# Molclaw P2rank

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

- Skill: `internscience/molclaw-p2rank` (Agent Skill)
- Install (CLI): `npx skillmds@latest add internscience/molclaw-p2rank`
- Raw SKILL.md: https://api.skillmd.com/api/skills/internscience/molclaw-p2rank/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT license
- Author: internscience (https://skillmd.com/u/internscience)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/internscience/molclaw-p2rank

---


# Pocket Location

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*.

```tex
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* :

```python
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"]
```