elaspic2 0.1.7

Predicting the effect of mutations on protein folding and protein-protein interaction.

ELASPIC2

Predicting the effect of mutations on protein folding and protein-protein interaction.

Usage

Web server

ELASPIC2 has been integrated into the original ELASPIC web server, available at: http://elaspic.kimlab.org.

Python API

The following notebooks can be used to explore the basic functionality of ELASPIC2.

Notebook name	Google Colab	Description
10_stability_demo.ipynb		Example notebook showing how to use ELASPIC2 to predict the effect of mutations on protein stability.
10_affinity_demo.ipynb		Example notebook showing how to use ELASPIC2 to predict the effect of mutations on protein binding affinity.

See other notebooks in the notebooks/ directory for more detailed information about how ELASPIC2 models are trained and validated.

REST API

ELASPIC2 is accessible through a REST API, documented at: https://elaspic2-api.proteinsolver.org/docs.

The following code snippet shows how the REST API can be used from within Python.

import json
import time
import requests

ELASPIC2_JOBS_API = "https://elaspic2-api.proteinsolver.org/jobs/"

mutation_info = {
    "protein_structure_url": "https://files.rcsb.org/download/1MFG.pdb",
    "protein_sequence": (
        "GSMEIRVRVEKDPELGFSISGGVGGRGNPFRPDDDGIFVTRVQPEGPASKLLQPGDKIIQANGYSFINI"
        "EHGQAVSLLKTFQNTVELIIVREVSS"
    ),
    "mutations": "G1A,G1C",
    "ligand_sequence": "EYLGLDVPV",
}

# Submit a job
job_request = requests.post(ELASPIC2_JOBS_API, json=mutation_info).json()
while True:
    # Wait for the job to finish
    time.sleep(10)
    job_status = requests.get(job_request["web_url"]).json()
    if job_status["status"] in ["error", "success"]:
        break
# Collect results
job_result = requests.get(job_status["web_url"]).json()
# Delete job (optional)
requests.delete(job_request["web_url"]).raise_for_status()
# Show results
print(job_result)

Command-line interface (CLI)

Finally, ELASPIC2 can be used through a command-line interface.

python -m elaspic2 \
  --protein-structure tests/structures/1MFG.pdb \
  --protein-sequence GSMEIRVRVEKDPELGFSISGGVGGRGNPFRPDDDGIFVTRVQPEGPASKLLQPGDKIIQANGYSFINIEHGQAVSLLKTFQNTVELIIVREVSS \
  --ligand-sequence EYLGLDVPV \
  --mutations G1A.G1C

Installation

Docker

Docker images that contain ELASPIC2 and all dependencies are available at: https://gitlab.com/elaspic/elaspic2/container_registry.

Conda-pack

Conda-pack tarballs containing ELASPIC2 and all dependencies are available at: http://conda-envs.proteinsolver.org/elaspic2/.

Simply download and extract the tarball into a desired directory and run conda-unpack to unpack.

wget http://conda-envs.proteinsolver.org/elaspic2/elaspic2-latest.tar.gz
mkdir ~/elaspic2
tar -xzf elaspic2-latest.tar.gz -C ~/elaspic2
source ~/elaspic2/bin/activate
conda-unpack

Conda

ELASPIC2 can be installed using conda. However, the torch-geometric dependencies have to be installed separately.

Replace cudatoolkit=10.1 and cu101 with the desired CUDA version.

conda create -n elaspic2 -c pytorch -c ostrokach-forge -c conda-forge -c defaults elaspic2 "cudatoolkit=10.1"
conda activate elaspic2
pip install "torch-scatter==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-sparse==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-cluster==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-spline-conv==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-geometric==1.6.1"

Python package index (PyPI)

ELASPIC2 can be installed using pip. However, the torch and torch-geometric dependencies have to be installed from external channels.

Replace cu101 with the desired CUDA version.

pip install elaspic2
pip install "torch==1.7.0+cu101" -f https://download.pytorch.org/whl/torch_stable.html
pip install "torchvision==0.8.1+cu101" -f https://download.pytorch.org/whl/torch_stable.html
pip install "torch-scatter==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-sparse==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-cluster==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-spline-conv==latest+cu101" -f https://pytorch-geometric.com/whl/torch-1.7.0.html
pip install "torch-geometric==1.6.1"

Data

Data used to train and validate the ELASPIC2 models are available at http://elaspic2.data.proteinsolver.org and http://protein-folding-energy.data.proteinsolver.org.

See the protein-folding-energy repository to see how these data were generated.

Acknowledgements

References

• Alexey Strokach, Tian Yu Lu, Philip M. Kim. ELASPIC2 (EL2): Combining contextualized language models and graph neural networks to predict effects of mutations.