If you have any questions regarding the code/data, please contact Yufan Liu via [email protected].

CLAPE framework

This repo holds the code of CLAPE (Contrastive Learning And Pre-trained Encoder) framework for protein-ligands binding sites prediction. We provide 3 ligand-binding tasks including protein-DNA, protein-RNA, and antibody-antigen binding sites prediction, an we will also provide small molecules binding sites weight in the future (check CLAPE-SMB for reference).

Usage

CLAPE is primarily dependent on a large-scale pre-trained protein language model ProtBert implemented using HuggingFace's Transformers and PyTorch. Please install the dependencies in advance, or create a conda/mamba envrionment using provided environment file. If you are using CLAPE-SMB, please install ESM.

wget https://github.com/YAndrewL/CLAPE/blob/main/environment.yaml
conda env create -f environment.yaml
conda activate clape 

1. Python package from pypi

We provide a python package for predicting ligand-binding sites of given protein sequences in FASTA format. Here we provide a sample file, and please use CLAPE as following steps, taking DNA-binding sites prediction as an example:

# download model weights and example file
wget https://github.com/YAndrewL/CLAPE/blob/main/example.fa
wget https://github.com/YAndrewL/CLAPE/blob/main/weights/DNA.pth
pip install clape  # install clape from pypi

# package usage example
from clape import Clape

model = Clape(model_path="model_path", ligand="DNA")
results = model.predict(input_file="example.fa")

You can set keep_score to True to keep the predicted score from model, and use switch_ligand to change to another binding site prediction task.

2. Command line tools

We also provide a command line tool, which will be installed along the python package, you may use as below:

clape --input example.fa --output out.txt --ligand DNA --model /path/to/downloaded/model

This command will first load the pre-trained models, users can specify the downloading directory using the --cache parameter.

Some parameters are described as follows:

Parameters	Descriptions
--help	Show the help doc.
--ligand	Specify the ligand for prediction, DNA, RNA, and AB (antibody) are supported now.
--threshold	Specify the threshold for identifying the binding site, the value needs to be between 0 and 1, default: 0.5.
--input	The path of the input file in FASTA format.
--output	The path of the output file, the first and the second line are the same as the input file, and the third line is the prediction result.
--cache	The path for saving the pre-trained parameters, default: protbert.
--model	The path for trained backbone models.

Citation

If you find our work helpful, please kindly cite the BibTex as following:

@article{10.1093/bib/bbad488,
    author = {Liu, Yufan and Tian, Boxue},
    title = "{Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning}",
    journal = {Briefings in Bioinformatics},
    volume = {25},
    number = {1},
    pages = {bbad488},
    year = {2024},
    month = {01},
    issn = {1477-4054},
    doi = {10.1093/bib/bbad488},
    url = {https://doi.org/10.1093/bib/bbad488},
    eprint = {https://academic.oup.com/bib/article-pdf/25/1/bbad488/55381199/bbad488.pdf},
}

If you are using CLAPE-SMB, please also kindly cite:

@article{10.1186/s13321-024-00920-2,
    author={Wang, Jue
    and Liu, Yufan
    and Tian, Boxue},
    title={Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning},
    journal={Journal of Cheminformatics},
    year={2024},
    month={Nov},
    day={06},
    volume={16},
    number={1},
    pages={125},
    issn={1758-2946},
    doi={10.1186/s13321-024-00920-2},
    url={https://doi.org/10.1186/s13321-024-00920-2}
}

Update

• [Nov. 2024] CLAPE-SMB is publised in Journal of Cheminformatics, please check the online version.

• [Aug. 2024] CLAPE can be used as a python package now, please check clape in pypi.

• [Mar. 2024] The training code is released with CLAPE-SMB, please check this repo for reference.

• [Jan. 2024] Our paper is publised in Briefings in Bioinformatics, please check the online version.