Questions and problems?
Energy minimization
Does the parameter NSNB (number of steps between two
updates of the nonbonded list) influence the energy
gradient? For me it looks as if the energy gradient
is reset. If I set NSNB equal to MAXCYC (Maximum number of
cycles) the minimization is successful, but if I keep
NSNB to the default value of 25 the minimizations ends up
in an endless loop with no futher progress in the rms value
of the energy gradient.
Every time the nonbonded list is updated, the set of interacting
atom pairs is likely to change, which can set the minimization off
in a different 'direction'. It is even logically possible that a
circular path could occur in this case, such that minimization would
never terminate, although it is far more likely that the molecule
would wander around indefinitely in a more-or-less infinitesmal space.
One approach would be to set a step limit on the minimization - 100-500
is usually sufficient to prepare for running MD. Another solution
(especially appropriate for fine-grain minimization for normal mode
calculation) would be to set the cutoff to include the entire system
and NSNB=9999999.
Bill Ross
My goal is to find a very low
gradient accurately on a large system without allowing the system to
collapse on itself due to the large number of steps required using
conjugate gradient methods. I have run the structure thru 600 steps of
steepest descent followed by 1000 steps of conjugate gradient all the while
keeping the backbone atoms completely constrained. After releasing the
backbones I ran an additional 1000 steps of conjugate gradient. This
results in a structure that has not collapsed very much but has a
reasonable amount of relaxation from the initial hand built conformation.
- You need to think hard about why you need a "very low gradient",
but I'll leave that to you. (Normal mode analysis is about
the only thing I can think of that really requires it.)
- What potential model you need to use to find a true minimum that
does not have collapsed helices (or whatever) is completely orthogonal
to the question of whether to use CG or NR to get there. Current
potential functions were designed primarily for MD simulations in water;
they typically do not have true minima that are close to X-ray structures
(where the latter exist). Example: even for a simple protein like
crambin, I have never been able to find a true minimum vaccum structure
that is closer than about 0.7 Ang. backbone RMS to the X-ray structure.
Yet crystal simulations at finite temperature (including the waters
present in the crystal structure) stay within about 0.3-0.4 Ang. of the
X-ray result.
- Once you have a potential model, what is the best way to find a true
minimum? As you have found out [using nmode], NR is generally not even
possible for
a large system. CG minimization (probably using the nmode algorithm)
is the best I know about. Plan to run 20,000 cycles or more, freezing
the non-bonded list at some point, so that non-bonded updates don't provide
"noise" to the energy function. The system may well drift further from
the starting structure than you would like, but it should be no different
with NR (assuming you had enough memory to get the latter to run.) CG
methods can get you to RMS gradients of 10**-6 or better, but you do have
to be patient.
Dave Case
usig nmode ... minimizing from the initial hand built conformation
You are using a minimizer designed
to grind small molecules to the absolute lowest energies
for a huge, hand-built model. This is like trying to clean
Brooklyn Bridge with a toothbrush. To make such a model
reasonable, it is way way better in my experience to run
md in the range of 1-100K, possibly using belly or restraints
to keep structure in the parts you trust more.
Bill Ross