/**
\page cineca Cineca tutorial

\authors Max Bonomi, stealing most of the material from other tutorials.
         Alejandro Gil Ley is acknowledged for beta-testing this tutorial.
\date November 20, 2015

This document describes the PLUMED tutorial held at CINECA, November 2015.
The aim of this tutorial is to learn how to use PLUMED to analyze molecular
dynamics (MD) simulations on-the-fly, to analyze existing trajectories, and to
perform enhanced sampling. Although the presented input files are correct,
the users are invited to **refer to the literature to understand how the
parameters of enhanced sampling methods should be chosen in a real application.**

Users are also encouraged to follow the links to the full PLUMED reference documentation
and to wander around in the manual to discover the many available features
and to do the other, more complete, tutorials. Here 
we are going to present only a very narrow selection of methods.

All the tests here are performed on a toy system, alanine dipeptide, simulated
in vacuum using the AMBER99SB force field. 
Simulations are carried out with GROMACS 5.0.4, which is here assumed to be already patched with PLUMED 2.2 and properly installed.
However, these examples could be easily converted to other MD software.

\section cineca-resources Resources

All the GROMACS input files and analysis scripts are provided in this
<a href="tutorial-resources/cineca.tar.gz" download="cineca.tar.gz"> tarball</a>.
Users are expected to write PLUMED input files based on the instructions below.

We can start by downloading the tarball and uncompressing it:

\verbatim
> tar xzvf cineca.tar.gz 
\endverbatim

\warning Exercises should be run inside the newly-created cineca directory,
by creating new subdirectories, one per exercise.

\section cineca-toymodel Alanine dipeptide: our toy model

In this tutorial we will play with alanine dipeptide (see Fig. \ref cineca-1-ala-fig).
This rather simple molecule is useful to benchmark data analysis and free-energy methods.
This system is a nice example because it presents two metastable states separated by a high free-energy barrier. 
It is conventional use to characterize the two states in terms of Ramachandran dihedral angles, which are denoted with \f$ \Phi \f$ and \f$ \Psi \f$ in Fig. \ref cineca-1-transition-fig .

\anchor cineca-1-ala-fig
\image html belfast-2-ala.png "The molecule of the day: alanine dipeptide."

\anchor cineca-1-transition-fig
\image html belfast-2-transition.png "Two metastable states of alanine dipeptide are characterized by their Ramachandran dihedral angles."

\section cineca-monitor Monitoring collective variables

The main goal of PLUMED is to compute collective variables, which are complex descriptors of a system than can be used
to analyze a conformational change or a chemical reaction. This can be done either on-the-fly, that is during
MD, or a posteriori, using PLUMED as a post-processing tool. In both cases
one should create an input file with a specific PLUMED syntax. A sample input file is below:

\verbatim
# compute distance between atoms 1 and 10
d: DISTANCE ATOMS=1,10
# create a virtual atom in the center between atoms 20 and 30
center: CENTER ATOMS=20,30
# compute torsional angle between atoms 1,10,20 and center
phi: TORSION ATOMS=1,10,20,center
# print d and phi every 10 step
PRINT ARG=d,phi STRIDE=10
\endverbatim
(see \ref DISTANCE, \ref CENTER, \ref TORSION, and \ref PRINT)

PLUMED works using kJ/nm/ps as energy/length/time units. This can be personalized using \ref UNITS.
Notice that variables should be given a name (in the example above, `d`, and `phi`), which is
then used to refer to these variables. Lists of atoms should be provided as 
comma separated numbers, with no space. Virtual atoms can be created and assigned a name for later use.
You can find more information on the PLUMED syntax
at \ref Syntax page of the manual. The complete documentation for all the supported
collective variables can be found at the \ref colvarintro page.

\subsection cineca-monitor-of Exercise 1: on-the-fly analysis

Here we will run a plain MD simulation on alanine dipeptide and compute two torsional angles on-the-fly.
GROMACS needs a .tpr file, which is a binary file containing initial positions
as well as force-field parameters. 
For this tutorial, it is sufficient to use the .tpr files provided in the SETUP directory of the package: 
- topolA.tpr - setup in vacuum, initialized in state A
- topolB.tpr - setup in vacuum, initialized in state B

However, we also provide .gro, .mdp, and .top files,
that can be modified and used to generate a new .tpr file.

GROMACS can be run (interactively) using the following command:
\verbatim
> gmx_mpi mdrun -s ../SETUP/topolA.tpr -nsteps 10000 -x traj.xtc
\endverbatim
The nsteps flags can be used to change the number of timesteps and topolA.tpr is the name of the tpr file.
While running, GROMACS will produce a md.log file, with log information, and a traj.xtc file, with a binary trajectory.
The trajectory can be visualized with VMD using:
\verbatim
> vmd confout.gro traj.xtc
\endverbatim

To activate PLUMED during a GROMACS MD simulation, you need to add the -plumed flag
\verbatim
> gmx_mpi mdrun -s ../SETUP/topolA.tpr -nsteps 10000 -plumed plumed.dat -x traj.xtc
\endverbatim
Here plumed.dat is the name of the PLUMED input file. Notice that PLUMED will write
information in the md.log that could be useful to verify if the simulation has been set up properly.

