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Bayesian weighing of electron cryo-microscopy data for integrative structural modeling

Massimiliano Bonomi, Samuel Hanot, Charles H. Greenberg, Andrej Sali, Michael Nilges, Michele Vendruscolo, Riccardo Pellarin
doi: https://doi.org/10.1101/113951
Massimiliano Bonomi
1Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
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  • For correspondence: mb2006@cam.ac.uk riccardo.pellarin@pasteur.fr
Samuel Hanot
2Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528, 75015 Paris, France
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Charles H. Greenberg
3Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, California 94158, United States
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Andrej Sali
3Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, California 94158, United States
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Michael Nilges
2Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528, 75015 Paris, France
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Michele Vendruscolo
1Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
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Riccardo Pellarin
2Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528, 75015 Paris, France
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  • For correspondence: mb2006@cam.ac.uk riccardo.pellarin@pasteur.fr
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Abstract

Summary Cryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map and other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.

Highlights

  • We present a modeling approach to integrate cryo-EM data with other sources of information

  • We benchmark our approach using synthetic data on 21 complexes of known structure

  • We apply our approach to the GroEL/GroES, RNA polymerase II, and exosome complexes

Footnotes

  • ↵‡ Lead contact

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 08, 2018.
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Bayesian weighing of electron cryo-microscopy data for integrative structural modeling
Massimiliano Bonomi, Samuel Hanot, Charles H. Greenberg, Andrej Sali, Michael Nilges, Michele Vendruscolo, Riccardo Pellarin
bioRxiv 113951; doi: https://doi.org/10.1101/113951
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Bayesian weighing of electron cryo-microscopy data for integrative structural modeling
Massimiliano Bonomi, Samuel Hanot, Charles H. Greenberg, Andrej Sali, Michael Nilges, Michele Vendruscolo, Riccardo Pellarin
bioRxiv 113951; doi: https://doi.org/10.1101/113951

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