Metadata-Version: 2.1
Name: pege
Version: 0.0.1
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
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 :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Dist: egnn-pytorch (>=0.2.6,<0.3.0)
Requires-Dist: pytorch-lightning (>=1.4.7,<2.0.0)
Requires-Dist: torch (>=1.9.0,<2.0.0)
Project-URL: Repository, https://github.com/bayer-science-for-a-better-life/pege
Description-Content-Type: text/markdown

# Protein Enviroment Graph Embeddings (PEGE)

Protein embeddings to describe local electrostic enviroments

# Installation & Basic Usage

PEG is installable from the Pypi repo:
```bash
python3 -m pip install peg
```

Peg 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()
atoms_emb = protein.get_atoms([<atom_numbers>])
```

# 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:pdreis@fc.ul.pt).