In this exercise, we will run a plain MD simulation and monitor the \f$\Phi\f$ and \f$\Psi\f$ dihedral angles
on-the-fly. In order to do so, you need to prepare the following PLUMED input file:

\verbatim
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17
PRINT ARG=phi,psi STRIDE=100 FILE=COLVAR
\endverbatim
(see \ref TORSION and \ref PRINT)

PLUMED is going to compute the collective variables only when necessary, that is, in this case, every 100 steps.
This is not very relevant for simple variables such as torsional angles, but provides a significant speed-up
when using expensive collective variables.

PLUMED will write a textual file named COLVAR containing three columns: simulation time, \f$\Phi\f$ and \f$\Psi\f$.
Results can be plotted using gnuplot:
\verbatim
> gnuplot
# this shows phi as a function of time
gnuplot> plot "COLVAR" u 2
# this shows psi as a function of time
gnuplot> plot "COLVAR" u 3
# this shows psi as a function of phi
gnuplot> plot "COLVAR" u 2:3
\endverbatim

Now try to do the same using the two different initial configurations that we provided (`topolA.tpr` and `topolB.tpr`).
Results from 200ps (100000 steps) trajectories in vacuum are shown in Figure \ref cineca-ala-traj.

\anchor cineca-ala-traj
\image html munster-ala-traj.png "(phi,psi) scatter plot obtained starting from two different structures. Simulation performed in vacuum."

Notice that the result depends heavily on the starting structure.
For the simulation in vacuum, the two free-energy minima are
separated by a large barrier so that the system cannot cross it in such short simulation time.
Also notice that the two clouds are well separated, indicating that these two collective variables
are appropriate to properly distinguish the two minima.

As a final comment, consider that if you run twice the same calculation in the same directory, you might overwrite the
output files. GROMACS takes automatic backup of these files, and PLUMED does it as well. In case you are restarting a simulation,
you can add the keyword \ref RESTART at the beginning of the PLUMED input file. This will tell PLUMED to *append* files
instead of taking a backup copy.

\subsection cineca-monitor-dr Exercise 2: analysis with the driver tool

Imagine you have already run a simulation, with or without PLUMED. You might want to compute the collective
variables a posteriori, i.e. from the trajectory file. You can do this by using the PLUMED executable on the command line.
Type
\verbatim
> plumed driver --help
\endverbatim
to have an idea of the possible options. See \ref driver for the full documentation.

Here we will use the driver the compute \f$\Phi\f$ and \f$\Psi\f$ on the trajectory previously generated. Let's assume
the trajectory is named `traj.xtc`. You should prepare a PLUMED input file named `analysis.dat` as:
\verbatim
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17
PRINT ARG=phi,psi FILE=COLVAR_ANALYSIS
\endverbatim
(see \ref TORSION and \ref PRINT)
Notice that typically when using the driver we do not provide a STRIDE keyword to PRINT. This implies "print at every step" which,
analyzing a trajectory, means "print for all the available snapshots". Then, you can use the following
command:
\verbatim
> plumed driver --mf_xtc traj.xtc --plumed analysis.dat
\endverbatim
Notice that PLUMED has no way to now the value of physical time from the trajectory. If you want physical time to
be printed in the output file you should give more information to the driver, e.g.:
\verbatim
> plumed driver --mf_xtc traj.xtc --plumed analysis.dat --timestep 0.002 --trajectory-stride 1000
\endverbatim
(see \ref driver)

In this case we inform the driver that the `traj.xtc` file was produced in a run with a timestep of 0.002 ps and
saving a snapshop every 1000 timesteps.

You might want to analyze a different collective variable, such as the gyration radius.
The gyration radius tells how extended is a molecule in space.
You can do it with the following PLUMED input file

\verbatim
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17

heavy: GROUP ATOMS=1,5,6,7,9,11,15,16,17,19
gyr: GYRATION ATOMS=heavy

# the same could have been achieved with
# gyr: GYRATION ATOMS=1,5,6,7,9,11,15,16,17,19

PRINT ARG=phi,psi,gyr FILE=analyze
\endverbatim
(see \ref TORSION, \ref GYRATION, \ref GROUP, and \ref PRINT)

Now try to compute the time serie of the gyration radius.

\section cineca-biasing Biasing collective variables

We have seen that PLUMED can be used to compute collective variables. However, PLUMED
is most often use to add forces on the collective variables. To this aim,
we have implemented a variety of possible biases acting on collective variables.
A bias works in a manner conceptually similar to the \ref PRINT command, taking as argument
one or more collective variables. However, here the STRIDE is usually omitted (that is equivalent to setting it to 1), which means
that forces are applied at every timestep. Starting from PLUMED 2.2 you can change the STRIDE also for bias potentials,
but that's another story.
In the following we will see 
how to build an adaptive bias potential with metadynamics and how to apply harmonic restraints. The complete documentation for
all the biasing methods available in PLUMED can be found at the \ref Bias page.

