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 <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",
    "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, 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")
    sessions["server-17"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools", "streamable-http")
    sessions["server-29"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", "sse")
    sessions["server-3"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "streamable-http")

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

    # Step 2: Analyze protein features
    result_2 = await sessions["server-17"].call_tool("analyze_protein", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Compute protein parameters
    result_3 = await sessions["server-29"].call_tool("ComputeProtPara", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Predict 3D structure
    result_4 = await sessions["server-3"].call_tool("pred_protein_structure_esmfold", arguments={})
    data_4 = parse(result_4)
    print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

    # Step 5: Predict binding pockets
    result_5 = await sessions["server-3"].call_tool("run_fpocket", arguments={})
    data_5 = parse(result_5)
    print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}")

    # Cleanup
    print("Workflow complete!")

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