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protein_function_annotation

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Protein Function Annotation Pipeline - Annotate protein function: UniProt metadata, InterPro domains, functional prediction, and GO enrichment. Use this skill for proteomics tasks involving query uniprot query interpro predict protein function get functional enrichment. Combines 4 tools from 2 SCP server(s).

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Protein Function Annotation Pipeline

Discipline: Proteomics | Tools Used: 4 | Servers: 2

Description

Annotate protein function: UniProt metadata, InterPro domains, functional prediction, and GO enrichment.

Tools Used

  • query_uniprot from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory
  • query_interpro from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory
  • predict_protein_function from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory
  • get_functional_enrichment from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING

Workflow

  1. Get UniProt metadata
  2. Get InterPro domain annotations
  3. Predict protein function
  4. Run GO enrichment analysis

Test Case

Input

{
    "uniprot_id": "P04637"
}

Expected Steps

  1. Get UniProt metadata
  2. Get InterPro domain annotations
  3. Predict protein function
  4. Run GO enrichment analysis

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-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory",
    "string-server": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING"
}

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["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack)
        sessions["string-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", stack)

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

        # Step 2: Get InterPro domain annotations
        result_2 = await sessions["server-1"].call_tool("query_interpro", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Predict protein function
        result_3 = await sessions["server-1"].call_tool("predict_protein_function", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Run GO enrichment analysis
        result_4 = await sessions["string-server"].call_tool("get_functional_enrichment", 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())