Yes, with 4 data sets you could remove tm6 to tm8. You would also
need to remove m8. But in this situation, you will be significantly
biasing the initial position (the starting universe will be further
away from that of the universal solution). I don't know how well this
new protocol will perform 4 data sets, i.e. this is untested, but I
would be highly reluctant to trust it. The relaxation data type and
field strength will be very important. I would even be wary using 5
data sets, especially if the missing data set is the higher-field NOE.
So I would never recommend using 4 data sets.
Regards,
Edward
On Jan 10, 2008 8:12 PM, Douglas Kojetin <[EMAIL PROTECTED]> wrote:
> Hi Edward,
>
> Thanks for the response. So, with 5 relaxation data sets, only tm8
> should be removed -- no need to remove m8 as well? Also, if only 4
> relaxation data sets were available, could {tm6-8 and m8} be removed
> to use the full_analysis.py protocol?
>
> Thanks,
> Doug
>
>
>
> On Jan 10, 2008, at 1:31 PM, Edward d'Auvergne wrote:
>
> > Hi,
> >
> > If you have 5 relaxation data sets, you can use the full_analysis.py
> > script but you will need to remove model tm8. This is the only model
> > with 6 parameters and doing the analysis without it might just work
> > (the other tm0 to tm9 models may compensate adequately).
> >
> > I've looked at the script and it seems fine. I think the issue is
> > that the model-free problem is not simply an optimisation issue. It
> > is the simultaneous combination of global optimisation (mathematics)
> > with model selection (statistics). You are not searching for the
> > global minimum in one space, as in a normal optimisation problem, but
> > for the global minimum across and enormous number of spaces
> > simultaneously. I formulated the totality of this problem using set
> > theory here http://www.rsc.org/Publishing/Journals/MB/article.asp?
> > doi=b702202f
> > or in my PhD thesis at
> > http://eprints.infodiv.unimelb.edu.au/archive/00002799/. In your
> > script, the CONV_LOOP flag allows you to automatically loop over many
> > global optimisations. Each iteration of the loop is the mathematical
> > optimisation part. But the entire loop itself allows for the sliding
> > between these different spaces. Note that this is a very, very
> > complex problem involving huge numbers spaces or universes, each of
> > which consists of a large number of dimensions. There was a mistake
> > in my Molecular BioSystems paper in that the number of spaces is
> > really equal to n*m^l where n is the number of diffusion models, m is
> > the number of model-free models (10 if you use m0 to m9), and l is the
> > number of spin systems. So if you have 200 residues, the number of
> > spaces is on the order of 10 to the power of 200. The number of
> > dimensions for this system is on the order of 10^2 to 10^3. So the
> > problem is to find the 'best' minimum in 10^200 spaces, each
> > consisting of 10^2 to 10^3 dimensions (the universal solution or the
> > solution in the universal set). The problem is just a little more
> > complex than most people think!!!
> >
> > So, my opinion of the problem is that the starting position of one of
> > the 2 solutions is not good. In one (or maybe both) you are stuck in
> > the wrong universe (out of billions of billions of billions of
> > billions....). And you can't slide out of that universe using the
> > looping procedure in your script. That's why I designed the new
> > model-free analysis protocol used by the full_analysis.py script
> > (http://www.springerlink.com/content/u170k174t805r344/?
> > p=23cf5337c42e457abe3e5a1aeb38c520&pi=3
> > or the thesis again). The aim of this new protocol is so that you
> > start in a universe much closer to the one with the universal solution
> > that you can ever get with the initial diffusion tensor estimate.
> > Then you can easily slide, in less than 20 iterations, to the
> > universal solution using the looping procedure. For a published
> > example of this type of failure, see the section titled "Failure of
> > the diffusion seeded paradigm" in the previous link to the
> > "Optimisation of NMR dynamic models II" paper.
> >
> > Does this description make sense? Does it answer all your questions?
> >
> > Regards,
> >
> > Edward
> >
> >
> >
> > On Jan 10, 2008 5:49 PM, Douglas Kojetin
> > <[EMAIL PROTECTED]> wrote:
> >> Hi All,
> >>
> >> I am working with five relaxation data sets (r1, r2 and noe at 400
> >> MHz; r1 and r2 and 600 MHz), and therefore cannot use the
> >> full_analysis.py protocol. I have obtained estimates for tm,
> >> Dratio, theta and phi using Art Palmer's quadric_diffusion program.
> >> I modified the full_analysis.py protocol to optimize a prolate tensor
> >> using these estimates (attached file: mod.py). I have performed the
> >> optimization of the prolate tensor using either (1) my original
> >> structure or (2) the same structure rotated and translated by the
> >> quadric_diffusion program. It seems that relax does not converge to
> >> a single global optimum, as different values of tm, Da, theta and phi
> >> are reported.
> >>
> >> Using my original structure:
> >> #tm = 6.00721299718e-09
> >> #Da = 14256303.3975
> >> #theta = 11.127323614211441
> >> #phi = 62.250251959733312
> >>
> >> Using the rotated/translated structure by the quadric_diffusion
> >> program:
> >> #tm = 5.84350638161e-09
> >> #Da = 11626835.475
> >> #theta = 8.4006873071400197
> >> #phi = 113.6068898953142
> >>
> >> The only difference between the two calculations is the orientation
> >> of the input PDB structure file. For another set of five rates
> >> (different protein), there is a >0.3 ns difference in the converged
> >> tm values.
> >>
> >> Is my modified protocol (in mod.py) setup properly? Or is this a
> >> more complex issue in the global optimization? In previous attempts,
> >> I've also noticed that separate runs with differences in the
> >> estimates for Dratio, theta and phi also converge to different
> >> optimized diffusion tensor variables.
> >>
> >> Doug
> >>
> >>
> >> _______________________________________________
> >> relax (http://nmr-relax.com)
> >>
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> >>
> >>
>
>
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