\subsection cineca-biasing-me Metadynamics

\hidden{Summary of theory}
\subsubsection cineca-biasing-me-theory Summary of theory
In metadynamics, an external history-dependent bias potential is constructed in the space of 
a few selected degrees of freedom \f$ \vec{s}({q}) \f$, generally called collective variables (CVs) \cite metad.
This potential is built as a sum of Gaussians deposited along the trajectory in the CVs space:

\f[
V(\vec{s},t) = \sum_{ k \tau < t} W(k \tau)
\exp\left(
-\sum_{i=1}^{d} \frac{(s_i-s_i({q}(k \tau)))^2}{2\sigma_i^2}
\right).
\f]

where \f$ \tau \f$ is the Gaussian deposition stride, 
\f$ \sigma_i \f$ the width of the Gaussian for the ith CV, and \f$ W(k \tau) \f$ the
height of the Gaussian. The effect of the metadynamics bias potential is to push the system away 
from local minima into visiting new regions of the phase space. Furthermore, in the long
time limit, the bias potential converges to minus the free energy as a function of the CVs:

\f[
V(\vec{s},t\rightarrow \infty) = -F(\vec{s}) + C.
\f]

In standard metadynamics, Gaussians of constant height are added for the entire course of a 
simulation. As a result, the system is eventually pushed to explore high free-energy regions
and the estimate of the free energy calculated from the bias potential oscillates around
the real value. 
In well-tempered metadynamics \cite Barducci:2008, the height of the Gaussian 
is decreased with simulation time according to:

\f[
 W (k \tau ) = W_0 \exp \left( -\frac{V(\vec{s}({q}(k \tau)),k \tau)}{k_B\Delta T} \right ),
\f]

where \f$ W_0 \f$ is an initial Gaussian height, \f$ \Delta T \f$ an input parameter 
with the dimension of a temperature, and \f$ k_B \f$ the Boltzmann constant. 
With this rescaling of the Gaussian height, the bias potential smoothly converges in the long time limit,
but it does not fully compensate the underlying free energy:

\f[
V(\vec{s},t\rightarrow \infty) = -\frac{\Delta T}{T+\Delta T}F(\vec{s}) + C.
\f]

where \f$ T \f$ is the temperature of the system.
In the long time limit, the CVs thus sample an ensemble
at a temperature \f$ T+\Delta T \f$ which is higher than the system temperature \f$ T \f$.
The parameter \f$ \Delta T \f$ can be chosen to regulate the extent of free-energy exploration:
 \f$ \Delta T = 0\f$ corresponds to standard MD, \f$ \Delta T \rightarrow \infty \f$ to standard
metadynamics. In well-tempered metadynamics literature and in PLUMED, you will often encounter
the term "biasfactor" which is the ratio between the temperature of the CVs (\f$ T+\Delta T \f$) 
and the system temperature (\f$ T \f$):

\f[
\gamma = \frac{T+\Delta T}{T}.
\f]

The biasfactor should thus be carefully chosen in order for the relevant free-energy barriers to be crossed
efficiently in the time scale of the simulation.
 
Additional information can be found in the several review papers on metadynamics 
\cite gerv-laio09review \cite WCMS:WCMS31 \cite WCMS:WCMS1103.

\endhidden

If you do not know exactly where you would like your collective variables to go,
and just know (or suspect) that some variables have large free-energy barriers
that hinder some conformational rearrangement or some chemical reaction,
you can bias them using metadynamics. In this way, a time dependent,
adaptive potential will be constructed that tends to disfavor visited configurations
in the collective-variable space. The bias is usually built as a sum of
Gaussian deposited in the already visited states.

\subsubsection cineca-welltemp-phi Exercise 3

In this exercise, we will run a well-tempered metadynamics simulation on alanine dipeptide in vacuum, using as CV the
backbone dihedral angle phi. In order to run this simulation we need to prepare the PLUMED input file (plumed.dat) as follows.

\verbatim
# set up two variables for Phi and Psi dihedral angles 
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17
#
# Activate well-tempered metadynamics in phi depositing 
# a Gaussian every 500 time steps, with initial height equal 
# to 1.2 kJoule/mol, biasfactor equal to 10.0, and width to 0.35 rad

METAD ...
LABEL=metad
ARG=phi
PACE=500
HEIGHT=1.2
SIGMA=0.35
FILE=HILLS
BIASFACTOR=10.0
TEMP=300.0
GRID_MIN=-pi
GRID_MAX=pi
GRID_SPACING=0.1
... METAD

# monitor the two variables and the metadynamics bias potential
PRINT STRIDE=10 ARG=phi,psi,metad.bias FILE=COLVAR

\endverbatim
(see \ref TORSION, \ref METAD, and \ref PRINT).

The syntax for the command \ref METAD is simple. 
The directive is followed by a keyword ARG followed by the labels of the CVs
on which the metadynamics potential will act.
The keyword PACE determines the stride of Gaussian deposition in number of time steps,
while the keyword HEIGHT specifies the height of the Gaussian in kJoule/mol. For each CVs, one has
to specified the width of the Gaussian by using the keyword SIGMA. Gaussian will be written
to the file indicated by the keyword FILE. 

The bias potential will be stored on a grid, whose boundaries are specified by the keywords GRID_MIN and GRID_MAX.
Notice that you should provide either the number of bins for every collective variable (GRID_BIN) or
the desired grid spacing (GRID_SPACING). In case you provide both PLUMED will use
the most conservative choice (highest number of bins) for each dimension.
In case you do not provide any information about bin size (neither GRID_BIN nor GRID_SPACING)
and if Gaussian width is fixed PLUMED will use 1/5 of the Gaussian width as grid spacing.
This default choice should be reasonable for most applications.

