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InteractionProfiler API#

docksuitex.interaction_profiler.InteractionProfiler #

Protein–ligand interaction fingerprinting using ProLIF.

This module wraps ProLIF to analyse the non-covalent interactions between a protein and docked ligand poses produced by AutoDock Vina or AutoDock4.

The analysis workflow
  1. Converting the multi-model PDBQT output → SDF using Open Babel.
  2. Loading the protein with MDAnalysis / ProLIF.
  3. Loading all ligand poses from the SDF via plf.sdf_supplier().
  4. Computing interaction fingerprints with ProLIF.
  5. Saving the resulting DataFrame, 2D/3D HTML, and barcode PNG.
Supported Input
  • Protein: PDB (.pdb) — typically the _prepared.pdb from Protein.prepare()
  • Ligand poses: multi-model PDBQT (.pdbqt) from Vina / AD4
Note

The intermediate SDF file is saved in prolif_intermediates/ inside the output directory.

__init__ #

__init__(
    protein_pdb: Union[str, Path],
    vina_output_pdbqt: Union[str, Path],
    _cpu: int = (os.cpu_count() or 2) - 1,
)

Initialize an InteractionProfiler with input paths and settings.

Parameters:

Name Type Description Default
protein_pdb str | Path

Path to the protein PDB file. This should be the original or prepared PDB (not PDBQT).

 required
vina_output_pdbqt str | Path

Path to the multi-model output.pdbqt produced by AutoDock Vina or AutoDock4.

 required
_cpu int

Number of CPUs for ProLIF fingerprint calculation. Defaults to os.cpu_count() - 1.

 (cpu_count() or 2) - 1

Raises:

Type Description
FileNotFoundError

If either input file does not exist.
ValueError

If file extensions are incorrect.

run #

run(save_to: Union[str, Path, None] = None) -> pd.DataFrame

Run the ProLIF interaction fingerprint analysis.

Performs the full workflow: convert PDBQT → SDF → load structures → compute fingerprints → save CSV, 2D/3D HTML, and barcode.

Folder structure created inside save_to::

save_to/
├── prolif_intermediates/
│   ├── <protein>.pdb                     # copy of input protein
│   ├── <vina_output>.pdbqt               # copy of input poses
│   └── ligands.sdf                       # converted ligand poses
├── prolif_interactions.csv
├── prolif_barcode.png
├── prolif_pose_1_interactions_2D.html
├── prolif_pose_1_interactions_3D.html
└── ...

Parameters:

Name Type Description Default
save_to str | Path

Directory where outputs will be saved. Defaults to prolif_results.

 None

Returns:

Type Description
DataFrame

pandas.DataFrame: Interaction fingerprint table with one row
DataFrame

per ligand pose and columns for each residue–interaction pair.

Raises:

Type Description
RuntimeError

If Open Babel conversion or ProLIF analysis fails.

save_2d_view #

save_2d_view(
    save_path: Union[str, Path], pose_idx: int = 1
) -> None

Save a 2D network diagram of the protein-ligand interactions.

Parameters:

Name Type Description Default
save_path Union[str, Path]

Path to save the 2D HTML file.

 required
pose_idx int

Pose index (1-based). Defaults to 1.

 1

save_3d_view #

save_3d_view(
    save_path: Union[str, Path], pose_idx: int = 1
) -> None

Save an interactive 3D view of the protein-ligand complex interactions.

Parameters:

Name Type Description Default
save_path Union[str, Path]

Path to save the 3D HTML file.

 required
pose_idx int

Pose index (1-based). Defaults to 1.

 1

view_interactions #

view_interactions()

Render an interactive viewer with Prev/Next buttons and a 2D/3D/Barcode toggle.

This method is designed for use in Jupyter Notebooks. It displays a widget with controls to switch between different ligand poses and different visualization modes (2D network, 3D complex, or the interaction barcode).