full-protein-analysis

name	full_protein_analysis
description	Full Protein Characterization - Complete protein characterization: validate sequence, compute all properties, predict structure, and analyze pockets. Use this skill for protein biochemistry tasks involving is valid protein sequence analyze protein ComputeProtPara pred protein structure esmfold run fpocket. Combines 5 tools from 4 SCP server(s).

Full Protein Characterization

Discipline: Protein Biochemistry | Tools Used: 5 | Servers: 4

Description

Complete protein characterization: validate sequence, compute all properties, predict structure, and analyze pockets.

Tools Used

• is_valid_protein_sequence from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• analyze_protein from server-17 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools
• ComputeProtPara from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio
• pred_protein_structure_esmfold from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• run_fpocket from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model

Workflow

1. Validate sequence
2. Analyze protein features
3. Compute protein parameters
4. Predict 3D structure
5. Predict binding pockets

Test Case

Input

{
    "sequence": "MKTIIALSYIFCLVFAGKRDEFPSTWYV"
}

Expected Steps

1. Validate sequence
2. Analyze protein features
3. Compute protein parameters
4. Predict 3D structure
5. Predict binding pockets

Usage Example

Note: Replace sk-b04409a1-b32b-4511-9aeb-22980abdc05c with your own SCP Hub API Key. You can obtain one from the SCP Platform.

import asyncio
import json
from contextlib import AsyncExitStack
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",
    "server-17": "https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools",
    "server-29": "https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio",
    "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model"
}

async def connect(url, stack):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
    read, write, _ = await stack.enter_async_context(transport)
    ctx = ClientSession(read, write)
    session = await stack.enter_async_context(ctx)
    await session.initialize()
    return session

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:  c.text
         (result)
    :  (result)

  ():
      AsyncExitStack()  stack:
        
        sessions = {}
        sessions[] =  connect(, stack)
        sessions[] =  connect(, stack)
        sessions[] =  connect(, stack)
        sessions[] =  connect(, stack)

        
        
        result_1 =  sessions[].call_tool(, arguments={})
        data_1 = parse(result_1)
        ()

        
        result_2 =  sessions[].call_tool(, arguments={})
        data_2 = parse(result_2)
        ()

        
        result_3 =  sessions[].call_tool(, arguments={})
        data_3 = parse(result_3)
        ()

        
        result_4 =  sessions[].call_tool(, arguments={})
        data_4 = parse(result_4)
        ()

        
        result_5 =  sessions[].call_tool(, arguments={})
        data_5 = parse(result_5)
        ()

        
        ()

 __name__ == :
    asyncio.run(main())