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NAME

       make_edi - generate input files for essential dynamics sampling

       VERSION 4.0.1

SYNOPSIS

       make_edi  -f  eigenvec.trr  -eig eigenval.xvg -s topol.tpr -n index.ndx
       -tar target.gro -ori origin.gro -o sam.edi -[no]h -nice  int  -[no]xvgr
       -mon  string -linfix string -linacc string -flood string -radfix string
       -radacc string -radcon string -outfrq int -slope real  -maxedsteps  int
       -deltaF0 real -deltaF real -tau real -eqsteps int -Eflnull real -T real
       -alpha real -linstep string -accdir string -radstep real  -[no]restrain
       -[no]hessian -[no]harmonic

DESCRIPTION

         make_edi  generates an essential dynamics (ED) sampling input file to
       be used with mdrun based on  eigenvectors  of  a  covariance  matrix  (
       g_covar) or from a normal modes anaysis ( g_nmeig).  ED sampling can be
       used  to  manipulate  the   position   along   collective   coordinates
       (eigenvectors)  of  (biological)  macromolecules  during  a simulation.
       Particularly, it may be used to enhance the sampling efficiency  of  MD
       simulations  by  stimulating  the  system  to explore new regions along
       these collective coordinates. A  number  of  different  algorithms  are
       implemented  to  drive  the  system  along  the eigenvectors ( -linfix,
       -linacc,  -radfix,  -radacc,  -radcon), to keep the  position  along  a
       certain (set of) coordinate(s) fixed ( -linfix), or to only monitor the
       projections of the positions onto these coordinates ( -mon).

       References:

       A. Amadei, A.B.M. Linssen, B.L. de Groot, D.M.F. van Aalten and  H.J.C.
       Berendsen;  An  efficient method for sampling the essential subspace of
       proteins., J. Biomol. Struct. Dyn. 13:615-626 (1996)

       B.L. de Groot, A. Amadei,  D.M.F.  van  Aalten  and  H.J.C.  Berendsen;
       Towards an exhaustive sampling of the configurational spaces of the two
       forms of the peptide hormone guanylin,J.  Biomol.  Struct.  Dyn.  13  :
       741-751 (1996)

       B.L.  de  Groot,  A.Amadei,  R.M.  Scheek, N.A.J. van Nuland and H.J.C.
       Berendsen; An extended sampling of the  configurational  space  of  HPr
       from E. coli PROTEINS: Struct. Funct. Gen. 26: 314-322 (1996)

       You  will  be  prompted for one or more index groups that correspond to
       the eigenvectors, reference structure, target positions, etc.

         -mon:  monitor  projections  of   the   coordinates   onto   selected
       eigenvectors.

          -linfix:   perform   fixed-step   linear  expansion  along  selected
       eigenvectors.

         -linacc:  perform  acceptance   linear   expansion   along   selected
       eigenvectors.   (steps  in  the  desired  directions  will be accepted,
       others will be rejected).

         -radfix:  perform  fixed-step   radius   expansion   along   selected
       eigenvectors.

          -radacc:   perform   acceptance   radius  expansion  along  selected
       eigenvectors.  (steps in the desired direction will be accepted, others
       will  be rejected).  Note: by default the starting MD structure will be
       taken as origin of the first expansion cycle for radius  expansion.  If
       -ori  is  specified,  you will be able to read in a structure file that
       defines an external origin.

        -radcon:  perform  acceptance  radius   contraction   along   selected
       eigenvectors towards a target structure specified with  -tar.

       NOTE: each eigenvector can be selected only once.

        -outfrq:  frequency (in steps) of writing out projections etc. to .edo
       file

        -slope: minimal slope in acceptance radius expansion. A new  expansion
       cycle  will  be  started  if the spontaneous increase of the radius (in
       nm/step) is less than the value specified.

        -maxedsteps: maximum number of steps per  cycle  in  radius  expansion
       before a new cycle is started.

       Note  on  the  parallel implementation: since ED sampling is a ’global’
       thing (collective coordinates etc.), at least on the ’protein’ side, ED
       sampling  is  not  very parallel-friendly from an implentation point of
       view. Because parallel ED requires much extra communication, expect the
       performance  to  be  lower  as in a free MD simulation, especially on a
       large number of nodes.

       All output of mdrun (specify with -eo) is written to a  .edo  file.  In
       the output file, per OUTFRQ step the following information is present:

       * the step number

       *  the  number of the ED dataset. (Note that you can impose multiple ED
       constraints in a single simulation - on different molecules e.g.  -  if
       several .edi files were concatenated first. The constraints are applied
       in the order they appear in the .edi file.)

