Dataset Information

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

ABSTRACT: Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a challenge. We report a computational method, Emap2sec, that identifies the secondary structures of proteins (?-helices, ?-sheets and other structures) in EM maps at resolutions of between 5 and 10?Å. Emap2sec uses a three-dimensional deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on EM maps simulated from 34 structures at resolutions of 6.0 and 10.0?Å, as well as on 43 maps determined experimentally at resolutions of between 5.0 and 9.5?Å. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods.

SUBMITTER: Maddhuri Venkata Subramaniya SR

PROVIDER: S-EPMC6717539 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

Maddhuri Venkata Subramaniya Sai Raghavendra SR Terashi Genki G Kihara Daisuke D

Nature methods 20190729 9

Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a challenge. We report a computational method, Emap2sec, that identifies the secondary structures of proteins (α-helices, β-sheets and other structures) in EM maps at resolutions of between 5 and 10 Å. Emap2sec uses a three-dimensional deep convolutio ...[more]

PMID: 31358979

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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

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

Publications

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

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