Skip to main content

A library for protein domain segmentation using Merizo and Chainsaw.

Project description

Protein Domain Segmentation Library

This library provides a convenient interface for protein domain segmentation using the powerful tools Merizo and Chainsaw. The library abstracts the complexity of using these tools, allowing users to predict protein domain boundaries directly from PDB files or MDAnalysis universes.

Features

  • Unified Interface: Use a consistent API to access both Merizo and Chainsaw functionalities.
  • Flexible Input Support: Predict from PDB files or MDAnalysis universes.
  • Lightweight Usage: Avoid integrating full tool codebases directly into your project.

Installation

Install the library and its dependencies using pip:

pip install protein-domain-segmentation

Usage

Below is an example demonstrating how to use the MerizoCluster and ChainsawCluster classes:

from protein_domain_segmentation import MerizoCluster, ChainsawCluster
import MDAnalysis as mda

# Initialize clusters
merizo_cluster = MerizoCluster()
chainsaw_cluster = ChainsawCluster()

# Example PDB file path
pdb_path = "example.pdb"

# Predict chopping using Merizo
merizo_result = merizo_cluster.predict_from_pdb(pdb_path)
print("Merizo Results:", merizo_result)

# Predict chopping using Chainsaw
chainsaw_result = chainsaw_cluster.predict_from_pdb(pdb_path)
print("Chainsaw Results:", chainsaw_result)

# Predict chopping from an MDAnalysis Universe
u = mda.Universe(pdb_path)
merizo_from_universe = merizo_cluster.predict_from_universe(u)
print("Merizo Results from Universe:", merizo_from_universe)

API Overview

Classes

  • Cluster: Abstract base class for clustering tools.

    • predict_from_pdb(pdb_path: str, model_params: Dict = None): Predicts the chopping of a protein from a PDB file.
    • predict_from_universe(universe: mda.Universe, model_params: Dict = None): Predicts the chopping of a protein from an MDAnalysis Universe.
  • MerizoCluster: Implementation of Cluster for Merizo.

  • ChainsawCluster: Implementation of Cluster for Chainsaw.

Methods

  • MerizoCluster.predict_from_pdb(pdb_path: str, model_params: Dict = None): Uses Merizo to predict chopping from a PDB file.

  • ChainsawCluster.predict_from_pdb(pdb_path: str, model_params: Dict = None): Uses Chainsaw to predict chopping from a PDB file.

Credits

This package wraps the functionality of the following tools:

Citation

If you use this package in your research, please cite the respective papers for Merizo and Chainsaw:

  • Merizo: Lau et al., 2023. Merizo: a rapid and accurate protein domain segmentation method using invariant point attention. Nature Communications.
  • Chainsaw: Wells et al., Chainsaw: protein domain segmentation with fully convolutional neural networks. Bioinformatics.

License

This package is distributed under the MIT License. See the LICENSE file for details.

Acknowledgments

We extend our gratitude to the developers of Merizo and Chainsaw for their invaluable contributions to the field of protein domain segmentation.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

protein_domain_segmentation-0.1.0.tar.gz (52.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

protein_domain_segmentation-0.1.0-py3-none-any.whl (59.9 kB view details)

Uploaded Python 3

File details

Details for the file protein_domain_segmentation-0.1.0.tar.gz.

File metadata

File hashes

Hashes for protein_domain_segmentation-0.1.0.tar.gz
Algorithm Hash digest
SHA256 92c2d0b0adfbdacf32ec4c1541404405d63d8b13626b541cbc62b481c6947e48
MD5 3bdd7437386dd88341db36d5ed2d6685
BLAKE2b-256 8a9db4572a99dd8c98a14ea4dc8abf8a7440260bf840666728d11ba1a900ebdc

See more details on using hashes here.

File details

Details for the file protein_domain_segmentation-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for protein_domain_segmentation-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 07e6fbeec611ea5e2b482ac5ece49226a46e905449c7a5986a66773477afbc3e
MD5 d77bcc01079f12915e2498f1997e93cd
BLAKE2b-256 586b396cc7effb034e99f9981325bc0b3b5a0a8c4dcd89225a1fc3f4d356a29a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page