Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps †
1
Computing and Software Systems, University of Washington, Bothell, WA 98011, USA
2
Department of Anatomy and Cell Biology, McGill University, Montreal, QC H3A 0C7, Canada
*
Authors to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Washington, DC, USA, 29 August–1 September 2018.
‡
These authors contributed equally to this work.
Molecules 2019, 24(6), 1181; https://doi.org/10.3390/molecules24061181
Received: 15 January 2019
/
Revised: 27 February 2019
/
Accepted: 14 March 2019
/
Published: 26 March 2019
(This article belongs to the Special Issue Selected Papers from the 11th Computational Structural Bioinformatics Workshop (CSBW-2018))
Cryo-electron microscopy (cryo-EM) is becoming the imaging method of choice for determining protein structures. Many atomic structures have been resolved based on an exponentially growing number of published three-dimensional (3D) high resolution cryo-EM density maps. However, the resolution value claimed for the reconstructed 3D density map has been the topic of scientific debate for many years. The Fourier Shell Correlation (FSC) is the currently accepted cryo-EM resolution measure, but it can be subjective, manipulated, and has its own limitations. In this study, we first propose supervised deep learning methods to extract representative 3D features at high, medium and low resolutions from simulated protein density maps and build classification models that objectively validate resolutions of experimental 3D cryo-EM maps. Specifically, we build classification models based on dense artificial neural network (DNN) and 3D convolutional neural network (3D CNN) architectures. The trained models can classify a given 3D cryo-EM density map into one of three resolution levels: high, medium, low. The preliminary DNN and 3D CNN models achieved 92.73% accuracy and 99.75% accuracy on simulated test maps, respectively. Applying the DNN and 3D CNN models to thirty experimental cryo-EM maps achieved an agreement of 60.0% and 56.7%, respectively, with the author published resolution value of the density maps. We further augment these previous techniques and present preliminary results of a 3D U-Net model for local resolution classification. The model was trained to perform voxel-wise classification of 3D cryo-EM density maps into one of ten resolution classes, instead of a single global resolution value. The U-Net model achieved 88.3% and 94.7% accuracy when evaluated on experimental maps with local resolutions determined by MonoRes and ResMap methods, respectively. Our results suggest deep learning can potentially improve the resolution evaluation process of experimental cryo-EM maps.
View Full-Text
Keywords:
computational structural biology; cryo-electron microscopy; deep learning; resolution validation
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Avramov, T.K.; Vyenielo, D.; Gomez-Blanco, J.; Adinarayanan, S.; Vargas, J.; Si, D. Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps †. Molecules 2019, 24, 1181. https://doi.org/10.3390/molecules24061181
AMA Style
Avramov TK, Vyenielo D, Gomez-Blanco J, Adinarayanan S, Vargas J, Si D. Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps †. Molecules. 2019; 24(6):1181. https://doi.org/10.3390/molecules24061181
Chicago/Turabian StyleAvramov, Todor K., Dan Vyenielo, Josue Gomez-Blanco, Swathi Adinarayanan, Javier Vargas, and Dong Si. 2019. "Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps †" Molecules 24, no. 6: 1181. https://doi.org/10.3390/molecules24061181
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
