CHARMM c39b2 gamus.doc

===============================================================================


File: GAMUS -=- Node: Top
Up: (commands.doc) -=- Next: Syntax


                Gaussian Mixture Adaptive Umbrella Sampling (GAMUS)

    GAMUS is a hybrid of adaptive umbrella sampling (see
*Note adumb::(adumb.doc)syntax ) and metadynamics which is suited to
identifying free energy minima for multidimensional reaction coordinates.
Like adaptive umbrella sampling this method attempts to calculate the free
energy surface in terms of designated reaction coordinates, and uses the
negative of this as a biasing potential to enhance the sampling.
The distribution of reaction coordinates is expressed in terms of mixtures
of Gaussians whose size and shape are optimized to fit the data as closely
as possible.  This is similar to the use of Gaussians in metadynamics to fill
free energy basins but is more flexible.  No grids or histograms are used,
which reduces the memory and statistical requirements of the method and
allows the efficient exploration of free energy surfaces in 3-5 dimensions.
The method is described in 

P. Maragakis, A. van der Vaart, and M. Karplus.  J. Phys. Chem. B 113, 4664
(2009).  doi:10.1021/jp808381s

Please report problems to Justin Spiriti at jspiriti@usf.edu and/or
Arjan van der Vaart at avandervaart@usf.edu.


* Menu:

* Syntax::        Syntax of the GAMUS commands
* Function::      Purpose of each of the commands
* Caveats::       Some limitations of the GAMUS method
* Installation::  Installation of GAMUS within CHARMM
* Examples::      Usage example of the GAMUS module



File: GAMUS -=- Node: Syntax
Up: Top -=- Previous: Top -=- Next: Function



                                 Syntax

[SYNTAX GAMUS functions]

Syntax:

GAMUs DIHE     4X(atom-spec)

GAMUs INIT     [TEMP real] [RUNI int] [WUNI int] [IFRQ int]

GAMUS FIT      DATA int NGAUSS int NREF int NITR int SIGMA real GAMMA real

GAMUS REWEight NSIM int QUNI int PUNI int NGAUss int [NREF int] [NITR int]
               [SIGMa real] [GAMMa real] [WEIGHT int]

GAMUS WRITE    UNIT int

GAMUS CLEAR 

GAMUS INFO     [VERBose]

where:      atom-spec ::= { segid resid iupac }
                          { resnumber iupac   }




File: GAMUS -=- Node: Function
Up: Top -=- Previous: Syntax -=- Next: Caveats

 



0.  Introduction

A GAMUS simulation cycles through three stages:
1. Molecular dynamics with a biasing potential equal to minus the free energy 
surface estimated from the previous simulation.
2. Determination of the partition function ratios Z_i/Z_0 for each biased 
simulation relative to the unbiased simulation. This is done using the 
multistate acceptance ratio method (MARE), which optimizes the likelihood of 
observing the work values associated with the transitions between biasing 
potentials (see P. Maragakis et al. J. Phys. Chem. B 112, 6168 (2008))
3. Reweighting of the data and fitting of a new mixture of Gaussians to the 
distribution of reaction coordinates encountered in previous simulations.  
This is done using the expectation-maximization (E-M) algorithm (see A. P. 
Dempster et al.  J. R. Stat. Soc. B 39, 1 (1977) and Bowers et al. 
Comput. Phys. Commun. 164, 311 (2008)).  Starting from an initial guess, this 
algorithm iteratively refines the weights, means, and variance-covariance 
matrices of the Gaussians in order to maximize the likelihood of observing the 
data given the fitted distribution.  The E-M algorithm is started several
times from randomly chosen starting points; the fit with the best likelihood
is chosen. From this fit, the new free energy surface and biasing potential
may be obtained.

GAMUS produces three sets of input/output files.  GAMUS potential files contain
the weights, means, and variance-covariance matrices of the Gaussians used to 
define the biasing potential, as well as the value of the Bayesian knowledge 
parameter gamma.  These files have the following format:

34 5 ! Number of Gaussians, number of coordinates
-25  ! log(gamma)

then follows the natural logarithms of the weights, the means, and the
inverses of the variance-covariance matrices, see subroutine READGAMUS in
gamus/gamus.src
Energy-coordinate files contain the values of the reaction coordinates
encountered during the simulation, as well as the values of the biasing
potential.  Weight files contain the values of the reaction coordinates
as well as the natural logarithms of the weights needed to recover a canonical
Boltzmann distribution. 


1.  GAMUS DIHE 

Define a dihedral angle as a reaction coordinate for GAMUS.


2.  GAMUS INIT

Initializes the GAMUS potential.  TEMP specifies the temperature of the 
simulation.  RUNI and WUNI give unit numbers for the biasing potential to be 
used and the energy-coordinate file to be written during the simulation.
An initial potential file is needed for the first GAMUS simulation; this
should specify 0 gaussians and a value of ln(gamma) equal to -D*ln(360)
where D is the number of reaction coordinates.
IFRQ gives the frequency of recording the values of the reaction coordinates.
This should be infrequent enough that consecutive values are uncorrelated
(about 0.2 ps in most cases).


