Automated interpretation of Cryo-EM density maps with convolutional neural networks

Abstract

In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (Cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Here, we demonstrate the potential of convolutional neural networks for the annotation of Cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate protein secondary structure elements as well as RNA/DNA. It can be straightforwardly applied to annotate newly reconstructed maps to support domain placement or to supply a starting point for main-chain placement. Due to its high recall and precision rates of 95.1% and 80.3%, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.