Hi,
I think the principal downside of dihedral PCA in cpptraj vs Cartesian
PCA is that you can't easily visualize dihedral PCA results in
coordinate space yet (via creating pseudo-trajectories) the way you
can with Cartesian PCA. However, it can still be useful to e.g.
compare dihedral PC projections between two separate trajectories.
Check this article out for more on dihedral PCA:
https://aip.scitation.org/doi/10.1063/1.2746330
Hope this helps,
-Dan
On Fri, Jul 9, 2021 at 12:30 AM 刘保艳 <870726grin.163.com> wrote:
>
> Dear Sir or Madam,
> I am writing to ask questions about how to analyze the results about dPCA (Dihedral covariance matrix calculation and projection for backbone phi/psi) analysis. I get the following instructions from AMBER Manuals, also I succeeded and got three .dat files. However, I don't know how to analyze it and don't know the detailed principle or theory, can you give me some advice or recommend some examples to me? Thank you very much. Looking forward to your reply.
> 32.13.2. Dihedral covariance matrix calculation and projection for backbone phi/psi (PCA)
> parm ../1rrb_vac.prmtop
> trajin ../1rrb_vac.mdcrd # Generation of phi/psi dihedral data
> multidihedral BB phi psi resrange 2 run
> # Calculate dihedral covariance matrix and obtain eigenvectors
> matrix dihcovar dihedrals BB[*] out dihcovar.dat name DIH
> diagmatrix DIH vecs 4 out modes.dihcovar.dat name DIHMODES
> run # Project along eigenvectors
> projection evecs DIHMODES out dih.project.dat beg 1 end 4 dihedrals BB[*] run
>
>
> yours sincerely,
> Baoyan Liu
>
>
>
>
>
>
>
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Received on Tue Jul 13 2021 - 06:30:02 PDT