       * RMSD (for atoms involved in  fitting  prior  to  calculating  the  ED
       constraints)

       * projections of the positions onto selected eigenvectors

       FLOODING:

       with  -flood  you  can specify which eigenvectors are used to compute a
       flooding potential, which will  lead  to  extra  forces  expelling  the
       structure  out of the region described by the covariance matrix. If you
       switch -restrain the potential is inverted and the structure is kept in
       that region.

       The  origin  is normally the average structure stored in the eigvec.trr
       file.  It can  be  changed  with  -ori  to  an  arbitrary  position  in
       configurational  space.   With  -tau, -deltaF0 and -Eflnull you control
       the flooding behaviour.  Efl is the flooding strength,  it  is  updated
       according  to  the rule of adaptive flooding.  Tau is the time constant
       of adaptive flooding, high  tau  means  slow  adaption  (i.e.  growth).
       DeltaF0  is  the  flooding  strength  you want to reach after tau ps of
       simulation.  To use constant Efl set -tau to zero.

       -alpha is a fudge parameter  to  control  the  width  of  the  flooding
       potential.  A  value  of 2 has been found to give good results for most
       standard cases in flooding of proteins.  Alpha basically  accounts  for
       incomplete  sampling,  if you sampled further the width of the ensemble
       would increase, this is mimicked by alpha1.  For restraining alpha1 can
       give you smaller width in the restraining potential.

       RESTART  and  FLOODING:  If  you  want  to  restart  a crashed flooding
       simulation please find the values deltaF and Efl in the output file and
       manually put them into the .edi file under DELTA_F0 and EFL_NULL.

FILES

       -f eigenvec.trr Input
        Full precision trajectory: trr trj cpt

       -eig eigenval.xvg Input, Opt.
        xvgr/xmgr file

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

       -n index.ndx Input, Opt.
        Index file

       -tar target.gro Input, Opt.
        Structure file: gro g96 pdb tpr tpb tpa

       -ori origin.gro Input, Opt.
        Structure file: gro g96 pdb tpr tpb tpa

       -o sam.edi Output
        ED sampling input

OTHER OPTIONS

       -[no]hno
        Print help info and quit

       -nice int 0
        Set the nicelevel

       -[no]xvgryes
        Add  specific  codes  (legends  etc.)  in the output xvg files for the
       xmgrace program

       -mon string
        Indices of  eigenvectors  for  projections  of  x  (e.g.  1,2-5,9)  or
       1-100:10 means 1 11 21 31 ... 91

       -linfix string
        Indices of eigenvectors for fixed increment linear sampling

       -linacc string
        Indices of eigenvectors for acceptance linear sampling

       -flood string
        Indices of eigenvectors for flooding

       -radfix string
        Indices of eigenvectors for fixed increment radius expansion

       -radacc string
        Indices of eigenvectors for acceptance radius expansion

       -radcon string
        Indices of eigenvectors for acceptance radius contraction

       -outfrq int 100
        Freqency (in steps) of writing output in .edo file

       -slope real 0
        Minimal slope in acceptance radius expansion

       -maxedsteps int 0
        Max nr of steps per cycle

       -deltaF0 real 150
        Target destabilization energy  - used for flooding

       -deltaF real 0
        Start deltaF with this parameter - default 0, i.e. nonzero values only
       needed for restart

       -tau real 0.1
        Coupling constant for  adaption  of  flooding  strength  according  to
       deltaF0, 0 = infinity i.e. constant flooding strength

       -eqsteps int 0
        Number of steps to run without any perturbations

       -Eflnull real 0
        This  is  the  starting  value  of the flooding strength. The flooding
       strength is updated according to the adaptive flooding scheme. To use a
       constant flooding strength use -tau 0.

       -T real 300
        T is temperature, the value is needed if you want to do flooding

       -alpha real 1
        Scale width of gaussian flooding potential with alpha2

       -linstep string
        Stepsizes  (nm/step)  for  fixed  increment  linear  sampling  (put in
       quotes! "1.0 2.3 5.1 -3.1")

       -accdir string
        Directions for acceptance linear sampling - only sign counts! (put  in
       quotes! "-1 +1 -1.1")

       -radstep real 0
        Stepsize (nm/step) for fixed increment radius expansion

       -[no]restrainno
        Use   the   flooding   potential  with  inverted  sign  -  effects  as
       quasiharmonic restraining potential

       -[no]hessianno
        The eigenvectors and eigenvalues are from a Hessian matrix

       -[no]harmonicno
        The eigenvalues are interpreted as spring constant

SEE ALSO

       gromacs(7)

       More     information     about     GROMACS     is     available      at
       <http://www.gromacs.org/>.

                                Thu 16 Oct 2008                    make_edi(1)