
NAMEgmxanaeig  Analyze eigenvectors/normal modesSYNOPSISgmx anaeig [v [<.trr/.cpt/...>]] [v2 [<.trr/.cpt/...>]] [f [<.xtc/.trr/...>]] [s [<.tpr/.gro/...>]] [n [<.ndx>]] [eig [<.xvg>]] [eig2 [<.xvg>]] [comp [<.xvg>]] [rmsf [<.xvg>]] [proj [<.xvg>]] [2d [<.xvg>]] [3d [<.gro/.g96/...>]] [filt [<.xtc/.trr/...>]] [extr [<.xtc/.trr/...>]] [over [<.xvg>]] [inpr [<.xpm>]] [b <time>] [e <time>] [dt <time>] [tu <enum>] [[no]w] [xvg <enum>] [first <int>] [last <int>] [skip <int>] [max <real>] [nframes <int>] [[no]split] [[no]entropy] [temp <real>] [nevskip <int>] DESCRIPTIONgmx anaeig analyzes eigenvectors. The eigenvectors can be of a covariance matrix (gmx covar) or of a Normal Modes analysis (gmx nmeig).When a trajectory is projected on eigenvectors, all structures are fitted to the structure in the eigenvector file, if present, otherwise to the structure in the structure file. When no run input file is supplied, periodicity will not be taken into account. Most analyses are performed on eigenvectors first to last, but when first is set to 1 you will be prompted for a selection. comp: plot the vector components per atom of eigenvectors first to last. rmsf: plot the RMS fluctuation per atom of eigenvectors first to last (requires eig). proj: calculate projections of a trajectory on eigenvectors first to last. The projections of a trajectory on the eigenvectors of its covariance matrix are called principal components (pc’s). It is often useful to check the cosine content of the pc’s, since the pc’s of random diffusion are cosines with the number of periods equal to half the pc index. The cosine content of the pc’s can be calculated with the program gmx analyze. 2d: calculate a 2d projection of a trajectory on eigenvectors first and last. 3d: calculate a 3d projection of a trajectory on the first three selected eigenvectors. filt: filter the trajectory to show only the motion along eigenvectors first to last. extr: calculate the two extreme projections along a trajectory on the average structure and interpolate nframes frames between them, or set your own extremes with max. The eigenvector first will be written unless first and last have been set explicitly, in which case all eigenvectors will be written to separate files. Chain identifiers will be added when writing a .pdb file with two or three structures (you can use rasmol nmrpdb to view such a .pdb file). Overlap calculations between covariance analysisNote: the analysis should use the same fitting structureover: calculate the subspace overlap of the eigenvectors in file v2 with eigenvectors first to last in file v. inpr: calculate a matrix of innerproducts between eigenvectors in files v and v2. All eigenvectors of both files will be used unless first and last have been set explicitly. When v and v2 are given, a single number for the overlap between the covariance matrices is generated. Note that the eigenvalues are by default read from the timestamp field in the eigenvector input files, but when eig, or eig2 are given, the corresponding eigenvalues are used instead. The formulas are: difference = sqrt(tr((sqrt(M1)  sqrt(M2))^2)) normalized overlap = 1  difference/sqrt(tr(M1) + tr(M2)) shape overlap = 1  sqrt(tr((sqrt(M1/tr(M1))  sqrt(M2/tr(M2)))^2)) where M1 and M2 are the two covariance matrices and tr is the trace of a matrix. The numbers are proportional to the overlap of the square root of the fluctuations. The normalized overlap is the most useful number, it is 1 for identical matrices and 0 when the sampled subspaces are orthogonal. When the entropy flag is given an entropy estimate will be computed based on the Quasiharmonic approach and based on Schlitter’s formula. OPTIONSOptions to specify input files:
Options to specify output files:
Other options:
SEE ALSOgmx(1)More information about GROMACS is available at <http://www.gromacs.org/>. COPYRIGHT2022, GROMACS development team
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