Metadata-Version: 2.4
Name: crimm
Version: 2026.1.2
Summary: Chemistry with the ReInvented Macromolecular Mechanics
Author-email: Truman Xu <ziqiaoxu@umich.edu>, Stanislav Cherepanov <stanislc@umich.edu>
License: GPL-3.0-or-later
Project-URL: Homepage, https://github.com/BrooksResearchGroup-UM/crimm
Project-URL: Bug Tracker, https://github.com/BrooksResearchGroup-UM/crimm/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 2 - Pre-Alpha
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: biopython>=1.80
Requires-Dist: ipywidgets>=8
Requires-Dist: nglview>=3.1
Requires-Dist: scipy>=1.10
Requires-Dist: requests>=2.28
Requires-Dist: pandas>=1.5
Provides-Extra: protonation
Requires-Dist: propka>=3.5.1; extra == "protonation"
Provides-Extra: cheminformatics
Requires-Dist: rdkit; extra == "cheminformatics"
Provides-Extra: all
Requires-Dist: propka>=3.5.1; extra == "all"
Requires-Dist: rdkit; extra == "all"
Dynamic: license-file

# crimm

**crimm** stands for **Chemistry with the ReInvented Macromolecular Mechanics**.

This is a Python toolkit for biomolecule structure preparation, designed to unify common modeling routines under one scriptable platform. While many tools exist for tasks like solvation, adding hydrogens, or building missing loops, they often lack the scriptability needed for high-throughput pipelines. crimm aims to fill this gap by providing intuitive Python APIs that integrate seamlessly with pyCHARMM, RDKit, and other computational chemistry tools.

## Why crimm?

- **Scriptable**: All preparation steps are Python functions—no clicking through web interfaces or writing shell scripts
- **Integrated**: Works natively with pyCHARMM for energy calculations and simulations
- **Accurate**: Uses mmCIF format to correctly identify chain types, detect missing loops, and handle biological assemblies
- **Extensible**: Adaptors connect crimm to RDKit, PropKa, OpenMM, and more

## Features

- Fetch structures from RCSB PDB or AlphaFold Database
- Generate topology using **CHARMM36m** force field (proteins, nucleic acids, lipids, carbohydrates)
- Parameterize small molecule ligands with **CGenFF** integration
- Solvate in cubic or truncated octahedral water boxes
- Add ions at target concentrations (SPLIT, SLTCAP methods)
- Build missing loops from homology models
- Read/write native CHARMM PSF and CRD files
- Visualize structures in Jupyter notebooks with NGLView

## Installation

```bash
pip install crimm
```

Requires Python >= 3.8. For a complete environment:

```bash
conda env create -f env.yaml
```

> **Note**: pyCHARMM and OpenMM must be installed separately if needed.

## Quick Example

```python
from crimm.Fetchers import fetch_rcsb
from crimm.Modeller import TopologyGenerator
from crimm.Modeller.Solvator import Solvator
from crimm.IO import write_psf, write_crd

# Fetch and prepare structure
model = fetch_rcsb('1LSA', organize=True)
topo_gen = TopologyGenerator()
for chain in model.protein:
    topo_gen.generate(chain)

# Solvate and add 150 mM KCl
solvator = Solvator(model)
solvator.solvate(cutoff=10.0, box_type='octa')
solvator.add_ions(concentration=0.15, cation='POT', anion='CLA')

# Write CHARMM files
write_psf(model, 'system.psf')
write_crd(model, 'system.crd')
```

## Modules

| Module | Purpose |
|--------|---------|
| `Fetchers` | Download structures from RCSB PDB or AlphaFold |
| `Modeller` | Topology generation, solvation, loop building |
| `IO` | Read/write PDB, mmCIF, PSF, CRD files |
| `Adaptors` | Connect to pyCHARMM, RDKit, PropKa |

## Documentation

See `tutorials/` for Jupyter notebooks on structure preparation, topology generation, loop building, and more.

## License

GPLv3

## Links

- Repository: https://github.com/BrooksResearchGroup-UM/crimm
- Issues: https://github.com/BrooksResearchGroup-UM/crimm/issues
