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.
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.
We provide the Python script for predicting ligand-binding sites of given protein sequences in FASTA format. Here we provide a sample file, and please use CLAPE as following commands:
python clape.py --input example.fa --output out.txt
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, default: DNA. |
--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. |
Reference:
Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning by Yufan Liu and Boxue Tian. Published in Briefings in Bioinformatics.
Please contact [email protected] for questions.