Hi Edward, Sorry for the confusion. The local_tm models were generated and handed over to the next step of the analysis. However, the local_tm.txt was empty because all local_tm values were estimated higher than global tm. S2 values were estimated at first in the same size order I also see if I use the HYDRONMR diffusion model as a starting point (rather close to one except of two flexible loops). However, during the diffusion model selection s2 values become smaller and both local and global correlation times bigger. I think this could be due to over parameterization, or? I already increased the experimental error by factor 2 but this seemed not to help.
I did not remove the protein tag after purification (TEV site + StrepTag, about 20 residues). Because it is also not part of the used Xray crystal structure, it might induce a bias also I expect it to be highly flexible and move independently from the folded domain. Regards, Jonas -----Original Message----- From: edward.dauver...@gmail.com [mailto:edward.dauver...@gmail.com] On Behalf Of Edward d'Auvergne Sent: Donnerstag, 21. Januar 2016 10:57 To: Jonas Hanske Subject: Re: Confidence in Model-free output Hi, This sounds like a very strange bug! Which version of relax are you using? Looking at the most up to date code: http://www.nmr-relax.com/api/4.0/auto_analyses.dauvergne_protocol-pysrc.html#dAuvergne_protocol.execute (line 718) I can see that the list of diffusion models comes from the list of subdirectories in the results directory: # The contents of the results directory. dir_list = listdir(self.results_dir) The list of data pipes used in model selection, self.pipes, is created by checking the names in dir_list. Therefore I can only guess that this is a file system error (or the directory was accidentally renamed or moved). The analysis will create the local_tm directory - this is an essential first step in this protocol - so it simply cannot find that directory. Did you notice such a problem? Or did you temporarily run out of hard disk space? Do you have a listing of the results directory and can you see the local_tm/ directory there? This is really, really bizarre! As for outputting the diffusion tensor information to a text file, you could maybe run relax with the --tee flag to output all messages to a file, and then load each results file and run diffusion_tensor.display(). Alternatively you can access the object directly with a relax script, and then print out the elements: relax> pipe.create('test', 'mf') relax> diffusion_tensor.init((1.340e7, 1.516e7, 1.691e7, 0.000e7, 0.000e7, 0.000e7), param_types=3) relax> cdp.diff_tensor.Da 2630000.0 relax> print("Da: %s" % cdp.diff_tensor.Da) Da: 2630000.0 relax> Note that the objects in cdp.diff_tensor are automatically generated on initialisation and Python will raise an AttributeError if you ask for a parameter that does not exist for that diffusion tensor type (see the full parameter list at http://www.nmr-relax.com/api/4.0/data_store.diff_tensor-pysrc.html#DiffTensorData ). Regards, Edward On 21 January 2016 at 10:00, Jonas Hanske <jonas.han...@mpikg.mpg.de> wrote: > Hi Edward, > > Thanks for coming back to me. The input data should be fine. Temperature > calibration was performed with MetOH in H2O. I eliminated all overlapping > peaks and fitted the curves with the functions you also provide in relax and > performed consistency testing which worked well with only three outliers. I > eliminated the outliers from the analysis and run your protocol again. > However, I am worried about model selection since the chi2 values are very > close and I am not confident if the selection procedure reflects the true > diffusion model. I calculated the diffusion parameters with HYDRONMR which > gave a ellipsoid model which also came out in the last mf runs. I fed this as > starting point manually to the program and will now perform several manual > runs to see if the results are similar to the automated. > > The local_tm files were always empty in my analyses. > > Is there a quick way to write the diffusion tensor parameters to a .txt > file? I am new to python... > > Regards, > > Jonas -----Original Message----- From: edward.dauver...@gmail.com [mailto:edward.dauver...