Hi again. On Mon, 2008-03-31 at 11:28, Edward d'Auvergne wrote: > Hi, > > Please see below for my replies. > > On Fri, Mar 28, 2008 at 2:59 PM, Carl Diehl <[EMAIL PROTECTED]> wrote: > > Hi. > > I used the full_analysis.py script for testing and evaluating relax in > > comparison with published data. > > > > The system was calcium-loaded Calbindin D9k, with R1 & NOE at 600 MHz > > and R2, R1 & NOE at 500 MHz. The relaxation data is of very high > > quality. The model selection was done using home-written software, so no > > ordinary model selection à Modelfree. > > What do you mean by model selection? Did you use a different > technique from the statistical field of mathematical modelling and > model selection? Did you use a frequentist method, a Bayesian method, > or hypothesis testing methods (the last of which is described in > textbooks from this field of knowledge as being very, very bad)? Did > you use this to select between model-free models, diffusion models, or > the combined global model (model-free + diffusion)? > > To clarify, in the example above I've only used published relaxation values for D9k (Johan Kördel et al, Biochemistry 1992, 31, 4856-4866). This is a pretty old article and I'm mainly using the data for comparing and validating relax vs Modelfree. In other words none of the original model selection was done by me. The diffusion model was isotropic. The model selection was done by first optimising the global sum of squares vs model m2. Residues with a R2/R1 ratio below the average value were optimised with model m5.
> > After running full_analysis.py (removing excess models), > > By 'removing excess models', do you mean eliminating failed models? > What is an excess of models? Models with more parameters than the number of experimental values. MF_MODELS = ['m0', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6', 'm7', 'm8', 'm9'] LOCAL_TM_MODELS = ['tm0', 'tm1', 'tm2', 'tm3', 'tm4', 'tm5', 'tm6', 'tm7', 'tm9'] > > model selection > > gives me local_tm as the best model for the diffusion tensor. >Previous > > calculations (both 15N and 13C relaxation data) indicates that the > > diffusion tensor is to a good approximation, isotropic (only a very > > slight anistropy). > What does 'good', 'slight', etc. really mean? For a mathematical > modelling perspective, I don't understand these. Is a Da of > 1.001+/-0.001 slight, or 1.2+/-0.01, or 1.6+/-0.3? From my > experience, my opinion is that unless all the bond vectors point in > the same direction, isotropy will never be statistically significant > over the spheroids and ellipsoids. Dpar/Dper = 1.08 +/- 0.001. The axially symmetric diffusion model is statistically significant compared to the isotropic model as you say (after digging up the paper). An anistropy of 1.08 gives only a small effect on the relaxation rates (R1 +/- 0.05, ~R2 +/- ~0.25, NOE +/- ~0.01), an isotropic diffusion tensor is therefore a good approximation for most calculations. > > Looking at the local_tm values for the secondary structure, most of them > > have a local_tm which is similar to the isotropic tensor. > > If you used AIC or BIC model selection between the two models, what > are the chi-squared values, criteria values, and parameter numbers for > each model? In the test, did you compare the local_tm model to the > isotropic model? Or did you compare the local_tm model to the > isotropic, 2 spheroids, and ellipsoid simultaneously? Again, what is > the qualifier most? And how do the non-conforming residues not > conform? > I used the full_analysis.py script and ran all diffusion models until convergence according to protocol within the script. I will have to get back to you with the exact values of the model selection. > > > Is the local_tm model always correct? For a well folded protein, one > > would expect that the local_tm model should be invalid? > > As this is mathematical modelling, there is no such thing as an > invalid model. By definition of the term model, a model is an > approximation of something far more complex. Therefore there is only > a grey scale of how good a model approximates reality (of course we > can never know what is reality). For a folded, single domain, > globular protein (with no significant, floppy loops), then the sphere, > spheroids, or ellipsoid should be a good description and the local_tm > model will not be selected. Could you reproduce the model selection > statistics for all 4? If the local_tm model is chosen, then it is an > indication that something is not normal - quite possibly interesting > dynamics. Unfortunately, there's not enough information in your post > for me to tell you exactly what happened. One point that concerns me > is that you only have an R2 measurement at a single field strength. > Note that to differentiate between chemical exchange effects (which > are scaled quadratically with field strength) and internal nanosecond > motions (constant at different fields) and anisotropic tumbling of the > molecule (again constant), you really need the R2 collected at 2 > fields. But this may not necessarily be the reason you're seeing the > local_tm model being selected. I'm sorry that I am not yet able to > give you a clear answer. > Chemical exchange is not an issue for D9k. D9k has floppy ends and a loop in the middle, the rest of the sequence is rigid (S2 ~ 0.8-0.9). Instead of measuring R2 at two fields, one option is to measure 1H-15N dipolar/CSA transverse and longitudinal crossrelaxation and extract R2 without chemical exchange. The Kay group also published an article recently which allows measurements of R2 without chemical exchange. BTW, is it possible to incorporate the above types of relaxation rates as another experimental value to fit against. Regards -- Carl Diehl Department of Biophysical Chemistry Lund University 046-2220384 _______________________________________________ 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
