Re: [gmx-users] Pca 3d structure ?

Hi, The 3D structures are in the original trajectory. If you want particular 3D structure, e.g. corresponding to a certain projection, then look at the time corresponding to that particular projection and extract the frame at that time. Cheers, Tsjerk On Wed, Mar 6, 2013 at 5:13 PM, 라지브간디 wr

[gmx-users] Pca 3d structure ?

Dear gmx user, I have used g_covar and g_anaeig for backbone of proteins and got projections, 2d projections and so on. What want to know is how do i get the 3D structure of the whole protein (not only backbone) ? -- gmx-users mailing listgmx-users@gromacs.org http://lists.gromacs.org/mail

Re: [gmx-users] pca-based MD

Hi, Thomas! I'd like to ask you some addition questions about FMA. As I understood from the FMA page that technique is something like integrator of principal components (merge some PCs with identical functional motion seen in the X-ray structures for instance) calculated from g_covar. So FMA is al

RE: [gmx-users] PCA

use make_ndx to create a new group, then use the -n index.ndx option with g_covar > Date: Tue, 6 Nov 2012 09:58:51 +0200 > From: tkil...@gmail.com > To: gmx-users@gromacs.org > Subject: [gmx-users] PCA > > hi all, > > i would like to apply PCA (principal component a

Re: [gmx-users] PCA

Hi Tuba, I guess you have to create an index for each peptide and then extracting covariance matrix on each peptide using the new indexes. Francesco 2012/11/6 Tuba Kilinc > hi all, > > i would like to apply PCA (principal component analysis) for my peptides > that i simulated. i do know PCA f

[gmx-users] PCA

hi all, i would like to apply PCA (principal component analysis) for my peptides that i simulated. i do know PCA for one trajectory but what if i have more than one peptide ? how can i apply pca for example 10 peptides in a box ? typically, i start with a PCA on the simulation with g_covar -s

Re: [gmx-users] PCA

Ah, so, right, you'll have to extract a trajectory for each peptide and concatenate those trajectories. Sorry for misreading... Tsjerk On Thu, Oct 18, 2012 at 9:44 AM, Tsjerk Wassenaar wrote: > Hi Tuba, > > You can concatenate the trajectories with trjcat and perform PCA on > the resulting traj

Re: [gmx-users] PCA

Hi Tuba, You can concatenate the trajectories with trjcat and perform PCA on the resulting trajectory. Cheers, Tsjerk On Thu, Oct 18, 2012 at 9:07 AM, Tuba Kilinc wrote: > hi all, > > i would like to apply PCA (principal component analysis) for my peptides > that i simulated. i do know PCA for

[gmx-users] PCA

hi all, i would like to apply PCA (principal component analysis) for my peptides that i simulated. i do know PCA for one trajectory but what if i have more than one peptide ? how can i apply pca for example 10 peptides in a box ? typically, i start with a PCA on the simulation g_covar -s protein

Re: [gmx-users] pca-based MD

Dear all! I've read some reference papers about EDA sampling methods and found such usefull things. First of all as I understood for biassing MD simulation along several PCs extracted from another run the make_edi -radacc 1-3 option is exactly what I need. But I havent still understood about m

Re: [gmx-users] pca-based MD

I've still made such 'only c-alpha ensemble' of my structures by the other software and performed x-ray PCA. As the result I've extracted eigenvectors and obtained reasonable distribution (projection) of the structures along that eigenvectors. Now I have questions about pca-biassed MD_run. I've ma

Re: [gmx-users] pca-based MD

I've tried to make PCA from my X-ray data and forced with many problems :) Firstly I've made pdb trajectory in NMR-like format ( by means of pymol) consisted of all X-ray structures. than I've make .tpr file (From the tpr of the same protein which I've simulated previously) for the subset of C-al

Re: [gmx-users] pca-based MD

Hi, thanks again for explanation. Its also intresting to me is it possible > to do further biassed MD guided on that FMA modes as well as obtain > projections onto that FMA sub-spaces of X-ray datasets for instance ? > ( e.g for comparison of the results from FMA of experimental data as > well as

Re: [gmx-users] PCA comparison between two simulations

Hi, I would first do an alignment between the two proteins and then do PCA using only the Ca atom coordinates of the common atoms (or the ones you think are equivalent). I would also remove the very flexible loops to extract pure low-frequency motions with PCA. At the end you must have two eigensp

Re: [gmx-users] pca-based MD

Thomas, thanks again for explanation. Its also intresting to me is it possible to do further biassed MD guided on that FMA modes as well as obtain projections onto that FMA sub-spaces of X-ray datasets for instance ? ( e.g for comparison of the results from FMA of experimental data as well as MD_d

