Salut Edward ! I remember your poster in Goettingen as I brought a reprint home...
That answer is totally what I needed ! Thanks Sébastien Edward d'Auvergne wrote: > Hi, > > That is good question. I have to warn you though that my opinion is > going to be very heavily biased! Essentially the way that model-free > analysis has been implemented over the last 17 years or so (since the > publication of Kay et al., 1989) is as follows: > > 1. Estimate the Brownian rotational diffusion tensor. > 2. Hold the diffusion tensor fixed and optimise each model-free model. > 3. Model-free model selection (in my opinion this is best done using > AIC model selection ;). > 4. Optimisation of the diffusion tensor parameters together with the > parameters of the selected model-free model. > 5. Repeat the steps, using the final optimised diffusion tensor as > the starting point of the next iteration, until 'convergence'. > > On top of this I have recently proposed an additional step prior to > 'model-free model selection' called 'model-free model elimination' to > remove failed model-free models. The most common way of carrying out > step 1 is to use the R2/R1 ratio (Kay et al., 1989). relax can not > only implement this data analysis chain but, due to it's modularity > and flexibility, it can also implement many of the different published > variations to this approach. > > There is a sample script called 'full_analysis.py' distributed with > relax which implements a radically different approach to Kay's > paradigm. Rather than starting with the diffusion tensor and ending > with the model-free parameters, this new model-free optimisation > protocol applies this logic in reverse. It starts by optimising the > model-free models and finishes by optimising the diffusion tensor. > The benefits of this approach is that it avoids the pitfalls of > obtaining the initial diffusion tensor estimate, avoids the hidden > motion problem (Orekhov et al., 1995, Orekhov et al., 1999a, Orekhov > et al., 1999b), and avoids under-fitting (which causes artificial > motions to appear). > > I presented this new protocol on a poster at the 2006 ICMRBS > conference in Goettingen and I currently have a number of submitted > manuscripts which, unfortunately, are not published yet. These papers > will demonstrate the application and performance of the new model-free > optimisation protocol. However all the steps of the analysis are > described in fine detail at the start of the 'full_analysis.py' > script. > > Sorry about all that biased, unpublished opinion. In summary relax > can be used to implement most of the data analysis protocols in the > literature. I hope that answers your question. > > Edward > > > References: > Kay, L. E., Torchia, D. A., and Bax, A. (1989) Biochem. 28(23), > 8972-8979. > Orekhov, V. Y., Korzhnev, D. M., Diercks, T., Kessler, H., and > Arseniev, A. S. (1999a) J. Biomol. NMR 14(4), 345-356. > Orekhov, V. Y., Korzhnev, D. M., Pervushin, K. V., Hoffmann, E., and > Arseniev, A. S. (1999b) J. Biomol. Struct. Dyn. 17(1), 157-174. > Orekhov, V. Y., Pervushin, K. V., Korzhnev, D. M., and Arseniev, A. S. > (1995) J. Biomol. NMR 17(1), 157-174. > > > > On 10/5/06, Sebastien Morin <[EMAIL PROTECTED]> wrote: >> Hi ! >> >> I have a question about the diffusion tensor and the global correlation >> time. >> >> Palmer proposes to estimate the diffusion tensor and global correlation >> tensor as what follows : >> >> 1. Use pdbinertia with the 3D structure to get the moments of inertia. >> >> 2. Use r2r1_diffusion with the R2/R1 values and 3D structure to estimate >> the diffusion tensor type and values (isotropic, axial, anisotropic, >> Diso, Dpar, Dper, etc) and associated global correlation time (tm). >> >> 3. Confirm these values obtained by r2r1_diffusion with quadric using >> local correlation times obtained with r2r1_tm. >> >> When one possesses estimated values for his molecule, the next step is >> to use Model-Free with those values and select the models. At the end, a >> global optimization is performed (the diffusion tensor and the global >> correlation time are then optimized)... >> >> =========== >> >> What is the best way to estimate (and optimize) the diffusion tensor and >> global correlation time using the relax approach ? >> >> Thanks for helping me getting started with this promising program ! >> >> >> Séb >> >> >> >> -- >> >> ______________________________________ >> _______________________________________________ >> | | >> || Sebastien Morin || >> ||| Etudiant au doctorat en biochimie ||| >> |||| Laboratoire de resonance magnetique nucleaire |||| >> ||||| Dr Stephane Gagne ||||| >> |||| CREFSIP (Universite Laval) |||| >> ||| 1-418-656-2131 poste 4530 ||| >> || [EMAIL PROTECTED] || >> |_______________________________________________| >> ______________________________________ >> >> >> >> _______________________________________________ >> 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 >> > -- ______________________________________ _______________________________________________ | | || Sebastien Morin || ||| Etudiant au doctorat en biochimie ||| |||| Laboratoire de resonance magnetique nucleaire |||| ||||| Dr Stephane Gagne ||||| |||| CREFSIP (Universite Laval) |||| ||| 1-418-656-2131 poste 4530 ||| || [EMAIL PROTECTED] || |_______________________________________________| ______________________________________ _______________________________________________ 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
