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       g_cluster - clusters structures

       VERSION 4.0.1


       g_cluster  -f  traj.xtc  -s  topol.tpr  -n  index.ndx  -dm  rmsd.xpm -o
       rmsd-clust.xpm -g cluster.log -dist rmsd-dist.xvg -ev rmsd-eig.xvg  -sz
       clust-size.xvg   -tr   clust-trans.xpm   -ntr   clust-trans.xvg   -clid
       clust-id.xvg -cl clusters.pdb -[no]h -nice int -b time -e time -dt time
       -tu enum -[no]w -[no]xvgr -[no]dista -nlevels int -cutoff real -[no]fit
       -max real -skip int -[no]av -wcl int -nst int -rmsmin real -method enum
       -minstruct int -[no]binary -M int -P int -seed int -niter int -kT real


       g_cluster  can  cluster  structures  with  several  different  methods.
       Distances between structures can be determined  from  a  trajectory  or
       read from an XPM matrix file with the  -dm option.  RMS deviation after
       fitting or RMS deviation of atom-pair distances can be used  to  define
       the distance between structures.

       single  linkage:  add a structure to a cluster when its distance to any
       element of the cluster is less than  cutoff.

       Jarvis Patrick: add a structure to a cluster when this structure and  a
       structure  in  the cluster have each other as neighbors and they have a
       least  P neighbors in common. The neighbors of a structure  are  the  M
       closest structures or all structures within  cutoff.

       Monte Carlo: reorder the RMSD matrix using Monte Carlo.

       diagonalization: diagonalize the RMSD matrix.

       gromos:  use  algorithm  as  described in Daura  et al.  ( Angew. Chem.
       Int. Ed.  1999,  38, pp 236-240).   Count  number  of  neighbors  using
       cut-off,  take  structure with largest number of neighbors with all its
       neighbors as cluster and eleminate it from the pool of clusters. Repeat
       for remaining structures in pool.

       When  the  clustering  algorithm  assigns each structure to exactly one
       cluster (single linkage, Jarvis Patrick and gromos)  and  a  trajectory
       file  is  supplied, the structure with the smallest average distance to
       the others or the average structure or all structures for each  cluster
       will  be  written  to  a  trajectory file. When writing all structures,
       separate numbered files are made for each cluster.

       Two output files are always written:

        -o writes the RMSD values in the upper left half of the matrix  and  a
       graphical  depiction  of  the  clusters  in  the  lower right half When
       -minstruct = 1 the graphical depiction is black when two structures are
       in the same cluster.  When  -minstruct  1 different colors will be used
       for each cluster.

        -g writes information on the options used and a detailed list  of  all
       clusters and their members.

       Additionally, a number of optional output files can be written:

        -dist writes the RMSD distribution.

        -ev writes the eigenvectors of the RMSD matrix diagonalization.

        -sz writes the cluster sizes.

        -tr writes a matrix of the number transitions between cluster pairs.

        -ntr writes the total number of transitions to or from each cluster.

        -clid writes the cluster number as a function of time.

         -cl  writes  average  (with option  -av) or central structure of each
       cluster or writes numbered files with cluster members  for  a  selected
       set of clusters (with option  -wcl, depends on  -nst and  -rmsmin).


       -f traj.xtc Input, Opt.
        Trajectory: xtc trr trj gro g96 pdb cpt

       -s topol.tpr Input, Opt.
        Structure+mass(db): tpr tpb tpa gro g96 pdb

       -n index.ndx Input, Opt.
        Index file

       -dm rmsd.xpm Input, Opt.
        X PixMap compatible matrix file

       -o rmsd-clust.xpm Output
        X PixMap compatible matrix file

       -g cluster.log Output
        Log file

       -dist rmsd-dist.xvg Output, Opt.
        xvgr/xmgr file

       -ev rmsd-eig.xvg Output, Opt.
        xvgr/xmgr file

       -sz clust-size.xvg Output, Opt.
        xvgr/xmgr file

       -tr clust-trans.xpm Output, Opt.
        X PixMap compatible matrix file

       -ntr clust-trans.xvg Output, Opt.
        xvgr/xmgr file

       -clid clust-id.xvg Output, Opt.
        xvgr/xmgr file

       -cl clusters.pdb Output, Opt.
        Trajectory: xtc trr trj gro g96 pdb cpt


        Print help info and quit

       -nice int 19
        Set the nicelevel

       -b time 0
        First frame (ps) to read from trajectory

       -e time 0
        Last frame (ps) to read from trajectory

       -dt time 0
        Only use frame when t MOD dt = first time (ps)

       -tu enum ps
        Time unit:  ps,  fs,  ns,  us,  ms or  s

        View output xvg, xpm, eps and pdb files

        Add  specific  codes  (legends  etc.)  in the output xvg files for the
       xmgrace program

        Use RMSD of distances instead of RMS deviation

       -nlevels int 40
        Discretize RMSD matrix in  levels

       -cutoff real 0.1
        RMSD cut-off (nm) for two structures to be neighbor

        Use least squares fitting before RMSD calculation

       -max real -1
        Maximum level in RMSD matrix

       -skip int 1
        Only analyze every nr-th frame

        Write average iso middle structure for each cluster

       -wcl int 0
        Write all structures for first  clusters to numbered files

       -nst int 1
        Only write all structures if more than  per cluster

       -rmsmin real 0
        minimum rms difference with rest of cluster for writing structures

       -method enum linkage
        Method   for   cluster   determination:    linkage,    jarvis-patrick,
       monte-carlo,  diagonalization or  gromos

       -minstruct int 1
        Minimum number of structures in cluster for coloring in the xpm file

        Treat  the  RMSD matrix as consisting of 0 and 1, where the cut-off is
       given by -cutoff

       -M int 10
        Number of nearest neighbors considered for Jarvis-Patrick algorithm, 0
       is use cutoff

       -P int 3
        Number of identical nearest neighbors required to form a cluster

       -seed int 1993
        Random number seed for Monte Carlo clustering algorithm

       -niter int 10000
        Number of iterations for MC

       -kT real 0.001
        Boltzmann  weighting  factor  for Monte Carlo optimization (zero turns
       off uphill steps)



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                                Thu 16 Oct 2008                   g_cluster(1)