RT Journal Article
SR Electronic
T1 Multi-scale Bayesian modeling of cryo-electron microscopy density maps
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 113951
DO 10.1101/113951
A1 Samuel Hanot
A1 Massimiliano Bonomi
A1 Charles H. Greenberg
A1 Andrej Sali
A1 Michael Nilges
A1 Michele Vendruscolo
A1 Riccardo Pellarin
YR 2017
UL http://biorxiv.org/content/early/2017/03/04/113951.abstract
AB Cryo-electron microscopy has become a mainstream structural biology technique by enabling the characterization of biological architectures that for many years have eluded traditional methods like X- ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy. However, the translation of cryo-electron microscopy data into accurate structural models is hampered by the presence of random and systematic errors in the data, sample heterogeneity, data correlation, and noise correlation. As a consequence, in integrative biology approaches, it has been difficult to objectively weigh EM- derived restraints with respect to other sources of information. To address these challenges, here we introduce a Bayesian approach that allows efficient and accurate structural modeling of cryo-electron microscopy density maps at multiple scales, from coarse-grained to atomistic resolution. The accuracy of the method is benchmarked using a set of structures of macromolecular assemblies. The approach is implemented in the open-source Integrative Modeling Platform package (http://integrativemodeling.org) in order to enable structural determination by combining cryo-electron microscopy with other information, such as chemical cross-linking/mass spectrometry, NMR, and small angle X-ray scattering data.