Re: [gmx-users] pca-based MD

On 23 September 2012 17:18, James Starlight wrote: > Thomas, > > thank you for the explanation > > 1) Indeed ED sampling was exactly that I need. It's not quite > understand for me about correct chose of that parameters for biassing > simulation > > -linfix string Indices of eigenvec

Re: [gmx-users] pca-based MD

Thomas, thank you for the explanation 1) Indeed ED sampling was exactly that I need. It's not quite understand for me about correct chose of that parameters for biassing simulation -linfix string Indices of eigenvectors for fixed increment linear sampling

Re: [gmx-users] pca-based MD

I presume you are referring to Essential Dynamics Sampling, described in section 3.14 of the manual (v4.5.4). There is also a great tool that finds the few PCs that are maximally correlated to a functional quantity (e.g. the volume of the active site). The technique is coined Functional Mode Analys

Re: [gmx-users] PCA on secondary structure of protein.

Sure! You'll just be looking at correlations between secondary structure elements, disregarding the role that the loops may play. But it's a sound approach. :) Tsjerk On Tue, Nov 1, 2011 at 3:44 PM, vivek modi wrote: > Hi, > > I plan to perform PCA on a globular protein which I am studying. Th

[gmx-users] PCA on secondary structure of protein.

Hi, I plan to perform PCA on a globular protein which I am studying. The simulation for the same is done for 100ns. I have small doubt. Is it appropriate to perform PCA to study the movement in protein on only secondary structure elements (helices in this case). My protein contains long loops whic

Re: [gmx-users] PCA depends of the number of frames?

> You need two frames to say something about motion in a plane, and you > need at least three points to say something about motion in all three > dimensions. Right, that should've been three frames, and four points :S Sorry. Tsjerk -- Tsjerk A. Wassenaar, Ph.D. post-doctoral researcher Mole

Re: [gmx-users] PCA depends of the number of frames?

Hi Ricardo > For the case (1) and (2) the most representative structure was used in the > option -s ( One that has the lowest rmsd with respect to the average of each > cluster). > In case (3) the initial structure of the MD was used in the option -s. If all belong to the same system, it is bette

[gmx-users] PCA depends of the number of frames?

PCA depend of the number of frames? Dear users I did a test with three sets of frames (1) 13 frames From X-ray(2) 28 frames From X-ray(3) 1000 frames From MD In each case translations and rotations were eliminated.then g_covar was applied to obtain the covariance matrix g_covar -f sample.xtc

[gmx-users] PCA and Free energy landscape

Hello, I have done PCA using cartesian coordinates by the help of gromacs(g_covar and g_anaeig), then using the 2-d projection of trajectory on first two eigenvectors as reaction coordinates,I have calculated a 2-d representation of the gibbs free energy landscape(g_sham) using gromacs.Now on this

[gmx-users] PCA

I have done PCA using first g_covar and got eigval.xvg and eigenvec.trr files. The eigenvectors were analyzed by g_anaeig program and got eigcomp.xvg, eigrmsf.xvg, proj.xvg, and 2dproj.xvg files. Then I want to know 1. Which file among these shows relative positional fluctuation with eigenvector in

[gmx-users] PCA

I have done PCA using first g_covar and got eigval.xvg and eigenvec.trr files. The eigenvectors were analyzed by g_anaeig program and got eigcomp.xvg, eigrmsf.xvg, proj.xvg, and 2dproj.xvg files. Then I want to know 1. Which file among these shows relative positional fluctuation with eigenvector in

Re: [gmx-users] PCA

Hi Pawan, You may want to read up on PCA in some elementary multivariate statistics textbook to get a better grasp on what it does and how it's done. > I have a little concept problem regarding principal component analysis. So > my question is about ED sampling are as follows: > > 1. I have read

[gmx-users] PCA

I have a little concept problem regarding principal component analysis. So my question is about ED sampling are as follows: 1. I have read from the manual that g_covar calculates and diagonalize the (mass-weighted) covariance matrix. So what is the meaning of mass-weighted in covariance matrix? 2

[gmx-users] PCA

I have a little concept problem regarding principal component analysis. So my question is about ED sampling are as follows: 1. I have read from the manual that g_covar calculates and diagonalize the (mass-weighted) covariance matrix. So what is the meaning of mass-weighted in covariance matrix? 2

Re: [gmx-users] PCA component

Hi, Of course you can filter along one eigenvector. My guess is that the option for 3d projection was also selected (-3d)... Cheers, Tsjerk On May 8, 2010 10:08 AM, "David van der Spoel" wrote: On 2010-05-08 08.45, Anirban Ghosh wrote: > > Hi ALL, > > I am trying to do a PCA for my simulatio