3. GAMUS CLEAR

Turns GAMUS off and clears all associated data structures.  


4. GAMUS REWEight

Performs MARE and the E-M algorithm in order to calculate a new biasing
potential from previous potentials and energy-coordinate files. The biasing
potential files must be opened as a continuous sequence of NSIM units starting
with unit PUNI; likewise, the energy-coordinate files must be opened as
a continuous sequence of units starting with QUNI.

Keyword  Default   Purpose

NSIM     n/a       Number of previous simulations to include

PUNI     n/a       Initial biasing potential unit 

QUNI     n/a       Initial energy-coordinate file unit

NGAUss   n/a       Number of Gaussians to be used (should gradually increase
                   with the number of simulations.  It is suggested to add
		   about 1-2 Gaussians to the fit per ns of simulation.)

NREF     20        Number of refinements using the E-M algorithm from randomly
                   chosen starting points. 

NITR     200       Maximum number of iterations of the E-M algorithm per
                   refinement

SIGMa    5         Minimum size of a Gaussian in any direction (in degrees)

SMAX     90        Maximum size of a Gaussian in any direction (in degrees)

GAMMa    -1000     Cutoff for the Bayesian prior (see below)

WEIGht   -1        Unit number for writing weights of the frames (as their
                   natural logarithms) together with values of the reaction
		   coordinates

WORK     -1        Unit number for writing work values input into MARE
                   (primarily for debugging purposes) 

The E-M optimization can have a tendency for Gaussians to collapse around 
individual data points. For this reason, a minimum size of the Gaussian 
in each direction is imposed. When a Gaussian collapses to this minimum 
in all directions, the message "gaussian number N is of minimum size in 
all directions" is produced.  The SMAX option is used to impose a maximum size 
of a Gaussian in order to prevent periodicity assumptions from being violated.
If a large number of these messages appear, it may mean that too many
Gaussians are being used for the fit, or that the cap on ln(gamma) is too
small (see below).  

The value of gamma is chosen based on the probability of obtaining the least 
probable sampled data point.  A cap on the value of ln(gamma) can be used to 
restrict the extrapolation of the free energy surface in order to avoid deep 
artificial minima in the free energy surface (as described in Maragakis et al. 
JPC B 113, 4664 (2009)).  This is recommended if more than two reaction 
coordinates are used.  The value of the cap should be chosen so that the free 
energy differences ln(Zi/Z0) average around zero.  It should be noted that 
the biasing potential is limited to kT ln(gamma) so setting too low a cap 
can limit the part of the free energy surface that is sampled.


5. GAMUS FIT

Uses the E-M algotithm to fit weighted values of the reaction coordinates
found in the unit specified by DATA to a mixture of Gaussian distributions.
The NGAUSS, NREF, NITR, SIGMA and GAMMA parameters are specified as described
above for GAMUS REWEight. The first column in the file must be for the natural
logarithms of the weights; the remaining columns are for the values of the
reaction coordinates.


6. GAMUS WRITE

Writes the current GAMUS potential to a unit specified by UNIT. This usually
follows a GAMUS REWEIGHT or GAMUS FIT command that generates a new GAMUS
potential (see above).


7. GAMUS INFO

Prints out the weights and mean values of all the Gaussians in the current
GAMUS potential. If the VERBose option is specified, each variance-covariance
matrix will also be diagonalized to find the principal axes of each Gaussian
and the width of the Gaussian along each axis.



File: GAMUS -=- Node: Caveats
Up: Top -=- Previous: Function -=- Next: Installation



                                Caveats

1. The expectation-maximization algorithm fits the probability density, not 
the free energy.  Since the probability density is lower near free energy 
barriers, and since the free energy is proportional to the logarithm of 
the probability density, the statistical errors in estimating the free energy 
surface are greater near barriers.  Consequently, GAMUS does a much better job 
of identifying and locating free energy basins and of determining their shapes 
and relative free energies than it does of estimating free energy barriers.

2. The time necessary to fit a set of Gaussians increases linearly with the
number of Gaussians to be fitted.  Consequently, fitting can become very
expensive if many Gaussians are used.  In addition, using too many Gaussians
can result in some of the Gaussians collapsing around individual data points
("gaussian number N is of minimum size in all directions" message).
Consequently, it is suggested to add Gaussians slowly during the run;
a rate of about 1-2 additional Gaussians per ns of 
simulation is recommended.

3. Because of the extrapolation involved in determining the biasing potential 
from the free energy surface, it is possible for the fitting to introduce 
artificial minima in the free energy surface.  These artificial minima 
should go away in long enough simulations.  Adjusting the cap value of 
ln(gamma) can help with this, as described above.



File: GAMUS -=- Node: Installation
Up: Top -=- Previous: Caveats -=- Next: Examples


                             Installation

GAMUS requires an implementation of LAPACK in order to perform linear algebra 
as part of the E-M and MARE algorithms.  

