Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many mole...
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770–778 (IEEE, 2016). Abadi, M. et al. TensorFlow: a system for large-scale machine learning. Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation 265–283 (IEEE, 2016). Iudin, A., ...
CryoSPARC is the state-of-the-art platform used globally for obtaining 3D structural information from single particle cryo-EM data. The cryoSPARC platform enables automated, high quality and high-throughput structure discovery of proteins, viruses and m
1.1. Optionally, create a virtual environment and activate it pip install virtualenv virtualenv --system-site-packages -p python3 ./deepEMhancer_env source ./deepEMhancer_env/bin/activate Install deepEMhancer (using Tensorflow 1.14) For CPU only use (expect running times ~ 24h) ...
When the copies of the macromolecule/assembly are different from each other, cryo-electron tomography (cryo-ET) can be carried out (the sample needs to be rotated and images at different rotating angles will be collected). Due to the averaging of many identical copies of the same ...
The last section summarizes our contribution to Cryo-EM, ET, and SPA in the last eight years and discusses possible future directions of the research. 2. New Programs and Protocols The image processing pipeline of the Cryo-EM project might be very complicated. However, it is typically divided...
The neural network models were constructed and executed using the Python API of Tensorflow release version 1.5 software package (Google Brain Team, Google, Mountain View, CA, USA) on a Linux node with NVIDIA TITAN X (Pascal) (Nvidia Corp., Santa Clara, CA, USA) with 12 GB GPU memory. ...