Metadata-Version: 2.1
Name: PoseButcher
Version: 0.0.3
Summary: Package for parsing, writing, and modifying molecular structure files
Project-URL: Homepage, https://github.com/mwinokan/PoseButcher
Project-URL: Bug Tracker, https://github.com/mwinokan/PoseButcher/issues
Author-email: Max Winokan <max@winokan.com>
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Requires-Dist: ase
Requires-Dist: ipython
Requires-Dist: jupyterlab
Requires-Dist: molparse
Requires-Dist: mpytools
Requires-Dist: numpy
Requires-Dist: open3d
Requires-Dist: pandas
Requires-Dist: py3dmol
Requires-Dist: rdkit
Description-Content-Type: text/markdown


# PoseButcher

"A good butcher always trims the fat"

PoseButcher is a tool for categorising and segmenting virtual hits with reference to experimental protein structures and (fragment) hits.

Ligand atoms are tagged with categories:

	- GOOD:

		* fragment space: within the fragment bolus
		* pocket X: in a specified catalytic/allosteric pocket X

	- BAD:
		
		* protein clash: Clashing with the protein
		* solvent space: Heading out of the protein/crystal

## Usage at a glance

	1. Create the butcher (see PoseButcher.__init__):

		from posebutcher import PoseButcher
		butcher = PoseButcher(protein, hits, pockets)

	2. Chop up a posed virtual hit (rdkit.ROMol with a conformer):

		result = butcher.chop(mol)

	3. Tag a compound based on its pocket occupancy and clashes:

		tags = butcher.tag(mol)

	4. (Coming soon) Trim a parts of a compound that clash with a protein or leave the crystal

		mol = butcher.trim(mol)

	5. (Coming soon) Explore the expansion opportunities from a given atom in a virtual hit

		result = butcher.explore(mol, index, direction)

	6. (Coming soon) Score how well a virtual hit recapitulates shape and colour of the fragment bolus

		score: float = butcher.score(mol)

## Installation

1. Install PoseButcher:

`pip install posebutcher`

## Examples

PoseButcher ships with some open access test data from the XChem group at Diamond Light Source, funded by the ASAP consortium.

To run the "example.ipynb":

```
git clone git@github.com:mwinokan/PoseButcher
cd PoseButcher
jupyter lab
```