Once the PLUMED input file is prepared, one has to run GROMACS with the option to activate PLUMED and
read the input file:

\verbatim
gmx_mpi mdrun -s ../SETUP/topolA.tpr -plumed plumed.dat -nsteps 5000000 -x traj.xtc
\endverbatim

During the metadynamics simulation, PLUMED will create two files, named COLVAR and HILLS.
The COLVAR file contains all the information specified by the PRINT command, in this case
the value of the CVs every 10 steps of simulation, along with the current value of the metadynamics bias potential. 
We can use COLVAR to visualize the behavior of the CV during the simulation:

\anchor cineca-metad-phi-fig
\image html munster-metad-phi.png "Time evolution of the CV phi during the first 2 ns of a metadynamics simulation of alanine dipeptide in vacuum."

By inspecting Figure \ref cineca-metad-phi-fig, we can see that the system is initialized in one of the two metastable
states of alanine dipeptide. After a while (t=0.1 ns), the system is pushed
by the metadynamics bias potential to visit the other local minimum. As the simulation continues,
the bias potential fills the underlying free-energy landscape, and the system is able to diffuse in the
entire phase space.

The HILLS file contains a list of the Gaussians deposited along the simulation.
If we give a look at the header of this file, we can find relevant information about its content:

\verbatim
#! FIELDS time phi psi sigma_phi sigma_psi height biasf
#! SET multivariate false
#! SET min_phi -pi
#! SET max_phi pi
#! SET min_psi -pi
#! SET max_psi pi
\endverbatim 

The line starting with FIELDS tells us what is displayed in the various columns of the HILLS file:
the time of the simulation, the value of phi and psi, the width of the Gaussian in phi and psi,
the height of the Gaussian, and  the biasfactor.
We can use the HILLS file to visualize the decrease of the Gaussian height during the simulation,
according to the well-tempered recipe:

\anchor cineca-metad-phihills-fig
\image html munster-metad-phihills.png "Time evolution of the Gaussian height."

If we look carefully at the scale of the y-axis, we will notice that in the beginning the value
of the Gaussian height is higher than the initial height specified in the input file, which should be 1.2 kJoule/mol.
In fact, this column reports the height of the Gaussian rescaled by the pre-factor that
in well-tempered metadynamics relates the bias potential to the free energy. In this way, when
we will use \ref sum_hills, the sum of the Gaussians deposited will directly provide the free-energy,
without further rescaling needed (see below).

One can estimate the free energy as a function of the metadynamics CVs directly from the metadynamics
bias potential. In order to do so, the utility \ref sum_hills should be used to sum the Gaussians
deposited during the simulation and stored in the HILLS file.  
To calculate the free energy as a function of phi, it is sufficient to use the following command line:

\verbatim
plumed sum_hills --hills HILLS
\endverbatim

The command above generates a file called fes.dat in which the free-energy surface as function
of phi is calculated on a regular grid. One can modify the default name for the free energy file,
as well as the boundaries and bin size of the grid, by using the following options of \ref sum_hills :

\verbatim
--outfile - specify the outputfile for sumhills
--min - the lower bounds for the grid
--max - the upper bounds for the grid
--bin - the number of bins for the grid
--spacing - grid spacing, alternative to the number of bins
\endverbatim 

The result should look like this:

\anchor cineca-metad-phifes-fig
\image html munster-metad-phifes.png "Estimate of the free energy as a function of the dihedral phi from a 10ns-long well-tempered metadynamics simulation."

To assess the convergence of a metadynamics simulation, one can calculate the estimate of the free energy as a function
of simulation time. At convergence, the reconstructed profiles should be similar.
The option --stride should be used to give an estimate of the free energy every N Gaussians deposited, and
the option --mintozero can be used to align the profiles by setting the global minimum to zero.
If we use the following command line:

\verbatim
plumed sum_hills --hills HILLS --stride 100 --mintozero
\endverbatim

one free energy is calculated every 100 Gaussians deposited, and the global minimum is set to zero in all profiles.
The resulting plot should look like the following:

\anchor cineca-metad-phifest-fig
\image html munster-metad-phifest.png "Estimates of the free energy as a function of the dihedral phi calculated every 100 Gaussians deposited."

To assess the convergence of the simulation more quantitatively, we can calculate the free-energy difference between the two
local minima of the free energy along phi as a function of simulation time.
We can use following script to integrate the multiple free-energy profiles in the two basins defined
by the following regions in phi space: basin A, -3<phi<-1, basin B, 0.5<phi<1.5.

\verbatim
# number of free-energy profiles
nfes= # put here the number of profiles
# minimum of basin A
minA=-3
# maximum of basin A
maxA=1
# minimum of basin B
minB=0.5
# maximum of basin B
maxB=1.5
# temperature in energy units
kbt=2.5

for((i=0;i<nfes;i++))
do
 # calculate free-energy of basin A
 A=`awk 'BEGIN{tot=0.0}{if($1!="#!" && $1>min && $1<max)tot+=exp(-$2/kbt)}END{print -kbt*log(tot)}' min=${minA} max=${maxA} kbt=${kbt} fes_${i}.dat`
 # and basin B
 B=`awk 'BEGIN{tot=0.0}{if($1!="#!" && $1>min && $1<max)tot+=exp(-$2/kbt)}END{print -kbt*log(tot)}' min=${minB} max=${maxB} kbt=${kbt} fes_${i}.dat`
 # calculate difference
 Delta=$(echo "${A} - ${B}" | bc -l)
 # print it
 echo $i $Delta
done

\endverbatim

Please notice that nfes should be set to the 
number of profiles (free-energy estimates at different times of the simulation) generated by the option --stride of \ref sum_hills.

