protein-complex-analysis

name: protein_complex_analysis description: "Protein Complex Visualization & Analysis - Analyze protein complex: download structure, visualize complex, extract chains, and calculate quality metrics. Use this skill for structural biology tasks involving retrieve protein data by pdbcode visualize complex extract pdb chains calculate pdb basic info. Combines 4 tools from 1 SCP server(s)."

Protein Complex Visualization & Analysis

Discipline: Structural Biology | Tools Used: 4 | Servers: 1

Description

Analyze protein complex: download structure, visualize complex, extract chains, and calculate quality metrics.

Tools Used

• retrieve_protein_data_by_pdbcode from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• visualize_complex from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• extract_pdb_chains from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• calculate_pdb_basic_info from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool

Workflow

1. Download complex structure
2. Visualize protein-ligand complex
3. Extract individual chains
4. Calculate structural statistics

Test Case

Input

{
    "pdb_code": "6LU7"
}

Expected Steps

1. Download complex structure
2. Visualize protein-ligand complex
3. Extract individual chains
4. Calculate structural statistics

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-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-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")

    # Execute workflow steps
    # Step 1: Download complex structure
    result_1 = await sessions["server-2"].call_tool("retrieve_protein_data_by_pdbcode", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

    # Step 2: Visualize protein-ligand complex
    result_2 = await sessions["server-2"].call_tool("visualize_complex", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Extract individual chains
    result_3 = await sessions["server-2"].call_tool("extract_pdb_chains", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Calculate structural statistics
    result_4 = await sessions["server-2"].call_tool("calculate_pdb_basic_info", 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())