CryoSTAR

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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!

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.

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.

# 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.

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:

@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}
}