\anchor cineca-metad-phifes-difft-fig
\image html munster-metad-phifes-difft.png "Free-energy difference between basin A and B as a function of simulation time."

This analysis, along with the observation of the diffusive behavior in the CVs space, suggest that the simulation is converged.

\subsubsection cineca-welltemp-psi Exercise 4

In this exercise, we will run a well-tempered metadynamics simulation on alanine dipeptide in vacuum, using as CV the
backbone dihedral angle psi. In order to run this simulation we need to prepare the PLUMED input file (plumed.dat) as follows.

\verbatim
# set up two variables for Phi and Psi dihedral angles 
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17
#
# Activate well-tempered metadynamics in psi depositing 
# a Gaussian every 500 time steps, with initial height equal 
# to 1.2 kJoule/mol, biasfactor equal to 10.0, and width to 0.35 rad

METAD ...
LABEL=metad
ARG=psi
PACE=500
HEIGHT=1.2
SIGMA=0.35
FILE=HILLS
BIASFACTOR=10.0
TEMP=300.0
GRID_MIN=-pi
GRID_MAX=pi
GRID_SPACING=0.1
... METAD

# monitor the two variables and the metadynamics bias potential
PRINT STRIDE=10 ARG=phi,psi,metad.bias FILE=COLVAR

\endverbatim
(see \ref TORSION, \ref METAD, and \ref PRINT).

Once the PLUMED input file is prepared, one has to run GROMACS with the option to activate PLUMED and
read the input file. We will submit this job using the PBS queue system, as done for the previous
exercise.

As we did in the previous exercise, we can use COLVAR to visualize the behavior of the CV during the simulation.
Here we will plot at the same time the evolution of the metadynamics CV psi and of the other dihedral phi:

\anchor cineca-metad-psi-phi-fig
\image html munster-metad-psi-phi.png "Time evolution of the dihedrals phi and psi during a 10-ns long metadynamics simulation using psi as CV."

By inspecting Figure \ref cineca-metad-psi-phi-fig, we notice that something different happened compared to the previous exercise.
At first the behavior of psi looks diffusive in the entire CV space. However, around t=1 ns, psi seems trapped in a region of the CV space
in which it was previously diffusing without problems. The reason is that the non-biased CV phi after a while has jumped into a different local minima.
Since phi is not directly biased, one has to wait for this (slow) degree of freedom to equilibrate before the free energy along psi can converge.
Try to repeat the analysis done in the previous exercise (calculate the estimate of the free energy as a function of time and monitor
the free-energy difference between basins) to assess the convergence of this metadynamics simulation.

\subsection cineca-biasing-re Restraints

\hidden{Biased sampling theory}
\subsubsection cineca-biased-sampling-theory Biased sampling theory 

A system at temperature \f$ T\f$ samples 
conformations from the canonical ensemble:
\f[
  P(q)\propto e^{-\frac{U(q)}{k_BT}}
\f].
Here \f$ q \f$ are the microscopic coordinates and \f$ k_B \f$ is the Boltzmann constant.
Since \f$ q \f$ is a highly dimensional vector, it is often convenient to analyze it
in terms of a few collective variables (see \ref belfast-1 , \ref belfast-2 , and \ref belfast-3 ).
The probability distribution for a CV \f$ s\f$ is 
\f[
  P(s)\propto \int dq  e^{-\frac{U(q)}{k_BT}} \delta(s-s(q))
\f]
This probability can be expressed in energy units as a free energy landscape \f$ F(s) \f$:
\f[
  F(s)=-k_B T \log P(s)
\f].

Now we would like to modify the potential by adding a term that depends on the CV only.
That is, instead of using \f$ U(q) \f$, we use \f$ U(q)+V(s(q))\f$.
There are several reasons why one would like to introduce this potential. One is to
avoid that the system samples some un-desired portion of the conformational space.
As an example, imagine  that you want to study dissociation of a complex of two molecules.
If you perform a very long simulation you will be able to see association and dissociation.
However, the typical time required for association will depend on the size of the simulation
box. It could be thus convenient to limit the exploration to conformations where the 
distance between the two molecules is lower than a given threshold. This could be done
by adding a bias potential on the distance between the two molecules.
Another example
is the simulation of a portion of a large molecule taken out from its initial context.
The fragment alone could be unstable, and one might want to add additional
potentials to keep the fragment in place. This could be done by adding
a bias potential on some measure of the distance from the experimental structure
(e.g. on root-mean-square deviation).

Whatever CV we decide to bias, it is very important to recognize which is the
effect of this bias and, if necessary, remove it a posteriori.
The biased distribution  of the CV will be
\f[
  P'(s)\propto \int dq  e^{-\frac{U(q)+V(s(q))}{k_BT}} \delta(s-s(q))\propto e^{-\frac{V(s(q))}{k_BT}}P(s)
\f]
and the biased free energy landscape
\f[
  F'(s)=-k_B T \log P'(s)=F(s)+V(s)+C
\f]
Thus, the effect of a bias potential on the free energy is additive. Also notice the presence
of an undetermined constant \f$ C \f$. This constant is irrelevant for what concerns free-energy differences
and barriers, but will be important later when we will learn the weighted-histogram method.
Obviously the last equation can be inverted so as to obtain the original, unbiased free-energy 
landscape from the biased one just subtracting the bias potential
\f[
  F(s)=F'(s)-V(s)+C
\f]

Additionally, one might be interested in recovering the distribution of an arbitrary
observable. E.g., one could add a bias on the distance between two molecules and be willing to
compute the unbiased distribution of some torsional angle. In this case
there is no straightforward relationship that can be used, and one has to go back to
the relationship between the microscopic probabilities:
\f[
  P(q)\propto P'(q) e^{\frac{V(s(q))}{k_BT}}
\f]
The consequence of this expression is that one can obtained any kind of unbiased
information from a biased simulation just by weighting every sampled conformation
with a weight
\f[
  w\propto e^{\frac{V(s(q))}{k_BT}}
\f]
That is, frames that have been explored
in spite of a high (disfavoring) bias potential \f$ V \f$ will be counted more
than frames that has been explored with a less disfavoring bias potential.