@gmail.com] On Behalf Of Edward d'Auvergne Sent: Donnerstag, 21. Januar 2016 09:48 To: Jonas Hanske Cc: relax-users@gna.org; Christoph Rademacher Subject: Re: Confidence in Model-free output Hi Jonas, Welcome to the relax mailing lists! From the output, I guess you are using the automated model-free analysis protocol (the one I developed and published). Or are you running a custom analysis protocol? Such results are not uncommon and are usually an indication of a bigger problem. Often this comes back to inadequate temperature calibration and/or control - have a close look at this text I wrote on the subject: http://www.nmr-relax.com/manual/Temperature_control_and_calibration.html Other times it can be due to a bias in the fitting of relaxation curves. Which software did you use to calculate the relaxation rates from the base data? Another reason could be the failure of the diffusion tensor optimisation due to the specific tensor not being an adequate description of reality. This happens if you have large scale internal motions, partial dimerisation (even with 5-10%), long flexible tails, etc. A good test is to use the local_tm global model, which is strangely absent from the above list of global models. The local_tm global model will always be too noisy and give some terrible outlier values, but in general it is a good test for the diffusion tensor approximation. As there is no global diffusion assumed, the local tm values can adapt to the local diffusion contribution. The single value may be a good enough approximate average value for the spread of 3, 5, 10 or more global diffusion coefficients. Therefore if you see the same trend in S2 values in the local_tm models as you do in the oblate spheroid, then you know that the trend is real. Whether that real trend is due to the protein system or due to bad input data is another question. Hopefully this helps. Regards, Edward On 6 January 2016 at 10:14, Jonas Hanske <jonas.han...@mpikg.mpg.de> wrote: > Hi Edward, > > I am a last-year grad student working at a Max Planck Institute in Potsdam, > Germany. My thesis is concerned with a Ca2+ binding protein. I collected full > set of backbone relaxation data of the apo and holo form at two fields (600 > and 750) and saw a slight difference in R2 (about 10% decreased in holo > form), some difference in R1 (slightly increased in holo form), and no change > in NOE. However, after I run model free analysis (using the holo structure > for both apo and holo form since no apo structure is available, all 9 > local_tm models, and all diffusion models), I saw a global tm difference of > more than twofold and tremendous changes in s2 values in both forms with up > to 50% decrease for the holo form. Is this a likely outcome? I do not know if > I should trust model selection especially since the oblate model that was > chosen needed about 200 iterations to converge. I used the Newton minimizing > method. Below, I copied the AIC output from the console and the corresponding > global tm values. > > I'd be happy for any advice! > > Thanks in advance, > > Jonas > > > AIC model selection for apo form > > Global model - all diffusion tensor parameters and spin specific model-free > parameters. > # Data pipe Num_params_(k) > Num_data_sets_(n) Chi2 Criterion > sphere - mf (Wed Dec 30 14:23:37 2015) 197 468 > 526.81736 920.81736 > prolate - mf (Wed Dec 30 14:23:37 2015) 185 468 > 538.26716 908.26716 > oblate - mf (Wed Dec 30 14:23:37 2015) 197 468 > 512.99733 906.99733 > ellipsoid - mf (Wed Dec 30 14:23:37 2015) 197 468 > 518.69107 912.69107 > The model from the data pipe 'oblate - mf (Wed Dec 30 14:23:37 2015)' has > been selected. > > Global tm = 1.5247e-08 s > > > > AIC model selection for holo form > > Global model - all diffusion tensor parameters and spin specific model-free > parameters. > # Data pipe Num_params_(k) > Num_data_sets_(n) Chi2 Criterion > sphere - mf (Tue Jan 5 18:26:27 2016) 206 504 > 2305.05273 2717.05273 > prolate - mf (Tue Jan 5 18:26:27 2016) 209 504 > 2299.94808 2717.94808 > oblate - mf (Tue Jan 5 18:26:27 2016) 209 504 > 2281.81543 2699.81543 > The model from the data pipe 'oblate - mf (Tue Jan 5 18:26:27 2016)' has > been selected. > > Global tm = 6.8584e-09 s > > _______________________________________________ > relax (http://www.nmr-relax.com) > > This is the relax-users mailing list > relax-users@gna.org > > 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 _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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
