![]() Some of these studies involve very large protein assemblies of many subunits. The cryo-EM field is slowly moving to allow many high-resolution maps produced in one project or study 12, 13. In the past five years, more than 1,000 protein structures have been determined at 4 Å resolution or better in the EM databank using single-particle reconstruction 7, 8, 9, 10, 11. Due to the averaging of many identical copies of the same macromolecule in single-particle reconstruction, the achieved resolution can be very high, even beyond 2 Å resolution 5, 6. When the copies of the macromolecule/assembly are different from each other, cryo-electron tomography (cryo-ET) can be carried out (the sample needs to be rotated and images at different rotating angles will be collected). There is a density value associated with each voxel (volume pixel) of the map. A 3D density map is represented as values on a 3D grid. When there are many identical copies of the macromolecule in the sample (different views of the macromolecule are present in the collected images), single particle reconstruction can be carried out to reconstruct the 3D density map of the macromolecules. Briefly, samples are fast frozen in liquid-nitrogen cooled liquid ethane and imaged in an electron microscope at cryogenic temperatures. Cryo Electron Microscopy (Cryo-EM)Ĭurrently, one of the leading techniques for determining the atomic structure of proteins is cryo-electron microscopy (cryo-EM) 2, 3, 4. Therefore, learning the details of a protein’s 3D structure is a prerequisite to understanding its biological function. Despite each protein being composed from a combination of the same 20 naturally occurring amino acids, a protein’s functionality is mainly derived from its unique three-dimensional (3D) shape. ![]() From molecule transportation, to mechanical cellular support, to immune protection, proteins are the central building blocks of life in the universe 1. Proteins perform a vast array of functions within organisms. The source code and demo of this research has been published at. 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. 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. 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 was tested on 50 experimental maps between 2.6 Å and 4.4 Å resolution. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map protein sequences onto Cα traces to obtain full-atom protein structures. A helix-refinement algorithm made further improvements to the α-helix SSEs of the backbone trace. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with Cα placements. ![]() This method is largely automatic and only requires a recommended threshold value for each protein density map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. 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 (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a protein’s structure. 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. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (<2.5 Å resolution). Recent advances in this field have allowed for atomic resolution. Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. ![]()
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