SpectrAI-Initiative/protein_interaction_network

Protein Interaction Network Analysis

Discipline: Systems Biology | Tools Used: 4 | Servers: 2

Description

Build protein interaction network: map identifiers with STRING, get PPI network, compute enrichment, and link to KEGG pathways.

Tools Used

• mapping_identifiers from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING
• get_string_network_interaction from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING
• get_ppi_enrichment from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING
• kegg_link from kegg-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG

Workflow

1. Map gene names to STRING IDs
2. Get interaction network
3. Compute PPI enrichment
4. Link to KEGG pathways

Test Case

Input

{
    "genes": [
        "TP53",
        "BRCA1",
        "MDM2"
    ],
    "species": 9606
}

Expected Steps

1. Map gene names to STRING IDs
2. Get interaction network
3. Compute PPI enrichment
4. Link to KEGG pathways

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 = {
    "string-server": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING",
    "kegg-server": "https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG"
}

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: return c.text
        return str(result)
    except: return str(result)

async def main():
    async with AsyncExitStack() as stack:
        # Connect to required servers
        sessions = {}
        sessions["string-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", stack)
        sessions["kegg-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG", stack)

        # Execute workflow steps
        # Step 1: Map gene names to STRING IDs
        result_1 = await sessions["string-server"].call_tool("mapping_identifiers", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get interaction network
        result_2 = await sessions["string-server"].call_tool("get_string_network_interaction", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

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

        # Step 4: Link to KEGG pathways
        result_4 = await sessions["kegg-server"].call_tool("kegg_link", 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())