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CryoSegNet: Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net

CryoSegNet is a method using foundational image segmentation model for picking protein particles in cryo-EM micrographs. It is trained on 22 different protein types including membrane protein, signaling protein, transport protein, viral protein, ribosomes, etc. It uses U-Net and SAM's automatic mask generator for predicting the protein particles coordinates from the cryo-EM micrographs and generates output in the form of .star file which can be used in popular tools like RELION and CryoSPARC for generating 3D density maps. It has achieved the state-of-the-art performance and has surpassed the popular AI pickers like crYOLO and Topaz.

Overview

Figure below demonstrates the overview of particle picking process used by CryoSegNet.

Alt text

Installation

Clone project

git clone https://github.com/jianlin-cheng/CryoSegNet.git
cd CryoSegNet/

Download trained models

curl https://calla.rnet.missouri.edu/CryoSegNet/pretrained_models.tar.gz --output pretrained_models.tar.gz
tar -xvf pretrained_models.tar.gz
rm pretrained_models.tar.gz

Download training data (if required)

curl https://calla.rnet.missouri.edu/CryoSegNet/train_dataset.tar.gz --output train_dataset.tar.gz
tar -xvf train_dataset.tar.gz
rm train_dataset.tar.gz

Download test data

curl https://calla.rnet.missouri.edu/CryoSegNet/test_dataset.tar.gz --output test_dataset.tar.gz
tar -xvf test_dataset.tar.gz
rm test_dataset.tar.gz

Create conda environment

conda env create -f environment.yml
conda activate cryosegnet

Training Data Statistics

SN EMPAIR ID Protein Type Image Size Total Structure Weight (kDa) Training Images Validation Images Total Images
1 10005 TRPV1 Transport Protein (3710,3710) 272.97 23 6 29
2 10059 TRPV1 Transport Protein (3838,3710) 317.88 232 59 291
3 10075 Bacteriophage MS2 (4096,4096) 1000* 239 60 299
4 10077 Ribosome (70S) (4096,4096) 2198.78 240 60 300
5 10096 Viral Protein (3838,3710) 150* 240 60 300
6 10184 Aldolase (3838,3710) 150* 236 60 296
7 10240 Lipid Transport Protein (3838,3710) 171.72 239 60 299
8 10289 Transport Protein (3710,3838) 361.39 240 60 300
9 10291 Transport Protein (3710,3838) 361.39 240 60 300
10 10387 Viral Protein (3710,3838) 185.87 239 60 299
11 10406 Ribosome (70S) (3838,3710) 632.89 191 48 139
12 10444 Membrane Protein (5760,4092) 295.89 236 60 296
13 10526 Ribosome (50S) (7676,7420) 1085.81 176 44 220
14 10590 TRPV1 Transport Protein (3710,3838) 1000* 236 60 296
15 10671 Signaling Protein (5760,4092) 77.14 238 60 298
16 10737 Membrane Protein (5760,4092) 155.83 233 59 292
17 10760 Membrane Protein (3838,3710) 321.69 240 60 300
18 10816 Transport Protein (7676,7420) 166.62 240 60 300
19 10852 Signaling Protein (5760,4092) 157.81 274 69 343
20 11051 Transcription/DNA/RNA (3838,3710) 357.31 240 60 300
21 11057 Hydrolase (5760,4092) 149.43 236 59 295
22 11183 Signaling Protein (5760,4092) 139.36 240 60 300
Total 4,948 1,244 6,192

Test Data Statistics

SN EMPAIR ID Protein Type Image Size Total Structure Weight (kDa) Total Images
1 10017 β -galactosidase  (4096,4096)  450*  84
2 10028 Ribosome (80S)  (4096,4096)  2135.89 300
3 10081 Transport Protein  (3710,3838)  298.57 300
4 10093 Membrane Protein  (3838,3710)  779.4 295
5 10345 Signaling Protein  (3838,3710)  244.68 295
6 10532 Viral Protein  (4096,4096)  191.76 300
7 11056 Transport Protein  (5760,4092)  88.94 305
Total  1879

Prediction on Your Own Data

Prediction on your own Data (generate star file for usage in tools like CryoSPARC)

This section allows you to pick protein particles and generate .star file which can be used in tools like CryoSPARC for further post-processing.

If you have your own dataset available in .jpg format, place them under the directory my_dataset and run:

python generate_starfile_new_data_jpg.py --file_name abc.star

If you have your own dataset available in .mrc format, place them under the directory my_dataset and run:

python generate_starfile_new_data_mrc.py --file_name abc.star

Optional Arguments:
  --my_dataset_path (str, default: "my_dataset"): Path to your own dataset.
  --output_path (str, default: "output"): Output directory.
  --device (str, default: "cuda:0" if available, else "cpu"): Device for training (cuda:0 or cpu).
  --file_name (str, default="abc.star): Filename for picked proteins coordinates.

Prediction on your own Data (predict proteins on micrographs)

This section allows you to pick protein particles and represent them by circles in the micrographs.

If you have your own dataset available in .jpg format, place them under the directory my_dataset and run:

python predict_new_data_jpg.py

If you have your own dataset available with motion correction in .mrc format, place them under the directory my_dataset and run.

python predict_new_data_mrc.py

Optional Arguments:
  --my_dataset_path (str, default: "my_dataset"): Path to your own dataset.
  --output_path (str, default: "output"): Output directory.
  --device (str, default: "cuda:0" if available, else "cpu"): Device for training (cuda:0 or cpu).

