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.Proteins perform a vast array of functions within organisms. From molecule transportation, to mechanical cellular support, to immune protection, proteins are the central building blocks of life in the universe 1 . 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. Therefore, learning the details of a protein's 3D structure is a prerequisite to understanding its biological function. cryo electron Microscopy (cryo-eM). Currently, one of the leading techniques for determining the atomic structure of proteins is cryo-electron microscopy (cryo-EM) 2-4 . Briefly, samples are fast frozen in liquid-nitrogen cooled liquid ethane and imaged in an electron microscope at cryogenic te...

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 (< 2.5Å 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 evaluated protein. 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 Cα traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6Å and 4.4Å resolution. It outperformed several state-of-the-art prediction methods including RosettaES, MAINMAST, and a Phenix based method by producing the most complete prediction models, as measured by percentage of found Cα atoms. This method accurately predicted 88.5% (mean) of the Cα atoms within 3Å 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 RMSD of 1.23Å for all 50 experimental density maps which is similar to the Phenix based fully automatic method. This model and all code can be downloaded at https://github.com/DrDongSi/Ca-Backbone-Prediction. I. Introduction.Proteins perform a vast array of functions within organisms. From molecule transportation, to mechanical cellular support, to immune protection, proteins are the central building blocks of life in the universe [1]. 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 threedimensional (3D) shape. Therefore, learning the details of a protein's 3D structure is a prerequisite to understanding its biological function. A. Cryogenic Electronic Microscopy (Cryo-EM)Currently, one of the leading techniques for determining the atomic structure of proteins is cryoelectron microscopy (cryo-EM). Cryo-EM is a relatively new technique which uses a high-energy electron beam to image vitrified biological specimens. In the past five years, more than 1,000 protein structures have been determined to bet...