\endhidden

\hidden{Umbrella sampling theory}
\subsubsection cineca-umbrella-sampling-theory Umbrella sampling theory

Often in interesting cases the free-energy landscape has several local minima. If these
minima have free-energy differences that are on the order of a few times \f$k_BT\f$
they might all be relevant. However, if they are separated by a high saddle point
in the free-energy landscape (i.e. a low probability region) than the transition
between one and the other will take a lot of time and these minima will correspond
to metastable states. The transition between one minimum and the other could require
a time scale which is out of reach for MD. In these situations,
one could take inspiration from catalysis and try to favor in a controlled manner
the conformations corresponding to the transition state.

Imagine that you know since the beginning the shape of the free-energy landscape
\f$ F(s) \f$ as a function of one CV \f$ s \f$. If you perform a MD 
simulation using a bias potential which is exactly equal to \f$ -F(s) \f$,
the biased free-energy landscape will be flat and barrierless.
This potential acts as an "umbrella" that helps you to safely cross
the transition state in spite of its high free energy.

It is however difficult to have an a priori guess of the free-energy landscape.
We will see later how adaptive techniques such as metadynamics (\ref belfast-6) can be used
to this aim. Because of this reason, umbrella sampling is often used
in a slightly different  manner.

Imagine that you do not know the exact height of the free-energy barrier
but you have an idea of where the barrier is located. You could try to just
favor the sampling of the transition state by adding a harmonic restraint 
on the CV, e.g. in the form
\f[
  V(s)=\frac{k}{2} (s-s_0)^2
\f].
The sampled distribution will be 
\f[
  P'(q)\propto P(q) e^{\frac{-k(s(q)-s_0)^2}{2k_BT}}
\f]
For large values of \f$ k \f$, only points close to \f$ s_0 \f$ will be explored. It is thus clear
how one can force the system to explore only a predefined region of the space adding such a restraint.
By combining simulations performed with different values of \f$ s_0 \f$, one could
obtain a continuous set of simulations going from one minimum to the other crossing the transition state.
In the next section we will see how to combine the information from these simulations.

\endhidden

If you want to just bring a collective variables to a specific value, you
can use a simple restraint.
Let's imagine that we want to force the \f$\Phi\f$ angle to visit a region close to
\f$\Phi=\pi/2\f$. We can do it adding a restraint in \f$\Phi\f$, with the following input
\verbatim
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17
res: RESTRAINT ARG=phi AT=0.5pi KAPPA=5
PRINT ARG=phi,psi,res.bias
\endverbatim
(see \ref TORSION, \ref RESTRAINT, and \ref PRINT).

Notice that here we are printing a quantity named `res.bias`. We do this because \ref RESTRAINT does not define a single
value (that here would be theoretically named `res`) but a structure with several components. All
biasing methods (including \ref METAD) do so, as well as many collective variables (see e.g. \ref DISTANCE used with COMPONENTS keyword).
Printing the bias allows one to know how much a given snapshop was penalized.
Also notice that PLUMED understands numbers in the form `{number}pi`. This is convenient when using
torsions, since they are expressed in radians.

Now you can plot your trajectory with gnuplot and see the effect of KAPPA. You can also try different values
of KAPPA. The stiffer the restraint, the less the collective variable will fluctuate. However, notice 
that a too large kappa could make the MD integrator unstable.

\subsection cineca-multi Using multiple replicas

\warning Notice that multireplica simulations with PLUMED are fully supported
with GROMACS, but only partly supported with other MD engines.

Some free-energy methods are intrinsically parallel and requires running
several simultaneous simulations. This can be done with GROMACS using the
multi replica framework. That is, if you have 4 tpr files named topol0.tpr,
topol1.tpr, topol2.tpr, topol3.tpr you can run 4 simultaneous simulations.
\verbatim
> mpirun -np 4 gmx_mpi mdrun -s topol.tpr -plumed plumed.dat -multi 4 -nsteps 500000
\endverbatim
Each of the 4 replicas will open a different topol file, and GROMACS will
take care of adding the replica number before the .tpr suffix.
PLUMED deals with the extra number in a slightly different way.
In this case, for example, PLUMED first look for a file named `plumed.X.dat`,
where X is the number of the replica.  In case the file is not found,
then PLUMED looks for `plumed.dat`. If also this is not found, PLUMED will complain.
As a consequence, if all the replicas should use the same input file it is sufficient
to put a single `plumed.dat` file, but one has also the flexibility of using separate files
named `plumed.0.dat`, `plumed.1.dat` etc. 

