2009/12/14 Tiago Pais <tpais@xxxxxxxxxxx>:
I am not really sure I am following you. Let's see and please correct me if and where I may be mistaken. To optimize de diffusion tensor model I fix the modelfree models (m0-m9) and let the diffusion tensor be optimized. I have to questions with regard to this: 1- Does this optimization tells you if it is isotropic, axially symmetric or fully anisotropic? Where?
This will be what you set it to earlier with the diffusion_tensor.init() user function. Optimisation doesn't tell you this, but from the number of parameters minimised you can tell: 1 - sphere 4 - spheroid 6 - ellipsoid
2- Is there an exclusion of spins for tensor optimizations during this procedure, i.e., are all spins used to optimize the diffusion tensor? Or should I run this optimization using only a subset of residues that comply with the criterion of reduced mobility?
All selected spins are used. All deselected spins are excluded. However prior to optimisation, spins with no data, not enough data, etc. are deselected (now you will get a RelaxWarning explaining the problem). For optimising a diffusion tensor during a model-free analysis, all possible spins should be used. The exclusion of spins is only for getting the diffusion tensor from the R1/R2 ratio (from Kay et al., 1989). However this R1/R2 approach for proteins is problematic. E.g. in cases where the NH vectors of certain secondary structure elements point along the long axis of the protein, the high R2s might be mistaken as mobility and excluded. Then the anisotropy of the tensor is underestimated and the repetitive optimisation may not be able to slide to the correct solution. This catastrophic failure is what I showed with the Olfactory Marker Protein (OMP) in my PhD thesis (downloadable as PDF at http://www.nmr-relax.com/refs.html#dAuvergne06, and also http://www.amazon.com/Protein-Dynamics-Model-free-Analysis-Relaxation/dp/3639057627/ref=sr_1_1?ie=UTF8&s=books&qid=1260815221&sr=8-1) and also published in my second relax paper (http://www.nmr-relax.com/refs.html#dAuvergneGooley08b). The R1/R2 ratio also experiences catastrophic failure when there is a large amount of mobility in the system. This was demonstrated by Orehkov et al, 1999 with the Bacteriorhodopsin fragment (1-36)BR. This is again in my thesis in section 6.4.4, and http://www.nmr-relax.com/refs.html#dAuvergneGooley08a. This is the reason I developed the new protocol used in the full_analysis.py script. Specifically to avoid these huge analysis failures, but also to remove the mess that is the initial diffusion tensor estimate. In model-free analysis there is a chicken and egg question. Do you start the with the diffusion tensor or start with the internal motion? Starting with both is not physically possible due to the fact that you are searching for the best solution across 4*n^10 models, where 4 are the 4 diffusion tensor types, n is the number of spins analysed, and 10 is the number of model-free models used. This is a massive number of spaces to search through, and the number of dimensions in each individual optimisation space can also be huge! So you have a problem whereby you are searching the solution not only within one optimisation space, but across incredible numbers of optimisation spaces. It is a joint optimisation and model-selection problem. I've described this all in far more detail in my Mol. Biosyst. paper (http://www.nmr-relax.com/refs.html#dAuvergneGooley07). With this new protocol, I have reversed the concept. Rather than starting with the diffusion tensor, here you are starting with the internal motions. This however required multiple field strength data, but to avoid the catastrophic failures, you also have to have multiple field strength data. My 2007 and two 2008 papers (or the thesis) explains this much better and how this protocol in the full_analysis.py script tries to find the universal solution (the one solution in the universal set of all 4*n^10 spaces) in a more rigorous way than is currently done in the NMR field.
Sorry for all the trouble before you leave for holydays.
Not a problem. I hope this long email helped. Regards, Edward