Dataset Information

High-Resolution Cryo-EM Maps and Models: A Crystallographer's Perspective.

ABSTRACT: The appearance of ten high-resolution cryoelectron microscopy (cryo-EM) maps of proteins, ribosomes, and viruses was compared with the experimentally phased crystallographic electron density maps of four proteins. We found that maps calculated at a similar resolution by the two techniques are quite comparable in their appearance, although cryo-EM maps, even when sharpened, seem to be a little less detailed. An analysis of models fitted to the cryo-EM maps indicated the presence of significant problems in almost all of them, including incorrect geometry, clashes between atoms, and discrepancies between the map density and the fitted models. In particular, the treatment of the atomic displacement (B) factors was meaningless in almost all analyzed cryo-EM models. Stricter cryo-EM structure deposition standards and their better enforcement are needed.

SUBMITTER: Wlodawer A

PROVIDER: S-EPMC5657611 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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High-Resolution Cryo-EM Maps and Models: A Crystallographer's Perspective.

Wlodawer Alexander A Li Mi M Dauter Zbigniew Z

Structure (London, England : 1993) 20170831 10

The appearance of ten high-resolution cryoelectron microscopy (cryo-EM) maps of proteins, ribosomes, and viruses was compared with the experimentally phased crystallographic electron density maps of four proteins. We found that maps calculated at a similar resolution by the two techniques are quite comparable in their appearance, although cryo-EM maps, even when sharpened, seem to be a little less detailed. An analysis of models fitted to the cryo-EM maps indicated the presence of significant pr ...[more]

PMID: 28867613

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Project description:Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (<2.5?Å resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict C? atoms along a protein's backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a protein's structure. This model predicts secondary structure elements (SSEs), backbone structure, and C? atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each protein density map. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with C? placements. A helix-refinement algorithm made further improvements to the ?-helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map protein sequences onto C? traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6?Å and 4.4?Å resolution. It outperformed several state-of-the-art prediction methods including Rosetta de-novo, MAINMAST, and a Phenix based method by producing the most complete predicted protein structures, as measured by percentage of found C? atoms. This method accurately predicted 88.9% (mean) of the C? atoms within 3?Å of a protein's backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average root-mean-square deviation (RMSD) of 1.24?Å on a set of 50 experimental density maps which was tested by the Phenix based fully automatic method. The source code and demo of this research has been published at https://github.com/DrDongSi/Ca-Backbone-Prediction.

Dataset Information

High-Resolution Cryo-EM Maps and Models: A Crystallographer's Perspective.

Publications

High-Resolution Cryo-EM Maps and Models: A Crystallographer's Perspective.

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