CryoSTAR

🏠 Homepage | 📚 User Guide

CryoSTAR is a neural network based framework for recovering conformational heterogenity of protein complexes. By leveraging the structural prior and constraints from a reference pdb model, cryoSTAR can output both the protein structure and density map.

User Guide

The detailed user guide can be found at here. This comprehensive guide provides in-depth information about the topic at hand. Feel free to visit the link if you're seeking more knowledge or need extensive instructions regarding the topic.

Installation

• Create a conda environment: conda create -n cryostar python=3.9 -y
• Clone this repository and install the package: git clone https://github.com/bytedance/cryostar.git && cd cryostar && pip install .

Quick start

Preliminary

You may need to prepare the resources below before running cryoSTAR:

• a concensus map (along with each particle's pose)
• a pdb file (which has been docked into the concensus map)

Training

CryoSTAR operates through a two-stage approach where it independently trains an atom generator and a density generator. Here's an illustration of its process:

S1: Training the atom generator

In this step, we generate an ensemble of coarse-grained protein structures from the particles. Note that the pdb file is used in this step and it should be docked into the concensus map!

shell cd projects/star python train_atom.py atom_configs/1ake.py

The outputs will be stored in the work_dirs/atom_xxxxx directory, and we perform evaluations every 12,000 steps. Within this directory, you'll observe sub-directories with the name epoch-number_step-number. We choose the most recent directory as the final results.

text atom_xxxxx/ ├── 0000_0000000/ ├── ... ├── 0112_0096000/ # evaluation results │ ├── ckpt.pt # model parameters │ ├── input_image.png # visualization of input cryo-EM images │ ├── pca-1.pdb # sampled coarse-grained atomic structures along 1st PCA axis │ ├── pca-2.pdb │ ├── pca-3.pdb │ ├── pred.pdb # sampled structures at Kmeans cluster centers │ ├── pred_gmm_image.png │ └── z.npy # the latent code of each particle | # a matrix whose shape is num_of_particle x 8 ├── yyyymmdd_hhmmss.log # running logs ├── config.py # a backup of the config file └── train_atom.py # a backup of the training script

S2: Training the density generator

In step 1, the atom generator assigns a latent code z to each particle image. In this step, we will drop the encoder and directly use the latent code as a representation of a partcile. You can execute the subsequent command to initiate the training of a density generator.

```shell

change the xxx/z.npy path to the output of the above command

python traindensity.py densityconfigs/1ake.py --cfg-options extrainputdataattr.givenz=xxx/z.npy ```

Results will be saved to work_dirs/density_xxxxx, and each subdirectory has the name epoch-number_step-number. We choose the most recent directory as the final results.

text density_xxxxx/ ├── 0004_0014470/ # evaluation results │ ├── ckpt.pt # model parameters │ ├── vol_pca_1_000.mrc # density sampled along the PCA axis, named by vol_pca_pca-axis_serial-number.mrc │ ├── ... │ ├── vol_pca_3_009.mrc │ ├── z.npy │ ├── z_pca_1.txt # sampled z values along the 1st PCA axis │ ├── z_pca_2.txt │ └── z_pca_3.txt ├── yyyymmdd_hhmmss.log # running logs ├── config.py # a backup of the config file └── train_density.py # a backup of the training script

Reference

You may cite this software by: bibtex @article{li2023cryostar, author={Li, Yilai and Zhou, Yi and Yuan, Jing and Ye, Fei and Gu, Quanquan}, title={CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction}, journal={Nature Methods}, year={2024}, month={Oct}, day={29}, issn={1548-7105}, doi={10.1038/s41592-024-02486-1}, url={https://doi.org/10.1038/s41592-024-02486-1} }