Thank you, Edward. I will re-read the papers you mention to see if I misunderstood something. My confusion is mostly about the fixed parameters. Please correct me if I am wrong, but I thought that if the diffusion tensor is initialized manually and is set as "fixed", then the values in the output should be the same as entered.
The protein I work on has a symmetry axis, and the initial diffusion tensor estimation seemed to work quite well. I had mixed results with the "dauvergne_protocol.py", as in some instances the spheroid tensors were converging with |Dratio| of 4e-3, which did not make a lot of sense. The results also tended to oscillate extensively and in some cases the calculations lasted through ~120 rounds. I was under the impression that the tensor convergence was an issue and this was the main reason for reverting to the individual scripts. I use the data at three fields (R1, R2, NOE for two fields and R1, NOE for the third). Vitaly On Mon, Jan 23, 2012 at 12:05, Edward d'Auvergne <[email protected]> wrote: > 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
