PowerFit is a Python package and simple command-line program to automatically fit high-resolution atomic structures in cryo-EM densities. To this end it performs a full-exhaustive 6-dimensional cross-correlation search between the atomic structure and the density. It takes as input an atomic structure in PDB-format and a cryo-EM density with its resolution; and outputs positions and rotations of the atomic structure corresponding to high correlation values. PowerFit uses the local cross-correlation function as its base score. The score can optionally be enhanced by a Laplace pre-filter and/or a core-weighted version to minimize overlapping densities from neighboring subunits. It can further be hardware-accelerated by leveraging multi-core CPU machines out of the box or by GPU via the OpenCL framework. PowerFit is Free Software and has been succesfully installed and used on Linux and MacOSX machines.
Minimal requirements for the CPU version:
- Python2.7
- NumPy 1.8+
- SciPy
- GCC (or another C-compiler)
Optional requirement for faster CPU version:
- FFTW3
- pyFFTW 0.10+
To offload computations to the GPU the following is also required
- OpenCL1.1+
- pyopencl
- clFFT
- gpyfft
Recommended for installation
- git
- pip
If you already have fulfilled the requirements, the installation should be as easy as opening up a shell and typing
git clone https://github.com/haddocking/powerfit.git
cd powerfit
sudo python setup.py install
If you are starting from a clean system, follow the instructions for your particular operating system as described below, they should get you up and running in no time.
First install docker by following the instructions.
A docker container comprised of PowerFit and its CPU/GPU dependencies can be created for your compute platform as follows
docker build -t haddocking/powerfit:v2.1.0 -f Dockerfile .
docker run haddocking/powerfit:v2.1.0 <map> <resolution> <pdb>
Linux systems usually already include a Python2.7 distribution. First make sure the Python header files, NumPy, SciPy, and git are available by opening up a terminal and typing for Debian and Ubuntu systems
sudo apt-get install python-dev python-numpy python-scipy git
If you are working on Fedora, this should be replaced by
sudo yum install python-devel numpy scipy git
Sit back and wait till the compilation and installation is finished. Your system is now prepared, follow the general instructions above to install PowerFit.
First install git by following the instructions on their website, or using a package manager such as brew
brew install git
Next install pip, the Python package manager, by following the installation instructions on the website or open a terminal and type
sudo easy_install pip
Next, install NumPy and SciPy by typing
sudo pip install numpy scipy
Wait for the installation to finish. Follow the general instructions above to install PowerFit.
Installing pyFFTW for faster CPU version can be done as follows using brew
brew install fftw
sudo pip install pyfftw
First install git for Windows, as it comes with a handy bash shell. Go to git-scm, download git and install it. Next, install a Python distribution with NumPy and Scipy included such as Anaconda. After installation, open up the bash shell shipped with git and follow the general instructions written above.
After installing PowerFit the command line tool powerfit should be at your disposal. The general pattern to invoke powerfit is
powerfit <map> <resolution> <pdb>
where <map> is a density map in CCP4 or MRC-format, <resolution> is the
resolution of the map in ångstrom, and <pdb> is an atomic model in the
PDB-format. This performs a 10° rotational search using the local
cross-correlation score on a single CPU-core. During the search, powerfit
will update you about the progress of the search if you are using it
interactively in the shell.
Running PowerFit in a docker container named powerfit on data located at
a hypothetical /path/to/data on your machine can be done as follows
docker run --rm -v /path/to/data:/data powerfit \
powerfit /data/<map> <resolution> /data/<pdb> -d /data
First, to see all options and their descriptions type
powerfit --help
The information should explain all options decently. In addtion, here are some examples for common operations.
To perform a search with an approximate 24° rotational sampling interval
powerfit <map> <resolution> <pdb> -a 24
To use multiple CPU cores with laplace pre-filter and 5° rotational interval
powerfit <map> <resolution> <pdb> -p 4 -l -a 5
To off-load computations to the GPU and use the core-weighted scoring function and write out the top 15 solutions
powerfit <map> <resolution> <pdb> -g -cw -n 15
Note that all options can be combined except for the -g and -p flag:
calculations are either performed on the CPU or GPU.
When the search is finished, several output files are created
- fit_N.pdb: the top N best fits.
- solutions.out: all the non-redundant solutions found, ordered by their correlation score. The first column shows the rank, column 2 the correlation score, column 3 and 4 the Fisher z-score and the number of standard deviations (see N. Volkmann 2009, and Van Zundert and Bonvin 2016); column 5 to 7 are the x, y and z coordinate of the center of the chain; column 8 to 17 are the rotation matrix values.
- lcc.mrc: a cross-correlation map, showing at each grid position the highest correlation score found during the rotational search.
- powerfit.log: a log file, including the input parameters with date and timing information.
The use of multi-scale image pyramids can signicantly increase the speed of
fitting. PowerFit comes with a script to quickly build a pyramid called
image-pyramid. The calling signature of the script is
image-pyramid <map> <resolution> <target-resolutions ...>
where <map is the original cryo-EM data, <resolution is the original
resolution, and <target-resolutions> is a sequence of resolutions for the
resulting maps. The following example will create an image-pyramid with
resolutions of 12, 13 and 20 angstrom
image-pyramid EMD-1884/1884.map 9.8 12 13 20
To see the other options type
image-pyramid --help
If this software was useful to your research, please cite us
G.C.P. van Zundert and A.M.J.J. Bonvin. Fast and sensitive rigid-body fitting into cryo-EM density maps with PowerFit. AIMS Biophysics 2, 73-87 (2015).
For the use of image-pyramids and reliability measures for fitting, please cite
G.C.P van Zundert and A.M.J.J. Bonvin. Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids. J. Struct. Biol. 195, 252-258 (2016).
Apache License Version 2.0
The elements.py module is licensed under MIT License (see header). Copyright (c) 2005-2015, Christoph Gohlke
| Operating System | CPU single | CPU multi | GPU |
|---|---|---|---|
| Linux | Yes | Yes | Yes |
| MacOSX | Yes | Yes | Yes |
| Windows | Yes | Fail | No |
The GPU version has been tested on:
- NVIDIA GeForce GTX 680 and AMD Radeon HD 7730M for Linux
- NVIDIA GeForce GTX 775M for MacOSX 10.10