Diffusion model Round Machine Time Time_for_opt/ =============== ===== ============= ======= ============= local_tm --- Pelican 15h00 -------------------------------------------------------------------- sphere MII init Pelican 0h10 1 Pelican 42h00 31h00 2 Pelican 23h00 21h00 3 Pelican 18h00 16h00 4 Pelicanprolate MIII init Hibou 0h20 1 Hibou 76h00 74h00 2 Hibou 44h00 39h00 3 Hibou oblate MIV init Hibou 0h20 1 Hibou 76h00 75h00 2 Hibou 44h00 40h00 3 Hibou ellipsoid MV init Pigeon 1h00 1 Pigeon 75h00 74h00 2 Pigeon 31h00 27h00 3 Pigeon 20h00 17h00 4 Pigeon --------------------------------------------------------------------
Those times are quite long! They're not completely unreasonable considering you have 220 residues and data at 3 field strengths.
So, it seems the behaviour is normal with the processing times getting shorter as the optimization proceeds... Maybe the first runs were long because of my errors on the NOEs being too small (I think they could be too underestimated because, with the traditional approach used in ModelFree -- the approach from Mandel et al., 1995 -- I can select only ~20% of residues for model 1 (and almost no residues in subsequent models) under an axially symmetric diffusion tensor...
That percentage of model m1 being selected is quite normal, maybe just a little too high because of the deliberate under-fitting of that ANOVA/hypothesis testing approach. The underestimation of the R1, R2, and NOE errors could however explain the long calculation times. The more the errors are underestimated, the more convoluted and difficult the model-free space is to optimise in. Are you experimentally determining the NOE errors by measuring the base plane noise or by running both the saturated and reference NOE spectra twice? I would value the errors of equal or greater importance than the relaxation data itself! Under or overestimation of the errors will influence both optimisation and model selection, and hence the final dynamic picture of the molecule. High quality errors are very important.
Also, the fitted values seem reasonable.
That's a good sign. Despite the amount of time this is taking, you may end up with good results, even if it does take two weeks. Unless there is something subtly wrong causing the slow down. Have you modified the 'full_analysis.py' script much? Are you sure your relaxation data is being read in correctly with the correct field strength values? How did you determine your R1 and R2 values? Were the R1 and R2 errors determined using Monte Carlo simulations?
Anyway, it seems I'll get optimized models soon and then I could look at them and maybe exclude models 6 to 9 if I find them irrelevant... Let's see !
I would strongly recommend in keeping those models! You have the data to fully characterise these and dropping them will induce under-fitting if you have a system which exhibits large interesting motions. It doesn't seem like this is the slow point anyway.
A question, however... Is it normal that the X2 in the opt directories as the same value for every residue (for example in the file ellipsoid/round_3/opt/results.bz2) ? Is it the global X2 ?
Yes. The 'opt' directory is the last part of the new protocol where the global model - the diffusion model together with all model-free models of all residues - is optimised. It is a single optimisation of the single global chi-squared value. Edward _______________________________________________ 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
