Cryo-electron microscopy (cryo-EM) is an experimental technique that captures 2D images of biological molecules and assemblies (protein particles, virus, etc.) at cryogenic temperature using ‘direct’ electron-detection camera technology1. With the advent of cryo-EM, there has been a boom in str...
However, since the SPA problem is a non-convex optimization problem with enormous search space and there is high level of noise in the input images, the existing methods may produce biased or even wrong final models. In this work, to deal with the problem, consistent constraints from the ...
This method encodes structures in Fourier space using coordinate-based deep neural networks, and trains these networks from unlabeled 2D cryo-EM images by combining exact inference over image orientation with variational inference for structural heterogeneity. We demonstrate that the proposed method, ...
Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in...
Cryogenic electron microscopy (cryo-EM) provides images from different copies\nof the same biomolecule in arbitrary orientations. Here, we present an\nend-... YSG Nashed,F Poitevin,H Gupta,... 被引量: 0发表: 2021年 Cryo-Electron Microscopy of Aura Viruses Alphaviruses are a group of envelo...
The cornerstone of both techniques is the registration of images: 2D images in cryo-EM and 3D images in cryo-ET. There are several registration methods for 2D and 3D cryo-EM images; however, it is hard to evaluate these methods due to the lack of ground truth for real data. M...
crystallography cryo-em phenix cctbx Updated Dec 20, 2024 Python tbepler / topaz Star 181 Code Issues Pull requests Discussions Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micr...
Cryo-EM images of phase-separated lipid bilayer vesicles analyzed with a machine-learning approach Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melti... KD Sharma,M Doktorova,MN Waxham,.....
CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networkswww.nature.com/articles/s41592-020-01049-4 Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEsarxiv.org/abs/2106.14108发布...