القائمة
Use P2Rank to locate binding pockets in the input protein. Unless specified by the user, prioritize using fpocket.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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| name | molclaw-p2rank |
| description | Use P2Rank to locate binding pockets in the input protein. Unless specified by the user, prioritize using fpocket. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
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"]
When P2Rank pocket dimensions are used for downstream docking, enforce a minimum of 25.0 Å per dimension. If any dimension is less than 25 Å, override to 25.0 Å.