Dataset Information

Evaluating Local and Directional Resolution of Cryo-EM Density Maps.

ABSTRACT: A systematic and quantitative evaluation of cryo-EM maps is necessary to judge their quality and to capture all possible sources of error. A single value for global resolution is insufficient to accurately describe the quality of a reconstructed density. We describe the estimation and evaluation of two additional resolution measures, local and directional resolution, using methods based on the Fourier shell correlation (FSC). We apply the protocol to samples that encompass different types of pathologies a user is expected to encounter and provide analyses on how to interpret the output files and resulting maps. Implementation of these tools will facilitate density interpretation and can guide the user in adapting their experiments to improve the quality of cryo-EM maps, and by extension atomic models.

SUBMITTER: Aiyer S

PROVIDER: S-EPMC8294179 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Evaluating Local and Directional Resolution of Cryo-EM Density Maps.

Aiyer Sriram S Zhang Cheng C Baldwin Philp R PR Lyumkis Dmitry D

Methods in molecular biology (Clifton, N.J.) 20210101

A systematic and quantitative evaluation of cryo-EM maps is necessary to judge their quality and to capture all possible sources of error. A single value for global resolution is insufficient to accurately describe the quality of a reconstructed density. We describe the estimation and evaluation of two additional resolution measures, local and directional resolution, using methods based on the Fourier shell correlation (FSC). We apply the protocol to samples that encompass different types of pat ...[more]

PMID: 33368004

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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 Cα 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 Cα 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 Cα placements. A helix-refinement algorithm made further improvements to the α-helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map protein sequences onto Cα 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 Cα atoms. This method accurately predicted 88.9% (mean) of the Cα 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

Evaluating Local and Directional Resolution of Cryo-EM Density Maps.

Publications

Evaluating Local and Directional Resolution of Cryo-EM Density Maps.

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