Hi, This type of behaviour is to be expected. I have described it in detail in my 2008b paper:
d'Auvergne, E. J. and Gooley, P. R. (2008). Optimisation of NMR dynamic models II. A new methodology for the dual optimisation of the model-free parameters and the Brownian rotational diffusion tensor. J. Biomol. NMR, 40(2), 121-133. (http://dx.doi.org/10.1007/s10858-007-9213-3) Specifically see figure 2. That paper, as well as my review at http://dx.doi.org/10.1007/s10858-006-9007-z, go into full detail about what is happening here. If you have a close look, you'll see that the model-free models for your spin systems will be different at the end of points 3. and 6. What surprises me is how fast your tensor has converged. Normally this takes 5 to 10 iterations of steps 4-6 before the tensor stabilises (see fig 2). Though you could be sitting in a shallow region of the dual optimisation-modelling space and subsequent repetitions of steps 4-6 could cause your tensor to rotate widely before convergence (you might need to read the above two references before this sentence makes any sense). Also, if you only have single field strength data, it can be sometimes difficult to separate the real diffusion tensor from the internal motions (both real and fake). Just try to repeat steps 4-6 and see how many rounds it takes until the chi-squared value is identical (to machine precision) between two rounds. Note that as this is a search through not only the optimisation space but also the modelling space, that there may not be one solution but a repetitive circling around a minima in this dual space - you can see this as the chi2 value repeating itself (identically) every 2nd, 3rd, etc. iteration. I hope this explanation wasn't too complicated. Regards, Edward On 23 January 2012 18:44, V.V. <[email protected]> wrote: > Hi Edward, > > Thank you for the fast (as always) response. > > The order of the calculation is the following: > 1. Model-free fitting using an estimated diffusion tensor with > "mf-multimodel.py". > 2. Model selection with "modsel.py". > 3. Diffusion tensor optimization (first output). Chi2 = 167.466849 > 4. Adjustment of the diffusion tensor in "mf-multimodel.py" to the one in #3. > 5. Model-free fitting as in #1 (second output). > 6. Model selection as in #2. Chi2 = 166.606795 > > I am using the repository version. I am not sure about the exact > revision, but the last update I made was about 2 weeks ago. > > My first guess was just as yours, that the tensors are identical and > the angle difference is just due to symmetry, but it does not seem to > be the case. I can forward the two pdbs if it will help. > > Vitaly > > > On Mon, Jan 23, 2012 at 11:20, Edward d'Auvergne <[email protected]> wrote: >> Hi Vitaly, >> >> Do you mean that if you start with two different starting points, you >> end up with two different tensors? For the different rounds of >> iterations do you mean from the dauvergne_protocol.py script? Are the >> chi-squared values the same in both? There might also have been a fix >> for the diffusion tensor representation in more recent relax versions. >> Are you using the newest 1.3.13 version? Running 'relax -i' will >> give all the version info. There are symmetries in the diffusion >> tensor space, so two {theta, phi} pairs with different values can >> represent exactly the same tensor. Though the different tensors are >> very strange, especially considering that the tm and Dratio values are >> essentially identical! Are you using Modelfree4 as a back end >> optimisation engine? >> >> Regards, >> >> Edward >> >> >> On 23 January 2012 18:03, V.V. <[email protected]> wrote: >>> Dear Edward, >>> >>> I have encountered strange behavior with the initialization of the >>> diffusion tensor. I ran the first round of iterations, ending up with >>> the following oblate tensor: >>> >>> =============================== >>> Alternate parameters {tm, Dratio, theta, phi}. >>> tm (s): 9.486979075650368e-09 >>> Dratio: 0.6141733435119592 >>> theta (rad): 3.64621046835412 >>> phi (rad): 1.9625997908540063 >>> =============================== >>> >>> For the next round of model-free optimization, I have specified these >>> parameters manually: >>> >>> =============================== >>> diffusion_tensor.init((9.486979075650368e-09, 0.6141733435119592, >>> 3.64621046835412, 1.9625997908540063), param_types=2, >>> spheroid_type='oblate', fixed=True) >>> =============================== >>> >>> Yet in this round the diffusion tensor was showing up with different >>> Theta and Phi angles: >>> >>> =============================== >>> Alternate parameters {tm, Dratio, theta, phi}. >>> tm (s): 9.486979075650368e-09 >>> Dratio: 0.6141733435119593 >>> theta (rad): 0.0636383778934639 >>> phi (rad): 0.0342538282493545 >>> =============================== >>> >>> I have generated pdbs of both tensors and they are not identical. Do >>> you have any suggestions what is causing this? >>> >>> Thank you, >>> Vitaly _______________________________________________ relax (http://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
