CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs

CryoTransformer is a powerful and accurate particle-picking framework using Residual Network (ResNet) and Transformer. CryoTransformer was trained using the largest diverse labelled CryoPPP dataset for the first time. It recognizes and extracts abundance amount of true protein particles from the input micrographs while maintaining low false-positive rates. We performed rigorous evaluation, comparing our method with existing AI based methods and showcasing its robustness through multiple labels of evaluation. Our model outperformed the current state-of-the art methods and is poised to greatly facilitate the automation of Cryo-EM particle recognition.

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Comparison of CryoTransformer with existing AI-Based Methods (EMPIAR ID: 10532)

CryoTransformer.Performance.Comparison.mp4

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Overview of CryoTransformer Pipeliine

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Installation

Clone project

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

Download trained models

curl https://calla.rnet.missouri.edu/CryoTransformer/pretrained_model.tar.gz --output pretrained_model.tar.gz
tar -xvf pretrained_model.tar.gz
rm pretrained_model.tar.gz

Download training data (if required)

curl https://calla.rnet.missouri.edu/CryoTransformer/train_val_test_data.tar.gz --output train_val_test_data.tar.gz
tar -xvf train_val_test_data.tar.gz
rm train_val_test_data.tar.gz

Download test data

curl https://calla.rnet.missouri.edu/CryoTransformer/test_data.tar.gz --output test_data.tar.gz
tar -xvf test_data.tar.gz
rm test_data.tar.gz

Create conda environment

conda env create -f environment.yml
conda activate CryoTransformer

Prediction on Test Data

This code generates the predicted proteins encircled in Micrographs along with the .box and .star files.

python predict.py 

Optional Arguments:
    --save_micrographs_with_encircled_proteins', default='Y', type=str, choices=['Y', 'N'], help='Plot predicted proteins on Micrographs')
    --empiar, default='10081', type=str, help='EMPIAR ID for prediction. It accepts Micrographs in both .mrc or .jpg format')

Example usage: 
    python predict.py --empiar '10081'

Training and Reproducing Results (if required)

python train.py

Optional Arguments:
    --epochs, default=300, type=int, help='Number of training epochs')
    --batch_size, default=8, type=int, help='Batch size for training')
    --device, default='cuda:0', type=str, help='CUDA device for GPU acceleration')
    --remarks, default='CryoTransformer_github', type=str, help='Additional remarks')
    --data_path, default='CryoTransformer/train_val_test_data', type=str, help='Path to the training dataset')

Example Usage:
    python train.py --epochs 300 --batch_size 16  

Training Data Statistics

Data statistics for Training, validating, and Testing CryoTransformer (* Theoretical weight)

SN	EMPIAR ID	Type of Protein	Micrograph Size	Total Structure Weight (kDa)	Training Micrographs	Validation Micrographs	Test Micrographs	Total Micrographs
1	11183​	Signaling Protein	(5760,4092)	139.36	250	25	25	300
2	11057​	Hydrolase	(5760,4092)	149.43	250	25	20	295
3	11051​	Transcription/DNA/RNA	(3838,3710)	357.31	250	25	25	300
4	10852​	Signaling Protein	(5760,4092)	157.81	270	40	33	343
5	10816​	Transport Protein	(7676,7420)	166.62	250	25	25	300
6	10760​	Membrane Protein	(3838,3710)	321.69	250	25	25	300
7	10737​	Membrane Protein	(5760,4092)	155.83	250	25	17	292
8	10671​	Signaling Protein	(5760,4092)	77.14	250	25	23	298
9	10590​	Transport Protein	(3710,3838)	1000*	250	25	21	296
10	10526​	Ribosome (50S)	(7676,7420)	1085.81	180	20	20	220
11	10444​	Membrane Protein	(5760,4092)	295.89	250	25	21	296
12	10406​	Ribosome (70S)	(3838,3710)	632.89	200	20	19	239
13	10387​	Viral Protein	(3710,3838)	185.87	250	25	24	299
14	10291​	Transport Protein	(3710,3838)	361.39	250	25	25	300
15	10289​	Transport Protein	(3710,3838)	361.39	250	25	25	300
16	10240​	Lipid Transport Protein	(3838,3710)	171.72	250	25	24	299
17	10184​	Aldolase	(3838,3710)	150*	250	25	21	296
18	10096​	Viral Protein	(3838,3710)	150*	250	25	25	300
19	10077​	Ribosome (70S)	(4096,4096)	2198.78	250	25	25	300
20	10075​	Bacteriophage MS2	(4096,4096)	1000*	250	25	24	299
21	10059​	Transport Protein	(3838,3710)	317.88	250	25	16	291
22	10005​	Transport Protein	(3710,3710)	272.97	22	4	3	29
		Total Micrographs			5,172	534	486	6,192

Independent Test Data Statistics

Data statistics used for independent Testing sourced from EMPIAR repository

SN	EMPIAR ID	Type of Protein	Micrograph Size	Total Str. Weight (kDa)	Number of Micrographs
1	10081	Transport Protein	(3710,3838)	298.57	997
2	10532	Viral Protein	(4096,4096)	191.76	1,556
3	10093	Membrane Protein	(3838,3710)	779.4	1,873
4	10345	Signaling Protein	(3838,3710)	244.68	1,644
		Total Micrographs			6,070

Data statistics used for independent Testing sourced from CryoPPP dataset (* Theoretical weight)

SN	EMPIAR ID	Type of Protein	Micrograph Size	Total Str. Weight (kDa)	Number of Micrographs
1	10017	β -galactosidase	(4096,4096)	450*	84
2	10081	Transport Protein	(3710,3838)	298.57	300
3	10093	Membrane Protein	(3838,3710)	779.4	295
4	10345	Signaling Protein	(3838,3710)	244.68	295
5	10532	Viral Protein	(4096,4096)	191.76	300
6	11056	Transport Protein	(5760,4092)	88.94	305
		Total Micrographs			1,579

Rights and Permissions

Open Access
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Cite this work

If you use the code or data associated with this research work or otherwise find this data useful, please cite:

CryoTransformer

@article {Dhakal2024,
author = {Ashwin Dhakal and Rajan Gyawali and Liguo Wang and Jianlin Cheng},
title = {CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs},
year = {2024},
doi = {10.1093/bioinformatics/btae109},
publisher = {Oxford University Press},
journal = {Bioinformatics}
URL = { https://doi.org/10.1093/bioinformatics/btae109 } }

CryoPPP Dataset

@article {Dhakal2023,
author = {Dhakal, Ashwin and Gyawali, Rajan and Wang, Liguo and Cheng, Jianlin},
title = {A large expert-curated cryo-EM image dataset for machine learning protein particle picking},
year = {2023},
volume = {10},
issue = {1},
doi = {10.1038/s41597-023-02280-2},
journal = {Scientific Data},
url = { https://doi.org/10.1038/s41597-023-02280-2 } }