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Biophys J. 2016 Feb 23;110(4):785-97. doi: 10.1016/j.bpj.2015.12.038. Epub 2016 Feb 1.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling.

Author information

1
Physik-Department T38, Technische Universität München, Garching, Germany. Electronic address: sjoerd@tum.de.
2
Physik-Department T38, Technische Universität München, Garching, Germany.
3
Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany.

Abstract

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.

PMID:
26846888
PMCID:
PMC4776041
DOI:
10.1016/j.bpj.2015.12.038
[Indexed for MEDLINE]
Free PMC Article

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