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elaspic2-0.1.7
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مشاهده بیشترتوضیحات
Predicting the effect of mutations on protein folding and protein-protein interaction.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | elaspic2-0.1.7 |
| نام | elaspic2 |
| نسخه کتابخانه | 0.1.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Alexey Strokach |
| ایمیل نویسنده | alex.strokach@utoronto.ca |
| آدرس صفحه اصلی | https://gitlab.com/elaspic/elaspic2 |
| آدرس اینترنتی | https://pypi.org/project/elaspic2/ |
| مجوز | - |
# ELASPIC2
[](https://anaconda.org/ostrokach-forge/elaspic2/)
[](https://elaspic.gitlab.io/elaspic2/v0.1.7/)
[](https://gitlab.com/elaspic/elaspic2/commits/v0.1.7/)
[](https://elaspic.gitlab.io/elaspic2/v0.1.7/htmlcov/)
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` | <a href="https://colab.research.google.com/github/elaspic/elaspic2/blob/v0.1.7/notebooks/10_stability_demo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" width="200px" /></a> | Example notebook showing how to use ELASPIC2 to predict the effect of mutations on _protein stability_. |
| `10_affinity_demo.ipynb` | <a href="https://colab.research.google.com/github/elaspic/elaspic2/blob/v0.1.7/notebooks/10_affinity_demo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" width="200px" /></a> | Example notebook showing how to use ELASPIC2 to predict the effect of mutations on _protein binding affinity_. |
See other notebooks in the [`notebooks/`](https://gitlab.com/elaspic/elaspic2/-/tree/master/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.
```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.
```bash
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.
```bash
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.
```bash
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.
```bash
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`](https://gitlab.com/datapkg/protein-folding-energy) repository to see how these data were generated.
## Acknowledgements
<div align="center">
<img src="docs/_static/acknowledgements.svg" width="45%" />
</div>
## References
- Alexey Strokach, Tian Yu Lu, Philip M. Kim. _ELASPIC2 (EL2): Combining contextualized language models and graph neural networks to predict effects of mutations_.
نیازمندی
| مقدار | نام |
|---|---|
| <2.0,>=1.78 | biopython |
| <0.8,>=0.7 | brotlipy |
| <0.4,>=0.3 | fire |
| <1.10,>=1.9 | mdtraj |
| <1.2,>=1.1 | mmtf-python |
| <2.8,>=2.7 | nglview |
| <2.8,>=2.7 | paramiko |
| <6.3,>=6.2 | tenacity |
| <4.55,>=4.50 | tqdm |
نحوه نصب
نصب پکیج whl elaspic2-0.1.7:
pip install elaspic2-0.1.7.whl
نصب پکیج tar.gz elaspic2-0.1.7:
pip install elaspic2-0.1.7.tar.gz