On Feb 17, 2008 7:47 PM, Sebastien Morin <[EMAIL PROTECTED]> wrote: > Hi, > > I recently used the full_analysis.py script. > > In the final run, I wonder if it would be useful to make MC simulations > on the diffusion tensor parameters. Are errors on the diffusion tensor > parameters useful ?
They are, but it takes a long time. relax's more accurate and significantly higher precision optimisation, compared to Modelfree and Dasha, means this takes a long time. Especially when this is combined with the phenomenon of model and MC sim failure (the paper on Model-free Model Elimination is published in JBNMR). You can give it a go, but it could take forever - literally. There is an ancient post on this, lets see if I can dig it out. Ok, this is from Chris MacRaild, way back in 2005. Well before the mailing lists. So I have cut and paste the relevant part of my response, it's located at the end of this message. My model elimination paper has a very good description of this problem too. > I ask this question because I used to work with ModelFree (Palmer) and > this program output errors on every parameter, including the diffusion > tensor... > > Also, I realized that simulations were being done on the bond length and > CSA. Is this useful ? Why ? If the bond length or CSA parameters are part of the model, which is not the case the full_analysis.py script or if you are doing a normal analysis, then these will be treated as all model parameters and will be part of the simulation process (just as it was in the fitting). This is a hidden feature of relax. Someone adventurous could be the first in the field to play with these model-free 'constants' converted to variables, but it will require heavy testing. Synthetic test models are an absolute must! Maybe one day in a future post-doc or beyond, I'll get around to this. > What's your opinion about MC simulations on the diffusion tensor > parameters as long as the bond length and CSA in the final run ? MC simulations of the diffusion tensor will require Gary Thompson's multi processor code for running this on a cluster. Otherwise it will likely be impossibly slow (unless you crank down the accuracy and precision to that of Modelfree or Dasha (I wouldn't recommend this as it will be far worse than not simulating the tensor parameters)). The bond length and CSA values are constant in the models you are using, so they are unaffected by the simulations. Regards, Edward Here is part of the ancient message: ----- On 08 Sep 2005 17:37:09 +0000, Chris MacRaild wrote: > > On Thu, 2005-09-08 at 08:36, Edward d'Auvergne wrote: > > Hi, > > > > That's a tough question to answer. I'm not exactly sure what you are > > trying to do, but is it that you would like to constrain the longest > > of the three axes of the diffusion tensor to a general position in > > space where logic would suggest it should be? > > > Not quite. The homodimer has a two-fold axis of rotational symmetry, so > one axis of an asymmetric diffusion tensor must be co-linear with that > molecular symmetry axis. This need not be the longest axis - it could be > any of the three. This is an absolute physical constraint, and what we > are trying to do is minimise the space over which we search for the > diffusion tensor based on this constraint. > > > If so, does > > minimisation of the prolate or fully anisotropic tensors not return > > reasonable results? Or do they never converge? > > Based on T1/T2 ratios, we are fairly sure that a fully anisotropic > tensor is required, and the tensor that comes out of that analysis is > consistent with what we expect (ie. one axis lies along the molecular > symmetry axis). An unconstrained analysis of the fully anisotropic > tensor is simply too slow in relax, so I don't know if relax would also > converge on a reasonable result without constaints. Adding the > constraint will make the analysis much faster, so thats a major reason > why we are so keen to impliment it. I think the problem is in the Monte Carlo simulations. The way it is set up in the script is that for each simulation, the global model (diffusion tensor with all model-free parameters for all residues simultaneously) is optimised. This is probably not the best idea though. Although you won't get diffusion tensor errors, if you minimised each residue separately with the diffusion tensor fixed, you will probably get better results (and it will be orders of magnitude faster). The reason is because some simulations fail and need to be removed. Remember how I told you that certain models fail which is why the script implements the eliminate function before model select, well simulations also fail. The current version of relax has the elimination of simulations implemented. I'm about to send off my paper on model elimination which talks in depth about this, if you are interested in reading a pre-press copy. The only downside of having the diffusion tensor fixed is the bias introduced - although every single analysis in existence today has this bias so no one will notice. The result is a slight underestimation of the errors - although I don't know the techniques required to fix this problem. If computation time is the only issue, just add the appropriate commands to the script to fix the diffusion tensor prior to the Monte Carlo simulations, and all will be okay. _______________________________________________ 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
