Pocket Location

SkillDev tools

Use P2Rank to locate binding pockets in the input protein. Unless specified by the user, prioritize using fpocket.

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 Pocket Location skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/molclaw in skills/L1_tools/molclaw-p2rank/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.

The description of tool pred_pocket_prank.

Use P2Rank to predict ligand binding pockets in the input protein.
Args:
    pdb_file_path (str): Path to the protein structure file (PDB format)
Return:
    status (str): success/error
    msg (str): message
    pred_pockets (List[dict]): List of dict, each containing pocket confidence and center position information. The first pocket (pred_pockets[0]) has the highest score and is usually used for molecular docking.
        --site_id (str): Pocket id
        --probability (float): Predicted confidence score (0~1) of the pocket
        --center_x (float): Center X of the pocket
        --center_y (float): Center Y of the pocket
        --center_z (float): Center Z of the pocket

How to use tool pred_pocket_prank :

response = await client.session.call_tool(
    "pred_pocket_prank",
    arguments={
        "pdb_file_path": pdb_file_path
    }
)
result = client.parse_result(response)
pred_pockets = result["pred_pockets"]

Signals

GitHub stars
33
Forks
3
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
molclaw-p2rank
Source
github.com/internscience/molclaw