[gmx-users] Correct method to do Cartesian PCA

[Tsjerk Wassenaar]

Hi Bin,

Did you use the same force field, simulation length, other parameters? The
system size might play a role too. And for trialanine the reference used
for fitting may matter too. But first check all the other
similarities/differences.

Cheers,

Tsjerk
On Jan 8, 2016 16:35, "Bin Liu" <fdusuperstring at gmail.com> wrote:

> Hi Tsjerk,
>
> I used trjconv -pbc and trjconv -nojump to remove PBC and jump across the
> boundary before I applied  g_covar. This paper is about dPCA indeed. But it
> also contains the author's results on cPCA. Thank you for your help.
>
> Regards,
>
> Bin
>
>
>
> Message: 6
> Date: Thu, 7 Jan 2016 23:38:44 +0100
> From: Tsjerk Wassenaar <tsjerkw at gmail.com>
> To: Discussion list for GROMACS users <gmx-users at gromacs.org>
> Subject: Re: [gmx-users] Correct method to do Cartesian PCA
> Message-ID:
>         <
> CABzE1ShYQdeG4Sexss5QtJCy7qfg8s+2Jsh3PMChuFh4xnr7tg at mail.gmail.com>
> Content-Type: text/plain; charset=UTF-8
>
> Hi Bin,
>
> The procedure is correct, provided you ensured that the input trajectory
> did not have PBC jumps. But the paper you refer to is on dihedral angle
> PCA...
>
> Cheers,
>
> Tsjerk
> On Jan 7, 2016 23:06, "Bin Liu" <fdusuperstring at gmail.com> wrote:
>
> > Hi All,
> >
> > I am trying to reproduce the Cartesian PCA results for ALA3 in water by
> > Altis et al. (Fig.2 of Dihedral angle principal component analysis of
> > molecular dynamics simulations.   http://dx.doi.org/10.1063/1.2746330 )
> > It is probably the most well-known PCA analysis results. I created a
> system
> > as they suggested and got a 100 ns trajectory (after an equilibration
> > stage) for applying PCA.
> >
> > GROMOS96 43a1
> > SPC water
> > 300 K, 1 atm
> > PME
> > cutoff 1.0 nm
> > Box larger than they did, 2100~ waters
> >
> > As all the PCA tutorials indicate, applying Cartesian PCA can be as
> simple
> > as two commands, g_covar and g_anaeig. However with all my effort of
> trying
> > all the combinations of arguments I can think of, I still can't reproduce
> > the literature results. Here I humbly request help from experienced users
> > who have applied PCA before.
> >
> > The following is my approach. Suppose I have a .tpr file
> *npt_pr_100ns.tpr*
> > and a .trr file *npt_pr_100ns.trr*
> >
> > *g_covar -s npt_pr_100ns.tpr -f npt_pr_100ns.trr -o eigenval.xvg -v
> > eigenvec.trr -av average.pdb -l covar.log*
> > Choose a group for the least squares fit
> > Group     0 (         System) has  6518 elements
> > Group     1 (        Protein) has    22 elements
> > Group     2 (      Protein-H) has    16 elements
> > ...
> > Select a group: 1
> > Selected 1: 'Protein'
> >
> > Choose a group for the covariance analysis
> > Group     0 (         System) has  6518 elements
> > Group     1 (        Protein) has    22 elements
> > Group     2 (      Protein-H) has    16 elements
> > ...
> > Select a group: 1
> > Selected 1: 'Protein'
> >
> > *g_anaeig -v eigenvec.trr
> > -f npt_pr_100ns.trr -s npt_pr_100ns.tpr -2d 2dproj.xvg -first 1 -last 2*
> >
> > Select the index group that was used for the least squares fit in g_covar
> > Group     0 (         System) has  6518 elements
> > Group     1 (        Protein) has    22 elements
> > Group     2 (      Protein-H) has    16 elements
> > ...
> > Select a group: 1
> > Selected 1: 'Protein'
> >
> > Select an index group of 22 elements that corresponds to the eigenvectors
> > Group     0 (         System) has  6518 elements
> > Group     1 (        Protein) has    22 elements
> > Group     2 (      Protein-H) has    16 elements
> > ...
> > Select a group: 1
> > Selected 1: 'Protein'
> >
> > Is there anything I did incorrectly?  Another question: is -mwa argument
> > which turns on mass weighting for g_covar necessary?  Thank you very
> much.
> >
> > Best Regards,
> >
> > Bin
> > --
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