Metadata-Version: 2.1
Name: colav
Version: 0.0.1
Summary: Calculate structural representations (dihedral angles, CA pairwise distances, and strain analysis) for downstream analysis (e.g., PCA, t-SNE, or UMAP)
Project-URL: homepage, https://rs-station.github.io/colav/
Project-URL: repository, https://github.com/rs-station/colav
Author-email: Ammaar Saeed <aasaeed@college.harvard.edu>
License: MIT License
License-File: LICENSE
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Typing :: Typed
Requires-Python: >=3.7
Requires-Dist: biopandas
Requires-Dist: jupyter
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
Provides-Extra: docs
Requires-Dist: myst-parser; extra == 'docs'
Requires-Dist: sphinx; extra == 'docs'
Requires-Dist: sphinx-rtd-theme; extra == 'docs'
Provides-Extra: test
Requires-Dist: pytest-cov; extra == 'test'
Requires-Dist: pytest>=6.0; extra == 'test'
Description-Content-Type: text/markdown

# colav

`colav` (Conformational Landscape Visualization) provides tools for representing protein structures and mapping conformational landscapes. 

`colav` supports calculations for dihedral angles, pairwise distances, and strain. It is built on `biopandas`, `NumPy`, and `SciPy`. The methods analyze PDB files, either one-by-one or all together. The three methods currently implemented are: 

## Installation 

You can install `colav` using `pip`: 

```
pip install colav
```

## Examples 

Examples of how to use the software can be found in `scripts/`.

## Documentation 

Documentation can be found [here](rs-station.github.io/colav). 

## Support 

If you are having issues, please let us know. You can contact Ammaar at aasaeed@college.harvard.edu. 

## License 

This code is provided under the [MIT license](LICENSE). 

## Reference 

If you use `colav` in your work, please use the following citation: 
Saeed, A.A., Klureza, M.A., and Hekstra, D.R. (2023). Mapping protein conformational ensembles using crystallographic drug fragment screens. [doi]()