If you use the motion corrected dataset by CryoSPARC, place them under the directory my_dataset and remove the id appended in the beginning of filename for each micrographs. To remove the id appended in the beginning of each micrograph you may use the following command:

python remove_id.py

If Patch CTF Estimation job in CryoSPARC fails fails for some of your micrographs remove those micrographs from my_dataset folder and run:

python predict_new_data_mrc.py

Optional Arguments:
  --my_dataset_path (str, default: "my_dataset"): Path to your own dataset.
  --output_path (str, default: "output"): Output directory.
  --device (str, default: "cuda:0" if available, else "cpu"): Device for training (cuda:0 or cpu).

After getting the star file you may use this file in CryoSPARC for further processing:

1. Import Particles

From the builder in CryoSPARC, select the Import Particles job to import the particles available in star file. This job expects:

  • Inputs: Output of CTF Estimated Job
  • Particle meta path: Path to star file output from CryoSegNet
  • Remove leading UID in input micrograph file name: enabled

Alt text

2. Extract Mics.

From the builder in CryoSPARC, select the Extract Mics. job to extract the particles. This job expects:

  • Inputs: Output of Patch CTF job and Output of Import Particles job
  • Extraction box size (pix): Box size in pixels, by default 256

Alt text

After the particles are extracted with this job, you may run other jobs like 2D Class, Select 2D, Ab-Initio, Homo Refine, etc depending upon your interest.

Prediction on EMPIAR Test Data (available in directory test_dataset)

Prediction on Test Data (generate star file for usage in tools like CryoSPARC)

This function generates output in the form of .star file which can be utilized in tools like CryoSPARC for further steps like selecting the 2D classes, 3D reconstruction and so on.

python generate_starfile.py --empiar_id 10081 --file_name 10081.star

Optional Arguments:
  --test_dataset_path (str, default: "test_dataset"): Path to the test dataset.
  --output_path (str, default: "output"): Output directory.
  --device (str, default: "cuda:0" if available, else "cpu"): Device for training (cuda:0 or cpu).
  --empiar_id (str, default: "10081"): EMPIAR ID for prediction. 
  --file_name (str, default="10081.star): Filename for picked proteins coordinates.

Prediction on Test Data (predict proteins on micrographs)

This function outputs micrographs with predicted proteins represented by circles.

python predict.py --empiar_id 10081

Optional Arguments:
  --test_dataset_path (str, default: "test_dataset"): Path to the test dataset.
  --output_path (str, default: "output"): Output directory.
  --device (str, default: "cuda:0" if available, else "cpu"): Device for training (cuda:0 or cpu).
  --empiar_id (str, default: "10081"): EMPIAR ID for prediction.

Training (if required)

python train.py

Optional Arguments:
  --train_dataset_path (str, default: "train_dataset"): Path to the training dataset.
  --device (str, default: "cuda:0" if available, else "cpu"): Device for training (cuda:0 or cpu).
  --pin_memory (flag): Enable pin_memory for data loading if using CUDA.
  --num_workers (int, default: 8): Number of data loading workers.
  --num_channels (int, default: 1): Number of input channels.
  --num_classes (int, default: 1): Number of classes.
  --num_levels (int, default: 3): Number of levels in the model.
  --learning_rate (float, default: 0.0001): Learning rate.
  --num_epochs (int, default: 200): Number of training epochs.
  --batch_size (int, default: 6): Batch size.
  --input_image_width (int, default: 1024): Input image width.
  --input_image_height (int, default: 1024): Input image height.
  --input_shape (int, default: 1024): Input image shape.
  --logging (flag): Enable logging for wandb.
  --architecture_name : Model architecture name.

Example Usage:
    python train.py --batch_size 12 --learning_rate 0.001 --num_epochs 10 --architecture_name "my_custom_model"

Finetuning on your own dataset

  1. You need a star file to have coordinates of proteins picked manually. Refer to finetune_dataset/sample.star and make your star file in the same format

  2. Place all .mrc files inside finetune_dataset/mrc_files/ directory

  3. Denoise all the .mrc files and they will be stored inside finetune_dataset/images/ directory

Run:

python utils/generate_jpg.py
  1. Generate masks for images. Masks will be stored inside finetune_dataset/masks/ directory

Run:

python utils/generate_masks.py --file_name finetune_dataset/sample.star

You need to input the diameter size of protein in pixel value.

  1. Finetune the CryoSegNet Model
python finetune.py --train_dataset_path finetune_dataset/

Optional Arguments:
  --device (str, default: "cuda:0" if available, else "cpu"): Device for training (cuda:0 or cpu).
  --pin_memory (flag): Enable pin_memory for data loading if using CUDA.
  --num_workers (int, default: 8): Number of data loading workers.
  --num_epochs (int, default: 200): Number of training epochs.
  --batch_size (int, default: 6): Batch size.

Evaluation

Find the Precision, Recall, F1-Score and Dice Score

curl https://calla.rnet.missouri.edu/CryoSegNet/Evaluation/Groundtruth.tar.gz --output Evaluation/Groundtruth.tar.gz
curl https://calla.rnet.missouri.edu/CryoSegNet/Evaluation/General.tar.gz --output Evaluation/General.tar.gz
tar -xvf Evaluation/Groundtruth.tar.gz -C Evaluation/
tar -xvf Evaluation/General.tar.gz -C Evaluation/
rm Evaluation/Groundtruth.tar.gz
rm Evaluation/General.tar.gz

python utils/precision_recall.py --test_dataset_path Evaluation/Groundtruth

Rights and Permissions

Open Access
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Cite this work

If you use the code or data associated with this research work or otherwise find this data useful, please cite:
@article {Gyawali2023,
author = {Gyawali, Rajan and Dhakal, Ashwin and Wang, Liguo and Cheng, Jianlin},
title = {Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net },
year = {2023} }