Data for Exploring Conformational Landscape of Cryo-EM Using Energy-Aware Pathfinding Algorithm

There are four experiments. The main.ipynb notebook in each experiment directory details the pathfinding process and evaluates different algorithms.

Hsp90 (PDB ID: 2CG9): A synthetic dataset with two degrees of conformational changes. The .mrcs and .star files (convert into .pkl file first use cryodrgn functions) serve as training inputs for the cryoDRGN model.

NLRP3 (PDB ID: 6NPY): A synthetic dataset with three degrees of conformational changes. The .mrcs and .star files (convert into .pkl file first use cryodrgn functions) serve as training inputs for the cryoDRGN model.

EMPIAR-10076 in original latent space.

EMPIAR-10076 with Analysis Landscape Pipeline: We also adhere to the analyze landscape pipeline designed by the cryoDRGN team. The Jupyter notebooks in that directory illustrate our implementation of this pipeline for the EMPIAR-10076 dataset, starting with 1_sketching.ipynb and concluding in 2_learning_mapping.ipynb.

In the EMPIAR-10076 experiment, we utilize the training weights provided by the official cryoDRGN team. The complete weights and further information can be found here. Additionally, we filter out states unrelated to conformational changes, following the guidance of the labels provided by the cryoDRGN team here.