2024Fan CryoTrans
Citation
Fan, Xiao / Zhang, Qi / Zhang, Hui / Zhu, Jianying / Ju, Lili / Shi, Zuoqiang / Hu, Mingxu / Bao, Chenglong. CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM. 2024. Communications Biology, Vol. 7, No. 1, p. 1058
Abstract
Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single highresolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.
Keywords
Links
https://www.nature.com/articles/s42003-024-06739-9