Metadata-Version: 2.1
Name: pege
Version: 1.2.1
Summary: Protein embeddings to describe local electrostic enviroments
Home-page: https://github.com/bayer-science-for-a-better-life/pege
License: MIT
Author: Pedro Reis
Author-email: pdreis@fc.ul.pt
Requires-Python: >=3.6.2,<4.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Dist: pandas
Requires-Dist: pdbmender (>=0.5.4,<0.6.0)
Requires-Dist: torch (>=1.9.0)
Project-URL: Repository, https://github.com/bayer-science-for-a-better-life/pege
Description-Content-Type: text/markdown

# Protein Environment Graph Embeddings (PEGE)

Protein embeddings to describe local electrostic environments

# Installation & Basic Usage

PEGE is installable from the Pypi repo:
```bash
python3 -m pip install pege
```

[Pytorch Geometry](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) also needs to be installed

In order for the structure preprocessing to work python2 and gawk need to installed.
```bash
apt install python2 gawk
```

Pege can be used to obtain protein embeddings as well as descriptors for specific `atom_numbers` from a `pdb` file:
```python
from pege import Pege

protein = Pege(<pdb>)
protein_emb = protein.get_protein()
all_res_embs = protein.get_all_res_embs(chain="A")
```

# Documentation
TBA

# License
This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

# Contacts
Please submit a github issue to report bugs and to request new features. Alternatively, you may email the developer [directly](mailto:pedro.reis@bayer.com).


