Abstract
AbstractIn 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. Herein, 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 RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as 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.
Highlights
The resolution revolution in single particle electron cryomicroscopy yields macromolecular structures of unprecedented resolution
In low-resolution cryo-EM maps, a-helices can often be discerned as long cylindrical elements. This has been exploited by the program helixhunter,[11] which searches for prototypical helices in reconstruction maps using a cross-correlation strategy. b-Strands are more difficult to identify as they are more variable in shape and require morphological analysis.[12]
We employ a state of the art U-Net-style architecture[9] to demonstrate that at an average map resolution of 4 or better, experimentally derived reconstruction maps allow the training of a well-performing network that can be used for a wide range of specimens— with no re-training necessary
Summary
The resolution revolution in single particle electron cryomicroscopy (cryo-EM) yields macromolecular structures of unprecedented resolution. These structures allow us to identify new drug targets, for example in the Zika virus,[1] to fight tuberculosis[2] or to understand the fundamental processes of life, such as the process of translation by ribosomes.[3].
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