To compile CHARMM with GAMUS included add the keyword GAMUS to the install.com
command line, for example:

./install.com gnu large gfortran x86_64 gamus

Since the name and path of the LAPACK library can vary from one system to
another, the installer will prompt for the necessary link options.
These options will be added to the link command line.  



File: GAMUS -=- Node: Examples
Up: Top -=- Previous: Installation -=- Next: Top



                                Examples

The main loop for cycling through steps 1-3 above is encoded in the script. 
During each cycle molecular dynamics is invoked twice: once for equilibration 
and once for sampling.  GAMUS is also invoked twice: once for each section of 
molecular dynamics.  At the end of the molecular dynamics simulations,
the GAMUS REWEIGHT command is used to reweight the data from all previous
simulations and fit this to a mixture of Gaussians using the E-M algorithm.
This results in a new GAMUS potential, which is used for the next cycle of
molecular dynamics simulation.

The script structure is as follows:

! first read in the force field, PSF, set up implicit or explicit solvent, etc.
! write an initial GAMUS potential, specifying the number of reaction
! coordinates (4 in this case)

calc initgamma = -4 * ln( 360.0 )
open unit 1 write formatted name @9gamus-1.dat
write title unit 1
* 0 4  
* @initgamma
*
close unit 1


set index = 1
label gamusloop

calc oldindex = @index - 1
open unit 1 read formatted name @9restart-col-@oldindex.rst
read coor dynr curr unit 1
close unit 1

  ! here we equilibrate
    open unit 29 read card name @9restart-col-@oldindex.rst
    open unit 30 write card name @9restart-eq-@index.rst
    
    !this file contains the GAMUS potential
    open unit 44 read card name @9gamus-@index.dat
    ! in this file CHARMM will record biasing potential and reaction
    ! coordinate values encountered during the simulation.
    ! We write to a different file from "gamuse..." to keep this from being
    ! used later by the MARE and GMM fits
    open unit 45 write card name @9gamusex-@INDEX-q-@INDEX.dat 
    open unit 46 write unform name @9gamus-eq-@index.dcd
    ! This activates the GAMUS biasing potential and specifies the reaction
    ! coordinate (4 dihedral angles)
    gamus init temp @temp runi 44 wuni 45 ifrq @gamusfreq
    gamus dihe pep 1 c pep 2 n pep 2 ca pep 2 c  
    gamus dihe pep 2 n pep 2 ca pep 2 c pep 3 n
    gamus dihe pep 2 c pep 3 n pep 3 ca pep 3 c
    gamus dihe pep 3 n pep 3 ca pep 3 c pep 4 n
    dynamics ...  iunrea 29 iunwri 30 iuncrd 46 !perform dynamics for equilibration
    close unit 29
    close unit 30
    close unit 44
    close unit 45
    close unit 46
    gamus clear
    ! and now we collect statistics 

    open unit 29 read card name @9restart-eq-@index.rst
    open unit 30 write card name @9restart-col-@index.rst
    ! We do everything over again, this time writing in a file that is
    ! used by the MARE and GMM fits.
    ! The definition of the reaction coordinate must match the one given above.
    open unit 44 read card name @9gamus-@index.dat
    open unit 45 write card name @9gamuse-@INDEX-q-@INDEX.dat
    open unit 46 write unform name @9gamus-col-@index.dcd
    gamus init temp 300.0 runi 44 wuni 45 ifrq @gamusfreq
    gamus dihe pep 1 c pep 2 n pep 2 ca pep 2 c  
    gamus dihe pep 2 n pep 2 ca pep 2 c pep 3 n
    gamus dihe pep 2 c pep 3 n pep 3 ca pep 3 c
    gamus dihe pep 3 n pep 3 ca pep 3 c pep 4 n
 !   prnlev -6
    dynamics ... iunrea 29 iunwri 30 iuncrd 46  !perform dynamics for sampling
    close unit 29
    close unit 30
    close unit 44
    close unit 45
    close unit 46


	! now use the new commands to reweight the potential
	calc newindex = @index + 1
	calc ngauss = 4 + @index !This calculates the number of Gaussians to be used for the fit.
	set i = 1
	label loop
     		calc u1 = 10 + @i
     		calc u2 = 100 + @i
     		open unit @u1 read formatted name @9gamus-@i.dat
     		open unit @u2 read formatted name @9gamuse-@i-q-@i.dat
	incr i by 1
	if i .le. @index then goto loop

	open unit 7 write formatted name @9weights-@index
	open unit 8 write formatted name @9gamus-@newindex.dat
	gamus reweight nsim @index puni 11 quni 101 ngauss @ngauss nref 4 nitr 200 sigma 5.0 gamma -25.0 weights 7
	gamus write unit 8
	close unit 8
	close unit 7

	set i = 1
	label loop2
     		calc u1 = 10 + @i
     		calc u2 = 100 + @i
     		close unit @u1
     		close unit @u2
	incr i by 1
	if i .le. @index then goto loop2
	gamus clear

incr index by 1
if index .le. 4 then goto gamusloop

gamus clear

NIH Helix/Biowulf Systems
charmm.org Homepage