Ensembler is developed and tested on Linux and Mac. Windows is not well supported.

Supported Hardware

The simulation refinement stages of Ensembler use OpenMM, which performs best on CUDA-enabled GPUs. However, OpenMM also has an optimized CPU platform, so use of GPUs is optional.

Install with Conda

conda is a package manager built for scientific Python. Unlike easy_install or pip, it handles binaries and binary dependencies, which are critical for most scientific workflows.

Conda can be obtained by installing Continuum Anaconda - an awesome free Python distribution for scientific computing. The standard installation contains many of Ensembler’s dependencies. Alternatively, for a more minimal Python set-up you can install miniconda, which contains only Conda and Python.

First get a license for Modeller from the Modeller website (registration required; free for academic non-profit use).

Save this in an environment variable as follows


Then, to install Ensembler with Conda, use the following commands

$ conda config --add channels
$ conda config --add channels
$ conda install ensembler

Conda will automatically install all dependencies except for the optional dependencies Rosetta and MolProbity. These require licenses (free for academic non-profit use), and will have to be installed according to the instructions for those packages. Some limited installation instructions are included below, but these are not guaranteed to be up to date.

Install from Source

Clone the source code repository from github

$ git clone git://

Then, in the directory containing the source code, you can install it with.

$ python install


To use Ensembler, the following libraries and software will need to be installed.

Linux, Mac OS X or Windows operating system
We develop mainly on 64-bit Linux and Mac machines. Windows is not well supported.
Python >= 2.6
The development package (python-dev or python-devel on most Linux distributions) is recommended.
Comparative modeling of protein structures.
Molecular simulation toolkit, with GPU-accelerated simulation platform.
Simulation trajectory analysis library.
Statistical models for biomolecular dynamics.
PDB structure modeling
Collection of Python tools for computational biology and bioinformatics.
Numpy is the base package for numerical computing in Python.
For working with XML files.
For working with YAML files.
For building command-line interfaces.
For testing in Python

Optional packages:

Allows many Ensembler functions to be run in parallel using MPI.
Protein modeling suite. The loopmodel function is optionally used by Ensembler to reconstruct missing loops in template structures.
(If running Python 2.) Backport of the Python 3 subprocess module for Python 2. Used to run command-line programs such as Rosetta loopmodel. Includes timeout functionality which is particularly useful for the template loop reconstruction routine.
Some functionality, including the quickmodel and inspect functions, requires pandas.
For model validation. The package_models function can use this data to filter models by validation score.

Manually Installing the Dependencies


If you’re on ubuntu and have root, you can install most dependencies through your package manager (apt-get).

$ sudo apt-get install python-dev


If you’re on mac and want a package manager, you should be using homebrew and brews‘s Python (see this page for details). For example, numpy can be installed with brew as follows:

$ brew tap Homebrew/python
$ brew install python
$ brew install numpy

Then, you can install many of the remaining packages with pip.

$ pip install lxml


Chris Gohlke maintains windows binary distributions for an ever-growing set of Python extensions on his website. Download and install the the installers for setuptools, nose, numpy, scipy, numexpr, pandas and tables.

Testing Your Installation

Running the tests is a great way to verify that everything is working. The test suite uses nose and mock, which you can pick up via conda or pip if you don’t already have them.

$ conda install nose mock

To run the unit tests:

$ nosetests ensembler -a unit

Further tests are available which check interoperation of Ensembler with software dependencies such Rosetta loopmodel, or with external public databases such as UniProt, or are excluded from the unit tests due to being slow. To run them:

$ nosetests ensembler -a non_conda_dependencies -a network -a slow

Installation of Dependencies Unavailable Through Conda

(Note: only limited instructions are included here, and these are not guaranteed to be up to date. If you encounter problems, please consult the relevant support or installation instructions for that software dependency.)


Download the MolProbity 4.2 release source from the GitHub repo.

Extract the zip file, enter the created directory, and run the following command:

$ ./

This was all that was required when tested on a MacBook running OS X 10.8.

On a Linux cluster, it was first necessary to edit the file to uncomment the following line, and comment the make command:

$ ./binlibtbx.scons -j 1

This forces the build to use only a single core - this ran rather slowly, but using more cores resulted in build failure. This is likely due to memory issues. After runnng ./ it was then also necessary to run ./

Binaries can found in the [MolProbity source dir]/cmdline directory.


Download Rosetta here.

Rosetta version 2014.35 has been most thoroughly tested within ensembler.

Rosetta requires a program called Scons to compile. It is conda installable:

$ conda install --yes scons

It is important to add both rosetta_2014.35.57232_bundle/main/source/bin to your $PATH and rosetta_2014.35.57232_bundle/main/database to a new $MINIROSETTA_DATABASE path, or else loop modeling will not work.