Hi, The first part of your post is simple to respond to as I have used exactly this approach in my PhD thesis. This is described in Chapter 7 of the thesis which can be downloaded from http://eprints.infodiv.unimelb.edu.au/archive/00002799/ . The hybridisation function docstring 'help(run.hybridise)' will explain the rest (otherwise if you could help me in expanding this documentation so that it is fully comprehensible, that would be of great use (those coding are often blind to deficiencies in the code's documentation)). The second part is a little more worrying. The results should have converged well before 30 iterations unless there is something seriously wrong with the model or data. The inclusion of residues for which there is only 3 data sets may be the problem but, from memory, as you are using models with many more parameters than this these should be automatically deselected by relax. There is one scenario that I can theoretically conceive of and this has to do with the full_analysis.py script's attempt at finding the solution within the universal set by incorporating mathematics through the optimisation of the chi-squared function while also optimising the statistical quantity known as the Kullback-Leibler discrepancy. If this makes no sense, my publications (when they are all out) or thesis should explain everything. The scenario is that the dual optimisations are feeding off each other and causing the results to flip-flop between two continually interchanging models. There could simply be one parameter, being close to insignificance, that is appearing and disappearing causing the chi-squared and AIC values to be repeated every two iterations. Simply tabulating the chi-squared value for a number of these iterations should clearly demonstrate this problem. All that being said, this scenario is quite unlikely and something else is more likely to be the problem. Again a list of the chi-squared values for a large number of iterations would be very useful in tracking down the issue. Regards, Edward On 6/24/07, Douglas Kojetin <douglas.kojetin@xxxxxxxxx> wrote:
Hi All, I have two separate but related questions. I am using relax 1.2 (svn version 3301). I have relaxation data collected at two fields -- 500 and 600 MHz. However, I have data for 9 additional residues at 600 MHz that were unresolved at 500 MHz. (1) The protein I am studying has two domains, with considerable interactions between them, connected by a flexible linker. When all data (domains + linker) was included in the calculations, the full_analysis.py protocol picked local_tm for the AIC selection of the diffusion tensor. I would like to analyze my data using a hybrid model: (a) the two domains together (using the same diffusion tensor) and (b) the flexible linker using a separate diffusion tensor (likely local_tm). My guess is that a prolate or oblate tensor will be selected for the domains when analyzed without data from the linker region (the quadric_diffusion program from Art Palmer suggests an axially symmetric tensor is a good approximation). Can anyone provide an example of a script where relax is used to analyze a hybrid model, or briefly outline the steps? For example, should I run a local_tm optimization using all residues, then unselect the flexible linker residues in the unresolved file (as specified in the full_analysis.py script) and continue the optimization of the other tensors (sphere, prolate, oblate and ellipsoid)? (2) I am currently running the full_analysis.py protocol, without the data for the linker region. The optimization of the prolate tensor is taking much longer than the other tensors for this calculation (currently on round_30), as well as the prolate calculation using all data including the linker region (it converged in 14 rounds). The differences in the parameters between rounds are very small: """ ##################### # Convergence tests # ##################### Chi-squared test: chi2 (k-1): 785.88714033105236 chi2 (k): 785.88714033128417 The chi-squared value has not converged. Identical model-free models test: The model-free models have converged. Identical parameter test: Parameter: tm Value (k-1): 6.794068350295769e-09 Value (k): 6.7940683502957698e-09 The diffusion parameters have not converged. Parameter: Da Value (k-1): 6337661.7164024841 Value (k): 6337661.7164041474 The diffusion parameters have not converged. Parameter: theta Value (k-1): 1.6904048161417038 Value (k): 1.6904048161417222 The diffusion parameters have not converged. Parameter: phi Value (k-1): 0.30710640562938446 Value (k): 0.30710640562950142 The diffusion parameters have not converged. """ https://mail.gna.org/public/relax-devel/2007-06/msg00012.html relax does not report a problem for a specific residue, as was reported in the following post (https://mail.gna.org/public/relax- users/2006-12/msg00002.html). Could this be a result of having data at only one field for the 9 residues? Thanks in advance, Doug _______________________________________________ relax (http://nmr-relax.com) This is the relax-users mailing list relax-users@xxxxxxx 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