TY - JOUR T1 - Bayesian weighing of electron cryo-microscopy data for integrative structural modeling JF - bioRxiv DO - 10.1101/113951 SP - 113951 AU - Massimiliano Bonomi AU - Samuel Hanot AU - Charles H. Greenberg AU - Andrej Sali AU - Michael Nilges AU - Michele Vendruscolo AU - Riccardo Pellarin Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/08/08/113951.abstract N2 - 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.HighlightsWe present a modeling approach to integrate cryo-EM data with other sources of informationWe benchmark our approach using synthetic data on 21 complexes of known structureWe apply our approach to the GroEL/GroES, RNA polymerase II, and exosome complexes ER -