Also notice that providing the flag `-replex` one can instruct GROMACS to perform a replica exchange
simulation. Namely, from time to time GROMACS will try to swap coordinates among neighboring
replicas and accept of reject the exchange with a Monte Carlo procedure which also
takes into account the bias potentials acting on the replicas, even if different bias potentials
are used in different replicas. That is, PLUMED allows
to easily implement many forms of Hamiltonian replica exchange.

\subsection cineca-multi-wham Using multiple restraints with replica exchange

\hidden{Weighted histogram analysis method theory}
\subsubsection cineca-wham-theory Weighted histogram analysis method theory 

Let's now consider multiple simulations performed with restraints located in different positions.
In particular, we will consider the \f$i\f$-th bias potential as \f$V_i\f$.
The probability to observe a given value of the collective variable \f$s\f$ is:
\f[
P_i({s})=\frac{P({s})e^{-\frac{V_i({s})}{k_BT}}}{\int ds' P({s}') e^{-\frac{V_i({s}')}{k_BT}}}=
\frac{P({s})e^{-\frac{V_i({s})}{k_BT}}}{Z_i}
\f]
where
\f[
Z_i=\sum_{q}e^{-\left(U(q)+V_i(q)\right)}
\f]
The likelyhood for the observation of a sequence of snapshots \f$q_i(t)\f$ (where \f$i\f$ is
the index of the trajectory and \f$t\f$ is time) is just the product of the probability
of each of the snapshots. We use here the minus-logarithm of the likelihood (so that
the product is converted to a sum) that can be written as
\f[
\mathcal{L}=-\sum_i \int dt \log P_i({s}_i(t))=
\sum_i \int dt
\left(
-\log P({s}_i(t))
+\frac{V_i({s}_i(t))}{k_BT}
+\log Z_i
\right)
\f]
One can then maximize the likelyhood by setting \f$\frac{\delta \mathcal{L}}{\delta P({\bf s})}=0\f$.
After some boring algebra the following expression can be obtained
\f[
0=\sum_{i}\int dt\left(-\frac{\delta_{{\bf s}_{i}(t),{\bf s}}}{P({\bf s})}+\frac{e^{-\frac{V_{i}({\bf s})}{k_{B}T}}}{Z_{i}}\right)
\f]
In this equation we aim at finding \f$P(s)\f$. However, also the list of normalization factors \f$Z_i\f$ is unknown, and they should 
be found selfconsistently. Thus one can find the solution as
\f[
P({\bf s})\propto \frac{N({\bf s})}{\sum_i\int dt\frac{e^{-\frac{V_{i}({\bf s})}{k_{B}T}}}{Z_{i}} }
\f]
where \f$Z\f$ is selfconsistently determined as 
\f[
Z_i\propto\int ds' P({\bf s}') e^{-\frac{V_i({\bf s}')}{k_BT}}
\f]

These are the WHAM equations that are traditionally solved to derive the unbiased probability \f$P(s)\f$ by the combination
of multiple restrained simulations. To make a slightly more general implementation, one can compute
the weights that should be assigned to each snapshot, that turn out to be:
\f[
w_i(t)\propto \frac{1}{\sum_j\int dt\frac{e^{-\beta V_{j}({\bf s}_i(t))}}{Z_{j}} }
\f]
The normalization factors can in turn be found from the weights as
\f[
Z_i\propto\frac{\sum_j \int dt e^{-\beta V_i({\bf s}_j(t))} w_j(t)}{
\sum_j \int dt w_j(t)}
\f]

This allows to straighforwardly compute averages related
to other, non-biased degrees of freedom, and it is thus a bit more flexible.
It is sufficient to precompute this factors \f$w\f$ and use them to weight
every single frame in the trajectory.

\endhidden

\subsubsection cineca-multiple-rest-phi Exercise 5

In this exercise we will run multiple restraint simulations and learn
how to reweight and combine data with WHAM to obtain free-energy profiles.
We start with running in a replica-exchange scheme 32 
simulations with a restraint on phi in different positions, ranging from -3 to 3.
We will instruct GROMACS to attempt an exchange between different simulations every 1000 steps.

First we need to create the 32 PLUMED input files and .tpr files, using the following script:

\verbatim
nrep=32
dx=`echo "6.0 / ( $nrep - 1 )" | bc -l`

for((i=0;i<nrep;i++))
do
# center of the restraint
AT=`echo "$i * $dx - 3.0" | bc -l`

cat >plumed.${i}.dat << EOF
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17
#
# Impose an umbrella potential on phi
# with a spring constant of 200 kjoule/mol
# and centered in phi=AT
#
restraint-phi: RESTRAINT ARG=phi KAPPA=200.0 AT=$AT
# monitor the two variables and the bias potential
PRINT STRIDE=100 ARG=phi,psi,restraint-phi.bias FILE=COLVAR
EOF

# we initialize some replicas in A and some in B:
if((i%2==0)); then
  cp ../SETUP/topolA.tpr topol$i.tpr
else
  cp ../SETUP/topolB.tpr topol$i.tpr
fi
done
\endverbatim

And then run GROMACS with the following command: 

\verbatim
mpirun -np 32 gmx_mpi mdrun -plumed plumed.dat -s topol.tpr -multi 32 -replex 1000 -nsteps 500000 -x traj.xtc
\endverbatim

To be able to combine data from all the simulations, it is necessary to have an overlap between 
statistics collected in two adjacent umbrellas.  
Have a look at the plots of (phi,psi) for the different simulations to understand what is happening.
To visualize them all at once as in Fig. \ref cineca-usrem-phi-all, you can prepare the following script:

\verbatim
awk 'BEGIN{print ""; ORS=""; print "p \"COLVAR.0\" u 2:3 notitle,"; for(i=1;i<=30;i++) print "\"COLVAR."i"\" u 2:3 notitle,"; print "\"COLVAR.31\" u 2:3 notitle"; ORS="\n"; print ""}' > plot.plt
\endverbatim

and then open gnuplot and load it:

\verbatim
gnuplot> load "plot.plt"
\endverbatim

\anchor cineca-usrem-phi-all
\image html munster-usrem-phi-all.png "(phi,psi) scatter plot from the multiple restraints simulation."

