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

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Bonomi, Massimiliano | Hanot, Samuel | Greenberg, Charles | Sali, Andrej | Nilges, Michael | Vendruscolo, Michele | Pellarin, Riccardo

Edité par CCSD ; Elsevier (Cell Press) -

International audience. 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.

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