ProLIF MD Interaction Fingerprinting Skill

SkillDev tools

ProLIF MD trajectory analysis skill for protein-ligand interaction fingerprints with frame slicing and residue controls.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the ProLIF MD Interaction Fingerprinting Skill skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/molclaw in skills/L1_tools/molclaw-prolif-md/SKILL.md and read by ahel’s review.

Note:

  • Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution.
  • For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.
  • Please refer to skill molclaw-scp-server to complete tool invocation.

[!NOTE] Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution. For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.

Task Description

Analyze protein-ligand interaction fingerprints in molecular dynamics (MD) trajectories, with support for frame slicing and residue controls. Use this skill to evaluate whether binding patterns remain stable throughout simulation.

Routing note: This tool is the primary choice for MD trajectory interaction dynamics (multi-frame analysis). For single-structure analysis (one frame, one complex), use molclaw-interaction-visualizer instead.

Input Source Mapping

ParameterSource Guidance
topology_pathOutput topology file from MD workflow tools: e.g., protein_openmm_md, prepare_complex, prepare_protein_md, or goca_pipeline (.psf/.pdb/.prmtop)
trajectory_pathOutput trajectory file from the same MD workflow tools (.dcd/.nc/.xtc)
ligand_selectionUser-provided ligand selection string, for example resname LIG or resid 100-101
protein_selectionDefaults to protein; can be customized to narrow protein scope

Usage

Tool: prolif_md

Compute ProLIF fingerprints for an MD trajectory and return standardized summary metrics.
Args:
    topology_path (str): Path to the topology file (e.g., .psf, .pdb, .prmtop).
    trajectory_path (str): Path to the trajectory file to analyze.
    ligand_selection (str): Selection string identifying ligand atoms.
    protein_selection (str): Selection string for protein atoms. Default: 'protein'.
    interactions (List[str]|None): Optional interaction types to compute (e.g., Hydrophobic, HBDonor).
    count (bool): If True, compute interaction counts instead of fingerprints. Default: False.
    vicinity_cutoff (float|None): Optional distance cutoff for vicinity interactions.
    params_json (str|None): Optional JSON parameter file path for ProLIF interaction settings.
    start (int|None): Optional start frame index.
    stop (int|None): Optional stop frame index (exclusive).
    step (int|None): Optional frame stride.
    residues (List[str]|None): Optional explicit residue list to include.
    all_residues (bool): If True, include all residues in analysis. Default: False.
Return:
    status (str): 'success' or 'error'.
    msg (str): Human-readable summary or error message.
    command (str): The executed command label ('md').
    output_dir (str|None): Run-specific directory under tool_result/prolif_result.
    output_file (str|None): Path to the generated CSV file.
    n_frames (int|None): Number of processed frames.
    n_interactions (int|None): Number of interaction columns in output.
    frequent_interactions (List[dict]|None): High-frequency interactions (>30%) with keys 'interaction' and 'frequency'.
    result_summary (dict|None): Full summary dictionary from the wrapper.

How To Use prolif_md

response = await client.session.call_tool(
    "prolif_md",
    arguments={
        "topology_path": "relative/path/to/system.prmtop",
        "trajectory_path": "relative/path/to/md_prod.nc",
        "ligand_selection": "resname LIG",
        "protein_selection": "protein",
        "interactions": ["Hydrophobic", "HBDonor"],
        "start": 0,
        "stop": 100,
        "step": 2
    }
)
result = client.parse_result(response)
key_output = result["output_file"]

Example Parameter Sets

# 1) Main mode
{
    "topology_path": "relative/path/to/system.prmtop",
    "trajectory_path": "relative/path/to/md_prod.nc",
    "ligand_selection": "resname LIG",
    "protein_selection": "protein",
    "interactions": ["Hydrophobic", "HBDonor", "HBAcceptor"],
    "start": 0,
    "stop": 100,
    "step": 2
}

# 2) Variant mode
{
    "topology_path": "relative/path/to/system.prmtop",
    "trajectory_path": "relative/path/to/md_prod.nc",
    "ligand_selection": "resname LIG",
    "count": True,
    "all_residues": True,
    "vicinity_cutoff": 4.5,
    "params_json": "relative/path/to/prolif_params.json"
}

Signals

GitHub stars
33
Forks
3
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
molclaw-prolif-md
Source
github.com/internscience/molclaw