CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM.

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 high-resolu

SUBMITTER: Fan X 

PROVIDER: S-EPMC11350005 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature