Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Abstract

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[A] resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict C atoms along a proteins backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a proteins 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 evaluated protein. 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 C traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6[A] and 4.4[A] 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 C atoms. This method accurately predicted 88.5% (mean) of the C atoms within 3[A] of a proteins 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 1.23[A] for all 50 experimental density maps which is similar to 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.