Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling

Massimiliano Bonomi (1) , Samuel Hanot (2) , Charles Greenberg (3) , Andrej Sali (3) , Michael Nilges (2) , Michele Vendruscolo (1) , Riccardo Pellarin (2)

1 CAM - University of Cambridge [UK]
2 Bioinformatique structurale - Structural Bioinformatics
3 UC San Francisco - University of California [San Francisco]

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 as well as 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.

Mots clés: Gaussian mixture model; bayesian inference; cross-linking mass spectrometry; cryo-electron microscopy; data weighing; integrative structural modeling; macromolecular complexes; structural biology

Domaines: Biophysique; Biologie moléculaire

Massimiliano Bonomi, Samuel Hanot, Charles Greenberg, Andrej Sali, Michael Nilges, et al.. Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling. Structure, 2019, 27 (1), pp.175-188.e6. ⟨10.1016/j.str.2018.09.011⟩. ⟨pasteur-02404465⟩