Abstract
Cryo-EM is a rapidly developing method to investigate the three dimensional structure of large macromolecular complexes. In spite of all the advances in the field, the resolution of most cryo-EM density maps is too low for <em>de novo</em> model building. Therefore, the data are often complemented by fitting high-resolution subunits in the density to allow for an atomic interpretation. Typically, the first step in the modeling process is placing the subunits in the density as a rigid body. An objective method for automatic placement is full-exhaustive six dimensional cross correlation search between the model and the cryo-EM data, where the three translational and three rotational degrees of freedom are systematically sampled. In this article we present PowerFit, a Python package and program for fast and sensitive rigid body fitting. We introduce a novel, more sensitive scoring function, the core-weighted local cross correlation, and show how it can be calculated using FFTs for fast translational cross correlation scans. We further improved the search algorithm by using optimized rotational sets to reduce rotational redundancy and by limiting the cryo-EM data size through resampling and trimming the density. We demonstrate the superior scoring sensitivity of our scoring function on simulated data of the 80S D. melanogaster ribosome and on experimental data for four different cases. Through these advances, a fine-grained rotational search can now be performed within minutes on a CPU and seconds on a GPU. PowerFit is free software and can be downloaded from https://github.com/haddocking/powerfit.
Highlights
Determining the architecture of large macromolecular complexes is of considerable interest to understand their function and mechanisms
To test the scoring sensitivity of the CW-local cross-correlation (LCC), we used PowerFit to fit each subunit of the 80S D. melanogaster ribosome [15] independently in the density at different resolutions
The Cryo-electron microscopy (cryo-EM) data were created using a Python script based on the molmap function in UCSF Chimera
Summary
Determining the architecture of large macromolecular complexes is of considerable interest to understand their function and mechanisms. Cryo-EM data are complemented with known high-resolution three dimensional (3D) models determined either experimentally or via homology modeling These represent the pieces of the density puzzle that should all be fitted together in the map. The first step in the high-resolution modeling process is placing the subunits as rigid entities at the correct position in the density This is often done manually using graphics software, most notably UCSF Chimera using its fit-in-map function [3]. This is unfortunate as it is subjective and can lead to over-interpretation of the density map, as there is no objective scoring function to give an indication of the goodness-of-fit This is especially problematic if flexible fitting is applied afterwards, since for the refinement to make sense the subunit should be located in a local minimum, else it might drift away from its initial position during the process. This leads to a thorough and objective analysis of all possible solutions to locate the global cross-correlation minimum
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