PowerFit: An updated framework for fast rigid-body fitting into cryo-EM density maps.
- 1Utrecht University, Faculty of Science - Chemistry, Bijvoet Centre for Biomolecular Research
Abstract
Cryo-electron microscopy (cryo-EM) has become a central method for determining structures of molecular assemblies, requiring reliable tools to interpret the density maps. Despite recent advances in AI-driven model building, at intermediate-to-low resolution (4-15 [a]) accurate rigid-body fitting of existing atomic structures into cryo-EM maps remains an essential step in the modelling process. Here we present an updated version of our software package PowerFit. It performs an exhaustive translational and rotational search against the cryo-EM density map, implementing sensitive correlation functions. The updated release preserves the fitting accuracy and sensitivity of the original method while substantially improving the computational performance through modernised CPU and GPU implementations. In addition, to facilitate the interpretation of results, it provides an HTML report with interactive 3D visualisation using MolViewSpec. The same HTML report is used to generate the result page when using PowerFit via its web service. The report enables immediate inspection and validation of the results. The software's accessibility has been enhanced by updating it from Python 2 to Python 3, enabling straightforward installation via the Python Package Index (PyPI) and simple integration into computational pipelines. PowerFit is freely available at https://github.com/haddocking/powerfit, as a web service at https://wenmr.science.uu.nl/powerfit or in JupyterLite directly in a web browser at https://www.bonvinlab.org/powerfit/jupyterlite.html.
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BibTeX
@misc{engel2026powerfit,
title = {{PowerFit: An updated framework for fast rigid-body fitting into cryo-EM density maps.}},
author = {A. Engel and S. Verhoeven and B. Schilperoort and R. Vargas Honorato and A. M. J. J. Bonvin},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.755289},
url = {https://www.biorxiv.org/content/10.64898/2026.09.29.755289v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - PowerFit: An updated framework for fast rigid-body fitting into cryo-EM density maps.
AU - A. Engel
AU - S. Verhoeven
AU - B. Schilperoort
AU - R. Vargas Honorato
AU - A. M. J. J. Bonvin
PY - 2026
DA - 2026/09/29
PB - bioRxiv
DO - 10.64898/2026.09.29.755289
UR - https://www.biorxiv.org/content/10.64898/2026.09.29.755289v1
AB - Cryo-electron microscopy (cryo-EM) has become a central method for determining structures of molecular assemblies, requiring reliable tools to interpret the density maps. Despite recent advances in AI-driven model building, at intermediate-to-low resolution (4-15 [a]) accurate rigid-body fitting of existing atomic structures into cryo-EM maps remains an essential step in the modelling process. Here we present an updated version of our software package PowerFit. It performs an exhaustive translational and rotational search against the cryo-EM density map, implementing sensitive correlation functions. The updated release preserves the fitting accuracy and sensitivity of the original method while substantially improving the computational performance through modernised CPU and GPU implementations. In addition, to facilitate the interpretation of results, it provides an HTML report with interactive 3D visualisation using MolViewSpec. The same HTML report is used to generate the result page when using PowerFit via its web service. The report enables immediate inspection and validation of the results. The software's accessibility has been enhanced by updating it from Python 2 to Python 3, enabling straightforward installation via the Python Package Index (PyPI) and simple integration into computational pipelines. PowerFit is freely available at https://github.com/haddocking/powerfit, as a web service at https://wenmr.science.uu.nl/powerfit or in JupyterLite directly in a web browser at https://www.bonvinlab.org/powerfit/jupyterlite.html.
ER -