Hi Ed,
First, there were some bad assignments in my data set. I used the automatic
assignment (which takes an assigned peak list and propagates it to other
peak lists) procedure within NMRPipe for the first time and some peaks were
badly assigned.
Second, the PDB file is quite good as it is a representative conformation
from a 60 ns MD simulation using CHARMM. That said, the protein moves in the
simulation and, hence, the orientations also change. I could take another
conformation, which is what I'll do to cross-validate my models, but
nevertheless the orientations will change and subtil changes will appear.
This shouldn't be an issue since the vectors that move a lot in the
simulations should have correlating relaxation properties and that should be
seen in the models chosen.
Third, here are the stats for the ellipsoid optimization :
round t_total_(h) t_opt_(h) iter_opt model_change tm a b
g chi2 comments
===== =========== ========= ======== ============ ====== ==== =====
==== ================== =======================
1 146 144 207 --- 12.423 18.8 159.7
99.1 9282.2280010132217 ok
2 49 47 62 215 12.463 74.7 152.0
94.3 8793.0777454789404 ok
3 16 14 19 16 12.448 78.0 152.3
96.9 8767.5325004348124 ok
4 12 10 13 1 12.445 80.2 151.9
97.9 8765.5659442063006 ok
5 19 17 23 2 12.445 83.1 151.7
98.3 8761.0001889287214 ok
6 25 23 27 1 12.452 80.9 151.4
96.2 8744.6870170285692 ok
7 16 14 19 1 12.445 83.1 151.7
98.3 8761.0001889287269 almost_5
8 25 23 28 1 12.452 80.9 151.4
96.2 8744.6870170285729 almost_6
9 14 12 17 1 12.445 83.1 151.7
98.3 8761.0001889287269 almost_5_and_exactly_7
10 29 27 33 1 12.452 80.9 151.4
96.2 8744.6870170285656 almost_6_and_8
11 stopped...................................
As you can see, there is a kind of interchange between two runs in the end
of the optimization. In fact, from the iteration 5 on, there is only one
residue for which the model is changing, it's always the same. It changes
from model 5 to 6 and 6 to 5... with a tf of ~17, a ts of ~25000 and a S2 of
~0.73 (chi2 ~40 in aic file, but then with ts ~ 1200) when with model 6 and
ts of ~650 and S2 of ~0.78 when with model 5 (chi2 ~50 in aic file)
. How come a so high ts (25000) isn't eliminated..?
round AIC_or_OPT model S2 S2f S2s tf ts chi2
===== ========== ===== === ==== ==== ====== ====== =========
9 AIC 5 0.78 0.96 0.81 None 698 52
10 AIC 6 0.78 0.97 0.80 11.2 1173 39
9 OPT 5 0.78 0.96 0.81 None 630 ---
10 OPT 6 0.73 0.93 0.79 16.8 24904 ---
Fourth, the previous runs were made on 4 different computers which give
almost exactly the same calculation time, maybe differing from 10-15 %...
This shouldn't be what's causing those so extremely long times...
Fifth, I used the default algorithm whithin the full_analysis.py script. How
can I change the optimization algorithm so it's a two stage procedure like
you proposed ? Should I run several times with MIN_ALGOR = 'simplex' and,
after a few runs (maybe when the chi2 and number of iterations get to a
plateau) switch to MIN_ALGOR = 'newton' ?
I think that's almost everything I can find now...
Let me know if you know how to catch those problems before they appear...
Cheers
Séb :)
Edward d'Auvergne wrote:
Hi,
I've been trying to think of what could possibly be causing these
really long times, but I'm really not sure what is happening.
Unfortunately there just was not enough information in the post to
decipher the key to this problem. Is there something special about
those 7 residues? How accurate do you think their orientations are in
the PDB file you are using? And how accurate is the PDB file itself
with respect to all parts of the system?
Have you had a chance to investigate further as to what the issue
might be? For example, which part of the calculation is taking the
time? Is it the global optimisation of all parameters? Are the final
results of each round similar or completely different (selected model
wise and parameter value wise). How do the iteration numbers compare
at each stage. Essentially a fine analysis and comparison of the
results files and the printout from relax will be necessary to track
down this abnormal computation time. Oh, are you running these on the
same computer as the previous analysis?
As for the optimisation algorithm being stuck, if you've used the
default algorithm then this shouldn't happen. Optimisation should
terminate. There are certain very rare situations where the algorithm
known as the GMW Hessian modification, which is used by default as a
subalgorithm by the Newton algorithm in relax, can take large amounts
of time to complete. You'll see this as a increase in the number of
iterations by 4 to 5 orders of magnitude. One way to test this is to
use a lower quality optimisation algorithm first and then complete to
high precision with the Newton algorithm. In this case I would use
simplex first followed by the default Newton algorithm and its default
subalgorithms. In all cases constraints should be used. This will
only solve the long computation times if the GMW algorithm is at
fault.
Regards,
Edward
On 9/4/07, Sebastien Morin<sebastien.morin.1@xxxxxxxxx> wrote:
Hi all,
I am using the full_analysis.py script with data a three magnetic fields.
After a first complete cycle (going through the final optimization), I
realized that a few residues had extremely high chi-squared values (>
1000) no matter the diffusion model or model-free model chosen...
So I removed those residues (7 out of 222) and started the full_analysis
protocole again.
However, the optimization times are now extremely long and I should get
the final results in weeks...
Here are the available times (for local_tm, sphere and ellipsoid) :
Diffusion_model Round Time-before_N=222 X2
Time-now_N=215 X2
=============== ===== ================= =======
============== =======
local_tm --- 12h30 45949
14h30 5802 OK, X2 much smaller
sphere init --- 1154338 ---
249255
1 2h30 65654 36h00
10303 Long, but X2 much smaller
2 2h30 65654> 30h00
ellipsoid init --- 753535
--- 177764
1 4h00 64592>
67h00 ??
2 2h30 64592
not_there_yet
Is it possible that the algorithms get stuck somewhere during the
optimization..?
I thought that removing badly fit residues would, on the contrary, speed
up calculations...
Thanks for ideas !
Sébastien :)
--
______________________________________
_______________________________________________
| |
|| Sebastien Morin ||
||| Etudiant au PhD en biochimie |||
|||| Laboratoire de resonance magnetique nucleaire ||||
||||| Dr Stephane Gagne |||||
|||| CREFSIP (Universite Laval, Quebec, CANADA) ||||
||| 1-418-656-2131 #4530 |||
|| ||
|_______________________________________________|
______________________________________
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______________________________________
_______________________________________________
| |
|| Sebastien Morin ||
||| Etudiant au PhD en biochimie |||
|||| Laboratoire de resonance magnetique nucleaire ||||
||||| Dr Stephane Gagne |||||
|||| CREFSIP (Universite Laval, Quebec, CANADA) ||||
||| 1-418-656-2131 #4530 |||
|| ||
|_______________________________________________|
______________________________________
Re: Extremely long optimization times