GPSite
# Model Introduction GPSite is a geometry-aware multitask network for protein binding-site prediction. It simultaneously predicts potential binding sites between protein residues and DNA, RNA, peptides, proteins, ATP, heme (HEM), and various metal ions. The method uses sequence representations generated by a pretrained protein language model and predicted structures to perform binding-site prediction, without relying on MSA or experimentally resolved protein structures. Paper: > **Genome-scale annotation of protein binding sites via language model and geometric deep learning** > https://doi.org/10.7554/eLife.93695 # Model Description GPSite takes a protein FASTA sequence as input. The complete inference workflow first uses ESMFold to predict the protein structure and ProtT5-XL-UniRef50 to extract sequence representations. It then combines the predicted structure with DSSP features to construct residue-level geometric representations, and finally uses the GPSite graph neural network to output residue-level prediction scores for multiple binding-site types simultaneously. # Use Cases | Use case | Description | | --- | --- | | Protein binding-site prediction | Predict residue-level binding-site scores from a protein sequence | | Multi-ligand binding analysis | Simultaneously predict binding sites for DNA, RNA, peptides, proteins, ATP, HEM, and various metal ions | | Prediction without an experimental structure | Use ESMFold to predict the structure without providing an experimentally resolved structure in advance | | Batch protein sequence analysis | Perform structure prediction, feature extraction, and site prediction on multiple protein sequences in a FASTA file | # Usage ## 1. Using OneCode Experience intelligent one-click AI4S programming in the OneCode online environment: [Try intelligent one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) ## 2. Manual Installation and Usage **Hardware Requirements** - A GPU/DCU is recommended for running GPSite because ESMFold structure prediction in the complete workflow requires substantial computation and GPU memory. - GPSite supports CPU execution, but the structure prediction stage is significantly slower without a GPU/DCU. ### Set Up the Runtime Environment #### DCU Environment ```bash # Activate DTK and CONDA first conda create -n onescience311 python=3.11 -y conda activate onescience311 # Install with uv support pip install onescience[bio] \ -i http://mirrors.onescience.ai:3141/pypi/simple/ \ --trusted-host mirrors.onescience.ai ``` #### Environment Notes - If you encounter missing dependencies or version incompatibilities during execution, refer to the dependency versions specified in `requirements.txt` and install or adjust the relevant dependencies as needed. - The following versions are recommended for ProtTrans-related dependencies: ```bash python -m pip install \ "transformers==4.30.1" \ "tokenizers==0.13.3" \ "sentencepiece==0.1.99" ``` - ESMFold-related dependencies can be installed as follows: ```bash python -m pip install "fair-esm[esmfold]" python -m pip install modelcif==0.7 ``` - GPSite also requires `dllogger`. If online installation fails, download the `dllogger` source code and install it locally from the source directory: ```bash python -m pip install /path/to/dllogger-master ``` ### Prepare Weights and Models Complete GPSite inference depends not only on the GPSite weights in this repository, but also on ProtT5, ESMFold, and ESM-2 weights. Complete the following preparations before first use. #### 1) GPSite Model Weights The `model/` directory in the GPSite repository contains five trained weights: ```text model/ ├── fold0.ckpt ├── fold1.ckpt ├── fold2.ckpt ├── fold3.ckpt └── fold4.ckpt ``` During inference, the five models above are loaded in sequence and their prediction results are averaged. The complete model package downloaded from Hugging Face should already include these weights required for GPSite inference, so separate downloads are normally unnecessary. #### 2) ProtT5-XL-UniRef50 GPSite uses ProtT5-XL-UniRef50 to extract protein sequence representations. The model can be downloaded from: ```text https://zenodo.org/record/4644188 ``` After downloading the model, make sure that `PROTTRANS_DIR` in `scripts/run_infer.sh` points to the actual model directory. `scripts/predict.py` reads this path through the `PROTTRANS_PATH` environment variable. #### 3) ESMFold and ESM-2 The official version of GPSite automatically downloads ESMFold and ESM-2 on the first run. If the runtime environment has limited network access or is offline, prepare the local weights in advance. The recommended location is: ```text weight/checkpoints/ ├── esmfold_3B_v1.pt ├── esm2_t36_3B_UR50D.pt └── esm2_t36_3B_UR50D-contact-regression.pt ``` Also make sure that `ESMFOLD_HUB_DIR` in `scripts/run_infer.sh` points to the `weight` directory within the project. ESMFold v1 also loads ESM-2 internally, so at least the following files are required: ```text esmfold_3B_v1.pt esm2_t36_3B_UR50D.pt ``` It is also recommended to keep: ```text esm2_t36_3B_UR50D-contact-regression.pt ``` #### 4) DSSP GPSite calls the following during structural feature extraction: ```text scripts/feature_extraction/mkdssp ``` Before first use, make sure that this file has execute permission: ```bash chmod +x scripts/feature_extraction/mkdssp ``` ## 3. Quick Start ### Download the Model Package ```bash hf download OneScience-Group/GPSite --local-dir ./GPSite cd GPSite ``` - Complete GPSite inference additionally depends on **ProtT5-XL-UniRef50, ESMFold, ESM-2, and OpenFold**. Follow "Prepare Weights and Models" to install the relevant models, weights, and OpenFold first. - GPSite additionally depends on OpenFold. OpenFold must be installed in the current Python/Conda environment; its source code does not need to remain in the project directory. To install from source, download and extract the OpenFold source code, then run the following in the source directory: ```bash python3 setup.py install ``` # Example Data The official GPSite example input is located at: ```text conf/example/demo.fa ``` The input uses the standard FASTA format: ```text >protein_id MSEQUENCE... ``` GPSite automatically creates an output subdirectory based on the FASTA filename. For example, if the input is `conf/example/demo.fa`, the output will be located at: ```text