@inproceedings{
zhou2023cryostar,
title={Cryo{STAR}: Cryo-{EM} Heterogeneous Reconstruction of Atomic Models with Structural Regularization},
author={Yi Zhou and Yilai Li and Jing Yuan and Fei YE and Quanquan Gu},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=Jjcd2SaH9B}
}
Atomic models, which directly represent molecular structural variations (i.e., conformation), have received increasing attention in the field of cryo-electron microscopy (cryo-EM) heterogeneity analysis. However, the nonconvex nature of the structural space (the space of atomic coordinates) poses a significant challenge to finding a physical-plausible solution. In this paper, we address this challenge by proposing a novel approach, named cryoSTAR, with the aim of reconstructing atomic models from cryo-EM images. 
Our approach is motivated by the observation that weak regularization allows atomic models to be excessively flexible in the search space, resulting in a loss of local structural fidelity, while strong regularization tends to trap atomic models in the neighborhood of the initial structure, limiting their ability to explore the conformational landscape effectively. To strike a balance, we introduce adaptive structural regularization at the atomic level to modulate the reconstruction process. We relax the flexible region adaptively to allow for greater conformational changes. Our method achieves the lowest RMSD (up to a maximum decrease of 7.14\AA) on a synthetic dataset, and uncovers reasonable dynamics on an experimental dataset, highlighting its generalizability across different protein systems. Our work sheds light on the potential of atomic models as an alternative to traditional volumetric density maps for cryo-EM heterogeneous reconstruction.
CryoSTAR: Cryo-EM Heterogeneous Reconstruction of Atomic Models with Structural Regularization
Yi Zhou Yilai Li Jing Yuan Fei YE Quanquan Gu