AW: Random seed and equilibrated structure

From: Norman Geist (
Date: Fri Jul 27 2012 - 01:50:17 CDT



I don't know if this has something to do with your problem, but you should
get the sscache tcl script for vmd and watch how the secondary structure of
the protein behaves to better understand. I often saw that the proteins
still fold back and forth at constant temperature and pressure as they often
run through multiple stable conformations and in repeating cycles. So I
guess you won't see a proteins state, where it will stay like it is and keep
the same equilibrium state. Remember that VMD by default does only compute
the secondary structure information one time, when you choose new cartoon
f.i. or the ss coloring method. VMD will by default not update this
information over the trajectory so you won't see changes of the molecules


Also, for statistical purpose, the replica exchange method could be
interesting for you. I guess it's also possible to just run replicas without
any exchanges, but correct me if I'm wrong.





Von: [] Im Auftrag
von Dr. Eddie
Gesendet: Donnerstag, 26. Juli 2012 23:40
An: Aron Broom
Betreff: Re: namd-l: Random seed and equilibrated structure


Thanks Aron. I guess there is no way to make a small perturbation the an
already equilibrated protein. I want to have good statistics for distance
distributions in the equilibrated protein so having multiple runs helps. But
I guess changing the seed can just thrust me back into a parameter space
where it will likely take just as long to re-reach equilibrium. Right?


I have compared my rmsd to the initial frame as well since it seems to be a
standard approach and does make sense, but I am also curious if others have
a different method. My understanding was this is done because once the rmsd
has leveled off your results are converged. I have noticed that sensitive
things like the distance distribution between two residue alpha-carbons
becomes very narrow once the rmsd of the protein has equilibrated. Prior to
equilibration I get broader and multipeaked distributions.



On Thu, Jul 26, 2012 at 4:07 PM, Aron Broom <> wrote:

I'm not sure if that would show you what you want. If your run has
stabilized, it suggests you are now in some kind of reasonably deep minimum.
Presumably you are thinking of doing multiple runs to make sure that it is
actually the deepest minimum near to your starting point, and if so,
randomizing the seed from your already equilibrated structure may end up
just telling you that you have high walls around that minimum, even if there
are other deeper minima nearby.

Also, in terms of using the RMSD a measure of the simulation stabilizing, do
you just plot the RMSD as compared to the first frame? That is generally
what I've done, but I wonder if others have different approaches? The big
problem with that is the ambiguity of the RMSD, that is, if the RMSD levels
off at 3A, it means that two structures in the last part of your simulation
are different from the starting structure by 3A, but they could be more
different than that from one another. I've tried using VMD to do an RMSD
versus lag time or something, which would be a kind of fluctuation measure,
and found that could be more sensitive. I'd like to know what others do
with this, maybe there is something obvious I've missed?



On Thu, Jul 26, 2012 at 4:55 PM, Dr. Eddie <> wrote:

Hi all,

I've been simulating a protein with NAMD using constant pressure and
temperature and PME. It takes anywhere from 10-24 ns for the rmsd of the
protein's backbone to level-off. I'd like to have multiple runs to ensure
good statistics but that overhead is debilitating. Would I be able to simply
continue a run that has stabilized (after, say 30 ns) using different random
seeds to get the same result as having multiple runs start with different
random seeds and each running for 30+ ns?

Has anyone tried this? I'd be interested in success or failure since my time
scales are too long to try it myself.



Aron Broom M.Sc
PhD Student
Department of Chemistry
University of Waterloo

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