ProLoc-IHS

ProLoc-IHS is an advanced deep learning model designed to accurately predict the subcellular localization (SCL) of proteins using immunohistochemistry (IHC) images and protein sequences. By combining image features from the Human Protein Atlas and sequence features from UniProt, ProLoc-IHS uses a cross-attention mechanism to capture the complex relationship between protein images and sequence information. It can provide highly accurate multi-label predictions for protein subcellular localization and significantly outperform existing methods.

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How to use

This section describes how to use the trained ProLoc-IHS model to make subcellular localization predictions using a CSV file of sequences and a folder of IHC images.

Create a Virtual Environment

To run the code, we need to create a virtual environment using Anaconda, and install the required dependencies. The command is as follows：

git clone https://github.com/xinshuaiiii/ProLoc-IHS.git
conda create -n ProLoc-IHS pyhton=3.7.13
conda activate ProLoc-IHS
pip install -r requirements.txt

Download pretrained model

We use pre-trained Prott5, so you need to download the model and put it in the same directory as train.py.

Prott5: https://github.com/agemagician/ProtTrans

model:ProtT5-XL-UniRef50 (also ProtT5-XL-U50)

Prepare your data

Proteins IHC images and sequences are necessary to perform ProLoc-IHS. IHC images should be of .jpg format, and sequences should be of .csv format. You can refer to the format in dataset/test.csv as a sequence example.

Attention: your IHC images and sequences should be in same order, or your will get wrong results.

Predict

python test_directly.py \
  --seq_csv sequence.csv \
  --img_folder ihcFolder \
  --pred_output predictions.csv

This will generate the prediction results in predictions.csv.

Citation

If you use this work in your research, please cite the following paper.

@article{liu2025proloc,
  title={ProLoc-IHS: Multi-label protein subcellular localization based on immunohistochemical images and sequence information},
  author={Liu, Fu and Xin, Shuai and Liu, Yun},
  journal={International Journal of Biological Macromolecules},
  pages={144096},
  year={2025}
}

If you have any questions, please contact Yun Liu([email protected]) or Shuai Xin([email protected])