pocket-extraction 0.1.3

A package for extracting ligands and binding pockets from PDB files

Pocket Extraction

Pocket Extraction is a Python package built on Biopython for extracting ligands and binding pockets from structural biology files (PDB/mmCIF). It supports high-throughput screening as well as detailed structural analyses.

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Key Features ✨

• Binding Pocket Extraction
  Extract pockets around ligands using either:

  • An existing ligand file, or
  • Manually specified coordinates
    (Adjust the search radius as needed.)

• Ligand Extraction
  Retrieve ligands by specifying names (single/multiple) or by automatically processing all non-solvent HETATM residues.

• Flexible I/O Support

  • Input: PDB, mmCIF
  • Output: PDB (default), mmCIF

• Advanced Filtering & Batch Processing
  Filter by model ID, chain ID, or ligand names; process multiple ligands/pockets in one command.

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Installation

Install via pip:

pip install pocket_extraction

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Usage Examples

The package provides both CLI and Python interfaces. Below are the consolidated examples that cover the most common use cases.

1. Extracting Binding Pockets

CLI:

# Using an existing ligand file:
extract_pocket input.pdb -o pocket.cif --ligand_file ligand.pdb --radius 12.5

# Using manual coordinates (specify ligand center):
extract_pocket input.cif -o pocket.pdb --ligand_center 10.0 20.0 30.0 --radius 10.0

Python:

from pocket_extraction import extract_pocket, get_ligand_coords

# Option A: Using an existing ligand file
ligand_coords = get_ligand_coords("ligand.pdb")
extract_pocket("input.pdb", "pocket.pdb", ligand_coords=ligand_coords, radius=12.5)

# Option B: Using manually provided coordinates
extract_pocket("input.cif", "pocket.cif", ligand_center=[10.0, 20.0, 30.0], radius=10.0)

Note: Choose the option that matches your data source. The only difference is in providing either a ligand file (or coordinates obtained from it) or manual coordinates.

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2. Extracting Ligands

CLI:

# For a specific ligand or multiple ligands:
extract_ligand input.pdb -o output_path --ligands NAD         # Single ligand
extract_ligand input.cif -o output_dir --ligands ATP NAD --multi  # Multiple ligands, each saved separately

Python:

from pocket_extraction import extract_ligand

# Example for a specific ligand with optional filtering parameters:
extract_ligand("input.pdb", "nad.pdb", ligand_names=["NAD"], model_id=0, chain_id="A")

# Example for multiple ligands:
extract_ligand("input.cif", "output_dir/", ligand_names=["ATP", "NAD"], multi_mode=True)

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3. Combined Extraction of Ligands and Pockets

Use the combined function for simultaneous extraction. Adjust parameters based on your workflow:

CLI:

# Example 1: Merged multi-residue ligand with a unified pocket
extract_ligand_and_pocket input.pdb -l ligand.pdb -p pocket.pdb --ligand_names HIS ARG --model_id 0 --chain_id E --radius 12.0

# Example 2: Separate files for each ligand and pocket:
extract_ligand_and_pocket input.pdb -l ligands/ -p pockets/ --ligands ATP NAD --multi --radius 10.0

# Example 3: Automatic extraction of all non-solvent ligands and pockets:
extract_ligand_and_pocket input.pdb -l auto_ligands/ -p auto_pockets/ --multi --radius 10.0

Python:

from pocket_extraction import extract_ligand_and_pocket

# Option: Customize parameters for your workflow.
extract_ligand_and_pocket(
    pdb_file="input.pdb",      # or pdb_path for automatic extraction
    ligand_path="ligand.pdb",  # or a directory (e.g., "ligands/")
    pocket_path="pocket.pdb",  # or a directory (e.g., "pockets/")
    ligand_names=["ATP", "NAD"],   # omit or adjust as needed
    model_id=0,                # optional filtering
    chain_id="E",              # optional filtering
    multi_mode=True,           # set to True for separate files
    radius=12.0                # adjust the search radius
)

Note: The Python interface uses similar parameters to the CLI. The function adapts based on whether single files or directories are provided, and whether multi_mode is enabled.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Author

Hanker Wu
📧 GitHub: HankerWu
💬 For bug reports or feature requests, please open a GitHub issue.