2020Mullick SuperResolution

Citation

Mullick, B.; Wang, Y.; Yadav, P. & Farimani, A. B. Learning Super-Resolution Electron Density Map of Proteins using 3D U-Net. Proc. Machine Learning for Structural Biology Workshop, 2020

Abstract

A well-established protein structure is essential for understanding protein molecular mechanism, phenotypic implication and drug discovery. Recent development of cryo-Electron Microscopy (cryo-EM) offers the advantage of easy sample preparation and not requiring crystallized protein for structural biology. However, the resolution of cryo-EM electron density maps used to determine protein structure, is not at par with X-ray diffraction (XRD) or NMR. In this work, we propose to leverage a deep learning-based model to increase the resolution of low-quality electron density maps. The model is built upon U-Net with 3D convolutional layers, which contains three components: encoder, bottleneck, and decoder. To get paired maps of different resolutions, we collect high-resolution maps from XRD as ground truth labels. While the low-resolution maps are obtained through a noise model which combines dilation operations, Gaussian filters and Gaussian noise. We also introduce data augmentation techniques during model training, like random cropping, rotation, and flipping. Experiments show that when applied to low-resolution electron maps, the U-Net model can improve the resolution in the metric of EMRinger score, which redesigns the map so that it resolves the regions of ambiguity to offer greater certainty in the position of amino acids.

Links

https://www.mlsb.io/papers/MLSB2020_Learning_Super-Resolution_Electron_Density.pdf