Re: [gmx-users] PCA component

On 2010-05-08 08.45, Anirban Ghosh wrote: Hi ALL, I am trying to do a PCA for my simulation. I generated a covarience matrix using g_covar and now I want to visualize the motion only along first principal component. So with g_anaeig I gave the option "-first 1" "-last 1". But it gave the error a

[gmx-users] PCA component

Hi ALL, I am trying to do a PCA for my simulation. I generated a covarience matrix using g_covar and now I want to visualize the motion only along first principal component. So with g_anaeig I gave the option "-first 1" "-last 1". But it gave the error as:

[gmx-users] PCA component

Hi ALL, I am trying to do a PCA for my simulation. I generated a covarience matrix using g_covar and now I want to visualize the motion only along first principal component. So with g_anaeig I gave the option "-first 1" "-last 1". But it gave the error as:

Re: [gmx-users] PCA tutorial

rituraj purohit wrote: Dear friends, I want to do PCA for my MD data. If anybody know the tutorial regarding PCA, please let me know. Thanks in advanced There's not so much a tutorial for PCA as there is a sequence of a few commands. Tsjerk's lysozyme tutorial has an excellent section on P

[gmx-users] PCA tutorial

Dear friends, I want to do PCA for my MD data. If anybody know the tutorial regarding PCA, please let me know. Thanks in advanced Regard Rohan On 2/1/10, gmx-users-requ...@gromacs.org wrote: > Send gmx-users mailing list submissions to > gmx-users@gromacs.org > > To subscribe or unsubs

[gmx-users] PCA-how much of the structure to include?

Dear all, I'm performing PCA on a 20 nanosecond simulation of a ~200 aa protein. I performed PCA initially using only alpha carbons. I then reperformed it by using the backbone for fitting, and the whole protein for covar analysis. This worked but when I looked at some of the motions using the -e

RE: [gmx-users] PCA comparison

Hi, The message from all your analysis is very clear: Your principal components have not converged. Berk. > Date: Thu, 23 Oct 2008 14:37:00 + > From: [EMAIL PROTECTED] > To: gmx-users@gromacs.org > Subject: [gmx-users] PCA comparison > > > Hi Berk, > Indeed, when I

[gmx-users] PCA comparison

Hi Berk, Indeed, when I say that the overlap is below 0.4 I mean the highest values of the matrix of inner products. I expected the vectors to be quite similar with their neighbours, i.e. I expected vector 5 to have a high inner product with 5, or 4 or 6 of the other set. But this diagonal trend,

RE: [gmx-users] PCA comparison

genvector inner products to check if the pc directions change, or if only the eigenvalue change? Berk > Date: Thu, 23 Oct 2008 13:10:23 +0200 > From: [EMAIL PROTECTED] > To: gmx-users@gromacs.org > Subject: [gmx-users] PCA comparison > > Dear all, > We are performi

[gmx-users] PCA comparison

Dear all, We are performing some PCA analysis of several 22ns trajectories of a protein hexamer at different temperatures (280, 300, 320K). We expected to see a similar movement decribed by the ~10 lowest PCA's. their overlap is very poor: Below 0.35, and the diagonal elements are not remarkably hi

Re: [gmx-users] PCA uses average structure or reference structure

Hi Sunita, You should check the manual on this (and some statistics texts on PCA). g_covar calculates the average structure and takes the deviations around this average for further calculations. In that respect, you should be save. But the fact that you pose this question indicates that you may be

[gmx-users] PCA uses average structure or reference structure

hi all I did MD studies of a dimer using NAMD and charmm forcefields.now, in order analyze dynamic motions PCA was done using the command g_covar of GROMACS.here i gave .trr and .pdb as input.and selected backbone for analysis.the output obtained were average.pdb, average.trretc

RE: [gmx-users] PCA multiple outputs from g_anaeig

@gromacs.org Subject: [gmx-users] PCA multiple outputs from g_anaeig Hello, g_anaeig analyzes the eigen vectors from eigenvec.trr and eigenval.xvg generated by g_covar and writes the proj.xvg, 2d_proj.xvg, 3d_proj.xvg, filtered.xvg. etc. My question is., For a single run of g_anaeig only a

[gmx-users] PCA multiple outputs from g_anaeig

Hello, g_anaeig analyzes the eigen vectors from eigenvec.trr and eigenval.xvg generated by g_covar and writes the proj.xvg, 2d_proj.xvg, 3d_proj.xvg, filtered.xvg. etc. My question is., For a single run of g_anaeig only a specific 2d_proj.xvg (say between eigenvec 1 and 8), 3d_proj.xvg...

