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)
>>
>> 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
>>
>>
_______________________________________________
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