An often misunderstood fact about WHAM is that data of the different trajectories
can be mixed and it is not necessary to keep track of which restraint was used to produce
every single frame. Let's get the concatenated trajectory

\verbatim
trjcat_mpi -cat -f traj*.xtc -o alltraj.xtc
\endverbatim

Now we should compute the value of each of the bias potentials on the entire (concatenated) trajectory.

\verbatim
nrep=32
dx=`echo "6.0 / ( $nrep - 1 )" | bc -l`

for i in `seq 0 $(( $nrep - 1 ))`
do
# center of the restraint
AT=`echo "$i * $dx - 3.0" | bc -l`

cat >plumed.dat << EOF
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17
restraint-phi: RESTRAINT ARG=phi KAPPA=200.0 AT=$AT

# monitor the two variables and the bias potential
PRINT STRIDE=100 ARG=phi,psi,restraint-phi.bias FILE=ALLCOLVAR.$i
EOF

plumed driver --mf_xtc alltraj.xtc --trajectory-stride=10 --plumed plumed.dat

done
\endverbatim

It is very important that this script is consistent with the one used to generate the multiple simulations above.
Now, single files named ALLCOLVAR.XX will contain on the fourth column the value of the bias
centered in a given position, computed on the entire concatenated trajectory.

Next step is to compute the weights self-consistently solving the WHAM equations, using
the python script "wham.py" contained in the SCRIPTS directory. 
We will run this script as follows: 

\verbatim
bash ../SCRIPTS/wham.sh ALLCOLVAR.*
\endverbatim 

This script will produce several files. Let's visualize "phi_fes.dat", which contains the free energy as a function of phi, and compare this with the result previously obtained with metadynamics.

\anchor cineca-usrem-phi-fes
\image html munster-usrem-phi-fes.png "Free energy as a function of phi from multiple restraint (US-REM) and metadynamics (MetaD) simulations." 

\subsubsection cineca-multiple-rest-psi Exercise 6 

In the previous exercise, we use multiple restraint simulations to calculate the free energy as a function of the
dihedral phi. The resulting free energy was in excellent agreement with our previous metadynamics simulation.
In this exercise we will repeat the same procedure for the dihedral psi.
At the end of the steps defined above, we can plot the free energy "psi_fes.dat" and compare it with the
profile calculated from a metadynamics simulations using both phi and psi as CVs.

\anchor cineca-usrem-psi-fes
\image html munster-usrem-psi-fes.png "Free energy as a function of psi from multiple restraint (US-REM) and metadynamics (MetaD) simulations."

We can easily spot from the plot above that something went wrong in this multiple restraint simulations, despite we used the very same approach we adopted for the phi dihedral. The problem here is that psi is a "bad" collective variable, and the system is not able to equilibrate the missing slow degree of freedom phi in the short time scale of the umbrella simulation (1 ns). In the metadynamics exercise in which we biased only psi, we detect problems by observing the behavior of the CV as a function of simulation time. How can we detect problems in multiple restraint simulations? This is slightly more complicated, but running this kind of simulation in a replica-exchange scheme offers a convenient way to detect problems.

The first thing we need to do is to demux the replica-exchange trajectories and reconstruct the continous trajectories of the replicas
across the different restraint potentials. In order to do so, we can use the following script:

\verbatim
demux.pl md0.log
trjcat_mpi -f traj?.xtc traj??.xtc  -demux replica_index.xvg
\endverbatim
 
This commands will generate 32 continous trajectories, named XX_trajout.xtc. We will use the driver to calculate the value of the CVs
phi and psi on these trajectories.

\verbatim
nrep=32

for i in `seq 0 $(( $nrep - 1 ))`
do

cat >plumed.dat << EOF
phi: TORSION ATOMS=5,7,9,15
psi: TORSION ATOMS=7,9,15,17

# monitor the two variables
PRINT STRIDE=100 ARG=phi,psi FILE=COLVARDEMUX.$i
EOF

plumed driver --mf_xtc ${i}_trajout.xtc --trajectory-stride=10 --plumed plumed.dat

done
\endverbatim

The COLVARDEMUX.XX files will contain the value of the CVs on the demuxed trajectory. If we visualize these files (see previous exercise for instructions) we will notice that
replicas sample the CVs space differently. In order for each umbrella to equilibrate the slow degrees of freedom phi, the continuous replicas
must be ergodic and thus sample the same distribution in phi and psi.  

\anchor cineca-usrem-psi-demux
\image html munster-usrem-psi-demux.png "(phi,psi) scatter plot from the demuxed trajectories of the multiple restraints simulation in psi."


*/

link: @subpage cineca

description: A short 2 hours tutorial that introduces analysis, well-tempered metadynamics, and multiple-restraints umbrella sampling.

additional-files: cineca