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:
- Python3.10 or greater
- NumPy 1.8+
- SciPy
- GCC (or another C-compiler)
- 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
# To run on CPU
pip install .
# To run on GPU
pip install .[opencl]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.
Powerfit can be run in a Docker container.
Install docker by following the instructions.
Linux systems usually already include a Python3.10 or greater distribution. First make sure the Python header files, pip and git are available by opening up a terminal and typing for Debian and Ubuntu systems
sudo apt update
sudo apt install python3-dev python3-pip git build-essentialIf you are working on Fedora, this should be replaced by
sudo yum install python3-devel python3-pip git development-c development-toolsSteps for running on GPU
If you want to use the GPU version of PowerFit, you need to install the drivers for your GPU.
After installing the drivers, you need to install the OpenCL development libraries and OpenCL fft library. For Debian/Ubuntu, this can be done by running
sudo apt install opencl-headers ocl-icd-opencl-dev libclfft-devFor Fedora, this can be done by running
sudo dnf install opencl-headers ocl-icd-devel
# Manually install clFFT from https://github.com/clMathLibraries/clFFTInstall gpyfft, a Python wrapper for OpenCL fft library, using
pip install cython
pip install --no-use-pep517 gpyfft@git+https://github.com/geggo/[email protected]Check that the OpenCL installation is working by running
python -c 'import pyopencl as cl;from gpyfft import GpyFFT; ps=cl.get_platforms();print(ps);print(ps[0].get_devices())'
# Should print the name of your GPUYour 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 gitNext install pip, the Python package manager, by following the installation instructions on the website or open a terminal and type
sudo easy_install pipFollow the general instructions above to install PowerFit.
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 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.
Usage in Docker
The Docker images of powerfit are available in the GitHub Container Registry.
Running PowerFit in a Docker container with data located at
a hypothetical /path/to/data on your machine can be done as follows
docker run --rm -ti --user $(id -u):$(id -g) \
-v /path/to/data:/data ghcr.io/haddocking/powerfit:v3.0.0 \
/data/<map> <resolution> /data/<pdb> \
-d /data/<results-dir>For <map>, <pdb>, <results-dir> use paths relative to /path/to/data.
To run tutorial example use
# cd into powerfit-tutorial repo
docker run --rm -ti --user $(id -u):$(id -g) \
-v $PWD:/data ghcr.io/haddocking/powerfit:v3.0.0 \
/data/ribosome-KsgA.map 13 /data/KsgA.pdb \
-a 20 -p 2 -l -d /data/run-KsgA-dockerTo run on NVIDIA GPU using NVIDIA container toolkit use
docker run --rm -ti \
--runtime=nvidia --gpus all -v /etc/OpenCL:/etc/OpenCL \
-v $PWD:/data ghcr.io/haddocking/powerfit:v3.0.0 \
/data/ribosome-KsgA.map 13 /data/KsgA.pdb \
-a 20 -p 2 -l -d /data/run-KsgA-docker-nv --gpuTo run on AMD GPU use
sudo docker run --rm -ti \
--device=/dev/kfd --device=/dev/dri \
--security-opt seccomp=unconfined \
--group-add video --ipc=host \
-v $PWD:/data ghcr.io/haddocking/powerfit-rocm:v3.0.0 \
/data/ribosome-KsgA.map 13 /data/KsgA.pdb \
-a 20 -p 2 -l -d /data/run-KsgA-docker-amd--gpuFirst, to see all options and their descriptions type
powerfit --helpThe 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 24To use multiple CPU cores with laplace pre-filter and 5° rotational interval
powerfit <map> <resolution> <pdb> -p 4 -l -a 5To 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 15Note that all options can be combined except for the -g and -p flag:
calculations are either performed on the CPU or GPU.
To run on GPU
powerfit <map> <resolution> <pdb> --gpu
...
Using GPU-accelerated search.
...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 20To see the other options type
image-pyramid --helpIf 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) https://doi.org/10.3934/biophy.2015.2.73.
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) https://doi.org/10.1016/j.jsb.2016.06.011.
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 | No |
| Windows | Yes | Fail | No |
The GPU version has been tested on:
- NVIDIA GeForce GTX 1050 Ti, GeForce RTX 4070 and AMD Radeon RX 7900 XTX on Linux
- NVIDIA GeForce GTX 1050 Ti, AMD Radeon RX 7800 XT and AMD Radeon RX 7900 XTX in Docker container
To develop PowerFit, you need to install the development version of it using.
pip install -e .[dev]Tests can be run using
pytestTo run OpenCL on CPU install use pip install -e .[pocl] and make sure no other OpenCL platforms, like 'AMD Accelerated Parallel Processing' or 'NVIDIA CUDA', are installed .
The Docker container, that works for cpu and NVIDIA gpus, can be build with
docker build -t ghcr.io/haddocking/powerfit:v3.0.0 .The Docker container, that works for AMD gpus, can be build with
docker build -t ghcr.io/haddocking/powerfit-rocm:v3.0.0 -f Dockerfile.rocm .