binding-site-characterization

name	binding_site_characterization
description	Binding Site Characterization - Characterize binding sites: predict pockets with fpocket and P2Rank, get binding site info from ChEMBL, and visualize. Use this skill for structural biology tasks involving run fpocket pred pocket prank get binding site by id visualize protein. Combines 4 tools from 3 SCP server(s).

Binding Site Characterization

Discipline: Structural Biology | Tools Used: 4 | Servers: 3

Description

Characterize binding sites: predict pockets with fpocket and P2Rank, get binding site info from ChEMBL, and visualize.

Tools Used

• run_fpocket from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• pred_pocket_prank from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• get_binding_site_by_id from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
• visualize_protein from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool

Workflow

1. Predict pockets with fpocket
2. Predict pockets with P2Rank
3. Get ChEMBL binding site data
4. Visualize protein structure

Test Case

Input

{
    "pdb_code": "1AKE"
}

Expected Steps

1. Predict pockets with fpocket
2. Predict pockets with P2Rank
3. Get ChEMBL binding site data
4. Visualize protein structure

Usage Example

Note: Replace <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.

import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client

SERVERS = {
    "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model",
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
    "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool"
}

async def connect(url, transport_type):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
    read, write, _ = await transport.__aenter__()
    ctx = ClientSession(read, write)
    session = await ctx.__aenter__()
    await session.initialize()
    return session, ctx, transport

def parse(result):
    try:
        if hasattr(result, 'content') and result.content:
            c = result.content[0]
            if hasattr(c, 'text'):
                try: return json.loads(c.text)
                except: return c.text
        return str(result)
    except: return str(result)

async def main():
    # Connect to required servers
    sessions = {}
    sessions["server-3"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "streamable-http")
    sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")
    sessions["server-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")

    # Execute workflow steps
    # Step 1: Predict pockets with fpocket
    result_1 = await sessions["server-3"].call_tool("run_fpocket", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

    # Step 2: Predict pockets with P2Rank
    result_2 = await sessions["server-3"].call_tool("pred_pocket_prank", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Get ChEMBL binding site data
    result_3 = await sessions["chembl-server"].call_tool("get_binding_site_by_id", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Visualize protein structure
    result_4 = await sessions["server-2"].call_tool("visualize_protein", arguments={})
    data_4 = parse(result_4)
    print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

    # Cleanup
    print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())