Accelerated Weighted Ensemble (AWE) provides a Python library for adaptive sampling of molecular dynamics.
Jupyter Notebook Python C Other
Latest commit 9f7291b Nov 7, 2016 @jeffkinnison jeffkinnison committed on GitHub Merge pull request #14 from jeffkinnison/msmb-update
updated openmm simulation file



Accelerated Weighted Ensemble or AWE package provides a Python library for adaptive sampling of molecular dynamics. The framework decomposes the resampling computations and the molecular dynamics simulations into tasks that are dispatched for execution on resources allocated from clusters, clouds, grids, or any idle machines.

AWE uses Work Queue, which is part of the Cooperating Computing Tools (CCTools) package, for dispatching jobs for execution on allocated resources. Documentation on downloading, installing, and using Work Queue can be found here.



First, determine the location where AWE is to be installed. For example:


Compile and install AWE in the location pointed by $AWE_INSTALL_PATH using:

$ tar xf awe-src.tar.gz
$ cd awe
$ ./configure --prefix $AWE_INSTALL_PATH
$ make install

Next, set PATH to include the installed AWE binaries:

$ export PATH=${AWE_INSTALL_PATH}/bin:${PATH} 

Finally, set PYTHONPATH to include the installed AWE Python modules:

$ export PYTHONPATH=${AWE_INSTALL_PATH}/lib/python2.6/site-packages:${PYTHONPATH}  

Note that the AWE Python modules will be built for the version of Python accessible in your installed environment. The installation script creates a directory (under $AWE_INSTALL_PATH/lib) named with the version of Python for which the modules are built and copies the modules to this directory. So if your environment has a Python version different from 2.6, replace the version string accordingly when setting PYTHONPATH.

You can check if AWE was correctly installed by running:

$ awe-verify


First, create a directory from which you want to instantiate and run the AWE program. Here, we are going to run AWE to sample the state transitions of the Alanine Dipeptide protein using the code in

$ cd $HOME
$ mkdir awe-alanine 
$ cd awe-alanine 

To run AWE to sample a protein molecule, you will need to have the files describing the topology of atoms in that molecule, the coordinates of the walkers, and the coordinates of the cells. In addition, AWE transfers the executables from the GROMACS package required for running the simulations of each walker.

These files and executables can be fetched to the current working directory by running:

$ awe-prepare

This will create two directories named awe-generic-data and awe-instance-data. awe-generic-data will contain files that all AWE runs will require, such as the task executables and Gromacs forcefield files. awe-instance-data will contain files that are particular to a protein system such as the state definitions, initial protein coordinates, etc. Note that awe-prepare, by default, will transfer the files for the Alanine Dipeptide protein molecule. Further, awe-prepare will also copy the example program provided in the AWE source that samples the state transitions for the Alanine Dipeptide protein.

To run the example in, do

$ python

You will see this output right away:

$ python
  Running on port 9123...
  Loading cells and walkers

The AWE master program successfully started and began loading the cells and walkers for running the simulations. After that, the master waits for workers to connect so it can dispatch the simulation tasks for execution by the connected workers.

Now, start a worker for this master on the same machine:

$ work_queue_worker localhost 9123

However, to run a really large sampling, you will need to run as many workers as possible. A simple (but tiresome) way of doing so is to log into several machines and manually run work_queue_worker as above. But, if you have access to a batch system like Condor or SGE, you can use them to start many workers with a single submit command.

We have provided some scripts to make this easy. For example, to submit 10 workers to your local Condor pool:

$ condor_submit_workers 9123 10
  Submitting job(s)..........
  Logging submit event(s)..........
  10 job(s) submitted to cluster 298.

Or, to submit 10 worker processes to your SGE cluster:

$ sge_submit_workers 9123 10
  Your job 1054781 ("") has been submitted
  Your job 1054782 ("") has been submitted
  Your job 1054783 ("") has been submitted

Once the workers begin running, the AWE master can dispatch tasks to each one very quickly. It's ok if a machine running a worker crashes or is turned off; the work will be silently sent elsewhere to run.

When the AWE master process completes, your workers will still be available, so you can either run another master with the same workers, remove them from the batch system, or wait for them to expire. If you do nothing for 15 minutes, they will automatically exit.


The AWE master creates the following output files on completion:

  • cell-weights.csv describing the weight of the cells
  • walker-history.csv describing the cells that were visited by the walkers
  • color-transition-matrix.csv that describes the number of transitions between the defined cell groups
  • walker-weights.csv that describes the weights of the walkers

You can use these output files to generate different plots that visualize the output of the AWE run. To generate a Ramachandran plot (free energy landscape on phi-psi space) using cell-weights.csv, run the following command.

$ python awe-rama-ala -w cell-weights.csv -p awe-instance-data/topol.pdb -c awe-instance-data/cells.dat -n 100


  • -w specifies the csv file recording cell weights
  • -p the structure file of simulation target
  • -c the data file recording coordinates of cell centers
  • -n the number of cells

This produces an output file named awe-rama-ala.png that contains the Ramachandran plot.

To generate and visualize forward and backward fluxes from output color-transition-matrix.csv, use the script awe-flux.

 python awe-flux -i color-transition-matrix.csv -l 0.01  


  • -i specifies the data file recording transitions at every iteration
  • -l specifies the scale iteration length to actual unit (nano/pico/femto-seconds)
  • -o specifies the output directory

This will produce the following outputs:

  • instan-forward-flux.dat: instantaneous forward flux
  • instan-backward-flux.dat: instantaneous backward flux
  • forward-flux.png: plot of forward flux
  • backward-flux.png: plot of backward flux

Finally, to generate transition probability matrix from AWE output, run script awe-transMatrix

$ python awe-transMatrix -p walker-history.dat -w walker-weights.dat -t 1 -n 100

where the options

  • -p specifies the data file recording dependencies of walkers
  • -w specifies the data file recording weights and cell ID of walkers
  • -t specifies the time lag (number of iterations) for calculating transition matrix
  • -n specifies the number of cells

This prints the matrix to a file called trans-probability-matrix.csv.


You can run AWE to sample a different protein system by following the steps below:

  1. Sample the conformations space using ensemble simulations, replica exchange, etc.
  2. Cluster the data to obtain the cell definitions.
  3. Extract the conformations from each cluster as individual walkers.

Specifically, these steps translate to the following:

  1. Describe the topology of the system in topol.pdb.
  2. Prepare the state definitions and list them in cells.dat.
  3. Select the subset of atoms from the cell definitions cells.dat and list them in CellIndices.dat.
  4. Select the subset of atoms from the walker topology file topol.pdb and list them in StructureIndices.dat.
  5. Define the initial coordinates for the walkers in State$i-$j.pdb where i is the index of the cell and j is the index of the walker.
  6. Specify the parameters for the walker simulation by GROMACS in sim.mdp.


Please see the AUTHORS file.