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https://github.com/bytedance/cryostar
Leveraging Structural Prior and Constraints for Cryo-EM Heterogeneous Reconstruction
https://github.com/bytedance/cryostar
cryo-em research
Last synced: 1 day ago
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Leveraging Structural Prior and Constraints for Cryo-EM Heterogeneous Reconstruction
- Host: GitHub
- URL: https://github.com/bytedance/cryostar
- Owner: bytedance
- License: apache-2.0
- Created: 2023-11-06T07:15:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-16T03:23:19.000Z (about 1 month ago)
- Last Synced: 2024-10-17T16:41:23.552Z (about 1 month ago)
- Topics: cryo-em, research
- Language: Python
- Homepage: https://bytedance.github.io/cryostar/
- Size: 1.51 MB
- Stars: 35
- Watchers: 4
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CryoSTAR
[🏠 Homepage](https://bytedance.github.io/cryostar/) |
[📚 User Guide](https://byte-research.gitbook.io/cryostar/)
`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](https://byte-research.gitbook.io/cryostar/). 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 train_density.py density_configs/1ake.py --cfg-options extra_input_data_attr.given_z=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}
}
```