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Fast and sensitive rigid-body fitting into cryo-EM density maps with PowerFit

Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, the Netherlands

Special Issue: Structural analysis of macromolecules using Cryo electron microscopy

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 de novo 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.
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1. Bai X-C, McMullan G, Scheres SHW (2015) How cryo-EM is revolutionizing structural biology. Trends Biochem Sci 40: 49-57.    

2. Villa E, Lasker K (2014) Finding the right fit: chiseling structures out of cryo-electron microscopy maps. Curr Opin Struct Biol 25: 118-125.    

3. Pettersen EF, Goddard TD, Huang CC, et al. (2004) UCSF Chimera - a visualization system for exploratory research and analysis. J Comput Chem 25: 1605-1612.    

4. Esquivel-Rodriguez J, Kihara D (2013) Computational methods for constructing protein structure models from 3D electron microscopy maps. J Struct Biol 184: 93-102.    

5. Volkmann N, Hanein D (1999) Quantitative fitting of atomic models into observed densities derived by electron microscopy. J Struct Biol 125: 176-184.    

6. Rosemann AM (2000) Docking structures of domains into maps from cryo-electron microscopy using local correlation. Acta Crystallogr D Biol Crystallogr 56: 1332-1340.    

7. Chacón P, Wriggers W (2002) Multi-resolution contour-based fitting of macromolecular structures. J Mol Biol 317: 375-384.    

8. Kovacs JA, Chacón P, Cong Y, et al. (2003) Fast rotational matching of rigid bodies by Fast Fourier transform acceleration of five degrees of freedom. Acta Crystallogr D Biol Crystallogr 59: 1371-1376.    

9. Wu X, Milne JLS, Borgnia MJ, et al. (2003) A core-weighted fitting method for docking atomic structures into low-resolution maps: application to cryo-electron microscopy. J Struct Biol 141: 63-76.    

10. Garzón JI, Kovacs J, Abagyan R, et al. (2007) ADP_EM: fast exhaustive multi-resolution docking for high-throughput coverage. Bioinformatics 23: 427:433.

11. Hrabe T, Chen Y, Pfeffer S, et al. (2012) PyTom: a python-based toolbox for localization of macromolecules in cryo-electron tomograms and subtomogram analysis. J Struct Biol 178: 177-188.    

12. Hoang TV, Cavin X, Ritchie DW (2013) gEMfitter: A highly parallel FFT-based 3D density fitting tool with GPU texture memory acceleration. J Struct Biol 184: 348-354.    

13. Roseman AM (2003) Particle finding in electron micrographs using a fast local correlation algorithm. Ultramicroscopy 94: 225-236.    

14. Karney CFF (2007) Quaternions in molecular modeling. J Mol Graph Mod 25: 595-604.    

15. Anger AM, Armache J-P, Berninghausen O, et al. (2013) Structures of the human and Drosophila 80S ribosome. Nature 497: 80-85.    

16. Ranson NA, Farr GW, Roseman AM, et al. (2001) ATP-bound states of GroEL captured by cryo-electron microscopy. Cell 107: 869-879.    

17. Volkmann N (2002) A novel three-dimensional variant of the watershed transform for segmentation of electron density maps. J Struct Biol 138: 123-129.    

18. Pintilie G, Chiu W (2012) Comparison of Segger and other methods for segmentation and rigid body docking of molecular components in cryo-EM density maps. Biopolymers 97: 742-760.    

19. Pintilie GD, Zhang J, Goddard TD, et al. (2010) Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions. J Struct Biol 170: 427-438.    

20. Chen D-H, Madan D, Weaver J, et al. (2013) Visualizing GroEL/ES in the act of encapsulating a folding protein. Cell 153: 1354-1365.    

21. Guo Q, Yuan Y, Xu Y, et al. (2011) Structural basis for the function of a small GTPase RsgA on the 30S ribosomal subunit maturation revealed by cryoelectron microscopy. Proc Natl Acad Sci U S A 108: 13100-13105.    

22. Boehringer D, O'Farrell HC, Rife JP, et al. (2012) Structural insights into methyltransferase KsgA function in 30S ribosomal subunit biogenesis. J Biol Chem 287: 10453-10459.    

Copyright Info: © 2015, Alexandre M.J.J. Bonvin, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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