Hi,
Note that wavelet analysis is still fairly new in cpptraj, so it is
still fairly uncharted territory - there's going to be a lot of
experimentation involved. Your instinct to start with a smaller number
of frames is a good one!
On Tue, Apr 24, 2018 at 1:42 PM, Moataz Noureddine
<moataz.noureddine.gmail.com> wrote:
>
> parm structure_stripped.parm7
> trajin combined_stripped_aligned100ns.nc
> center :1-160 mass origin
> image origin center
> rms first *
You may want to try just using 'autoimage' instead of image/center.
Also, if you're going to be doing several rounds of wavelet analysis
it makes sense to create a stripped, imaged, and rms-fit trajectory
(i.e. combined_stripped_aligned100ns.nc should be stripped, imaged,
and fit) so you don't have to constantly image/fit.
>
> wavelet nb 54 s0 0.01 ds 0.25 correction 1.01 chival 1.6094 type morlet out
> wavelet_stripped100ns.gnu usemap
>
> First, do those seem like reasonable parameters for our analysis?
The value of s0 seems too small here to me. The parameters for wavelet
analysis can be a little tricky (as you're finding out). Essentially,
the time scales you get out depend on the time scale of the frames
going in. If the input frames are 1 ps apart, an initial scale value
(s0) of 1 means the wavelet will be 1 ps. The smallest value you
should choose is 2*delta, where delta is the time between your
snapshots. So if your snapshots are 1 ps apart, you should choose an
initial scale value of 2. The number of scaling values (nb) will
determine the max scaling value (s0*2^nb*ds), which in turn determines
the max length of "significant" motion that will be detected. In your
case, your scales range from 0.01 to about 116 - assuming your
trajectory save frequency is on the order of ps that would explain why
the resulting wavelet plots look so flat. You'll probably want to run
the wavelet analysis several times with a higher s0 and increasing
values of nb to reveal different timescale motions.
If you haven't already, you may want to check out this paper:
https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00727, which discusses
the implementation of wavelet analysis in cpptraj, and some of the
references therein (e.g.
https://www.worldscientific.com/doi/abs/10.1142/S0219691312500403)
which discuss application of wavelet analysis to biological systems.
Also note that the wavelet analysis code is OpenMP-parallelized, so
you'll get a speedup using cpptraj.OMP (up to the number of real cores
on your system, no hyperthreading).
Hope this helps,
-Dan
--
-------------------------
Daniel R. Roe
Laboratory of Computational Biology
National Institutes of Health, NHLBI
5635 Fishers Ln, Rm T900
Rockville MD, 20852
https://www.lobos.nih.gov/lcb
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Received on Thu Apr 26 2018 - 11:00:02 PDT