Re: [gmx-users] PCA average structure

Hi, Thank you for your reply. Regards, Nadia Vahdati Quoting David van der Spoel <[EMAIL PROTECTED]>: > [EMAIL PROTECTED] wrote: > > Hi gromacs users, > > > > Recently I asked about running PCA on MD output from Amber which was > > helpful.Based on the advise I used g_covar to run PCA on

Re: [gmx-users] PCA average structure

[EMAIL PROTECTED] wrote: Hi gromacs users, Recently I asked about running PCA on MD output from Amber which was helpful.Based on the advise I used g_covar to run PCA on all atoms. When I look at my average structure or filtered trajectory, I notice that some of the bonds are much shorter t

[gmx-users] PCA average structure

Hi gromacs users, Recently I asked about running PCA on MD output from Amber which was helpful.Based on the advise I used g_covar to run PCA on all atoms. When I look at my average structure or filtered trajectory, I notice that some of the bonds are much shorter than expected, and in the ca

Re: [gmx-users] pca on amber MD output

Thank you all for your replies. It has been very helpful. Kind regards Nadia Quoting Tsjerk Wassenaar <[EMAIL PROTECTED]>: > Hi Nadia, > > Gromacs reads .pdb files (single or multimodel ones) perfectly. The > programmers have noted that .pdb files are in Angstroms usually. > Besides, you don'

Re: [gmx-users] pca on amber MD output

Hi Nadia, Gromacs reads .pdb files (single or multimodel ones) perfectly. The programmers have noted that .pdb files are in Angstroms usually. Besides, you don't need an additional .pdb file when reading a .pdb trajectory (if required you can give the same file as a structure file). Cheers, Tsj

Re: [gmx-users] pca on amber MD output

Hi Mark, Thank you very much for the clarification. I am also testing this at the moment running with and without Anstroms/nm conversions. Regards, Nadia Quoting Mark Abraham <[EMAIL PROTECTED]>: > [EMAIL PROTECTED] wrote: > > Hi gromacs users, > > > > I am a new user to gromacs and realise

Re: [gmx-users] pca on amber MD output

[EMAIL PROTECTED] wrote: Hi gromacs users, I am a new user to gromacs and realise that there are lots of comments on the archive already about using gromacs with amber outputs but I am getting rather mixed messages. I would like to clarify a few things. I have an Amber trjectory file saved thr

Re: [gmx-users] pca on amber MD output

Hi, Thank you for your advise. I am aware of the IED webpage and that's what drew my attention to the Angstrom, nm issue! However I had run pca without any conversion and my structure didn't get distorted, all analysis returns structures in Angstrom that don't appear out of place but what I a

Re: [gmx-users] pca on amber MD output

On Thursday 05 October 2006 16:56, [EMAIL PROTECTED] wrote: > Hi gromacs users, > > I am a new user to gromacs and realise that there are lots of comments on > the archive already about using gromacs with amber outputs but I am getting > rather mixed messages. > > I would like to clarify a few thin

[gmx-users] pca on amber MD output

Hi gromacs users, I am a new user to gromacs and realise that there are lots of comments on the archive already about using gromacs with amber outputs but I am getting rather mixed messages. I would like to clarify a few things. I have an Amber trjectory file saved through VMD in a .pdb format

[gmx-users] PCA, equilibrium, free energies

We performed a long simulation on a peptide and we have carried out principal component analysis. First three eigen vectors account for 70% of the variance. From 5th eigen vector onwards distributions are gaussian. The first eigen vector definitely is not a gaussian. Under these conditions ***c

Re: [gmx-users] PCA eigenvalue normalization

Have you checked if your peptide is "jumping" out of the box? Regards. Pedro Hello, I have performed PCA analysis, without mass weighting, on a peptide using g_covar and g_anaeig. The first principal component generally corresponds to the stretching of the peptide. I understand that ea

Re: [gmx-users] PCA eigenvalue normalization

Hi Tyler,Note that the eigenvalue represents the sum of the variances for each particle along the associated eigenvector. That seems quite reasonable to me.TsjerkOn 4/6/06, Tyler Luchko <[EMAIL PROTECTED]> wrote: Hello,I have performed PCA analysis, without mass weighting, on a peptideusing g_cova

[gmx-users] PCA eigenvalue normalization

Hello, I have performed PCA analysis, without mass weighting, on a peptide using g_covar and g_anaeig. The first principal component generally corresponds to the stretching of the peptide. I understand that each eigenvalue represents the variance in the motion along the associated eigen