Hi, The model selection techniques are independent of the program used. So you can use AIC model selection with both Modelfree4 and relax. Which model selection technique did you use with Modelfree4? Did you use the independent FASTModelfree interface to Modelfree4? This separate program has the hypothesis testing model selection built in and is unavoidable. If you have used hypothesis testing with either program, then the more complex models are disfavoured as under-fitting occurs, and hence this will cause there to be less m5 models selected. Therefore I doubt that model selection is causing the difference between the two programs as you are seeing more m5 using Modelfree4.
The true cause is highly likely to be different diffusion tensors within the two programs and problem of using the isotropic diffusion tensor. Using this tensor when Brownian rotational diffusion is non-isotropic is not good (I can almost guarantee that every protein will be non-isotropic, if proper model selection is used (by proper I mean from the statistical field of model selection)). If a vector points along the long axis of the diffusion tensor, its correlation time will be underestimated and this will result in artificial Rex values will appear (see Tjandra et al., 1996). If the vector is perpendicular then artificial nanosecond motions (model m5 and higher) will appear (see Schurr et al., 1994). The refs are below. As for the te value being on the nanosecond timescale, there is nothing in the theory which stops this! There are no mathematical or physical limits on this. The only limits are statistical - forced by experimental noise - and which vary depending on the exact experimental noise, the amount of data collected, processing bias, etc. The limits can be maybe ~10 ps as a lower bound (this is highly variable and AIC model selection will show you this significance (the Mandel et al., 1995 hypothesis testing significantly increases the lower bound)). The upper bound is approximately the global correlation time, tm in the Lipari and Szabo notation, again purely because of noise. The decoupling approximation has no effect with these bounds, its effect is on the accuracy of the correlation time parameter itself. This is easily tested using synthetic data. Just use an isotropic diffusion with say tm = 10 ns and then create a motion of ts = 2 ns with an S2s value of 0.7 and a fast internal motion with tf = 10 ps and S2f = 0.9. The fast internal motion in this case is insignificant because of experimental noise (add 5% noise, optimise the model-free models, and do model selection (AIC or hypothesis testing)). The S2 value will incorporate the slow and fast order parameters - S2 = S2f*S2s. The tf parameter will not be able to be extracted because of noise. Hence the optimised te parameter will be very close to the ts parameter! This is all mathematical modelling, and there is nothing wrong with that. I hope this description helps in understanding why te has no problem with being on the nanosecond timescale. Regards, Edward Schurr, J. M., H. P. Babcock, and B. S. Fujimoto: 1994, 'A test of the model-free formulas. Effects of anisotropic rotational diffusion and dimerization.'. J. Magn. Reson. B 105(3), 211–224. Tjandra, N., P. Wingfield, S. Stahl, and A. Bax: 1996, 'Anisotropic rotational diffusion of perdeuterated HIV protease from N-15 NMR relaxation measurements at two magnetic'. J. Biomol. NMR 8(3), 273–284. On 8/14/07, Hongyan Li <[EMAIL PROTECTED]> wrote: > Dear Edward, > Sorry for the confusion. I tried to compare the results obtained from > Modelfree4 > and relax using the isotropic model (since I cannot get Modelfree4 works on > axial symetric model at moment). 't-rex-sim.agr' corresponds to results from > Modelfree4 and 't-rex-relax.agr' corresponds to the results from relax. Using > Modelfree4 there are more residues fitted to M5 where using relax, more > residues were fitted by M2 and M4, I supposed that is due to different > criteria > on model selections. However, one thing I don't understand is that te > extracted > from M2 and M4 should be on a scale of several hundreds ps (fast) instead of > several thousands ps (slow). In this regards, Modelfree4 is more resonable and > there seems some problem in terms of model elimination and model selection for > the Relax. > Best wishes, > Hongyan > Quoting Edward d'Auvergne <[EMAIL PROTECTED]>: > > > Hi, > > > > Sorry, I'm not exactly sure what the graphs correspond to. Is > > 't-rex-sim.agr' Modelfree4 using the prolate (or oblate) spheroid > > (this is axially symmetric anisotropic Brownian rotational diffusion)? > > And is 't-rex-relax.agr' the results from relax using the spheroid > > tensor? Have you used constraints on Da in relax to isolate the > > oblate and prolate spheroids? Also how many iterations of the > > model-free optimisation; model elimination; model selection; and > > global minimisation (the optimisation of the model-free parameters of > > all spin systems together with the diffusion parameters) have you > > used? What is the input data and do you have data at more that one > > field strength? > > > > I'll try to answer some of your questions, but without more > > information these may not be the answers you are after. The first > > thing which is a little worrying is that in 't-rex-sim.agr' there are > > many ts values between 6 to 8 nanoseconds. Unless you are working > > with an unfolded protein or a system that is far from globular, this > > is a very strong indication that the diffusion tensor is significantly > > underestimated. How did you determine the initial diffusion tensor in > > the analyses? Did you use the full_analysis.py script when using > > relax (which requires data at minimally 2 field strengths)? The > > errors on the Modelfree ts results are also worrying. This, to me, > > looks like that there has been failures in the MC simulations causing > > very similar errors on all the high ts values. Did you use an upper > > limit of 10 ns in Modelfree? > > > > Another worry is that you obtained similar results from relax using > > the spherical and spheroidal diffusion! How many iterations of > > model-free analysis did you use? And how did you determine the > > initial diffusion tensor? As for the te values in the nanosecond > > range, this is perfectly normal. This is modelling slow internal > > motions. Model m5 was designed for this purpose, but if the fast > > internal motion is close to insignificant due to experimental noise, > > then model m2 is perfectly capable in modelling the slow motion. Also > > if you set the range of the y-axis in all the correlation time graphs > > from 0 to 10 ns, then you can see that the results from Modelfree4 are > > more worrying. For the correlation time results, it is better to make > > two graphs - one for fast motions up to 200 or so picoseconds and one > > for slow motions from 200 ps up. Don't forget that what you are doing > > is modelling. The models don't care what the underlying true dynamics > > are - they will model that motion as best as they can. So classifying > > the dynamics based on which model is selected is at best distracting > > or at worst misleading. It's the results that matter, not the model. > > I hope this answers some of your questions. > > > > Regards, > > > > 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
