feasible for the more unstable complexes) which amounts to a concentration difference of ~ 3%. An additional problem is the inaccuracy of the concentration determination by UV(280nm) and of course the sample degradation over time. I never quantified the concentration after the measurements, which in hindsight seems pretty stupid. I should check if there are any differences (there certainly are, but I wonder if they turn out as significant). Sorry for the long sermon. I appreciate that you always read my stuff and also answer in a really helpful and extensive manner. Cheers Martin -- Martin Ballaschk AG Schmieder Leibniz-Institut für Molekulare Pharmakologie Robert-Rössle-Str. 10 13125 Berlin [email protected] Tel.: +49-30-94793-234/315 Büro: A 1.26 Labor: C 1.10 _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-users mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-users
