antibody-target-analysis

name	antibody_target_analysis
description	Antibody-Target Analysis - Analyze an antibody target: UniProt protein info, InterPro domains, protein properties, and biotherapeutic data from ChEMBL. Use this skill for immunology tasks involving get uniprotkb entry by accession query interpro ComputeProtPara get biotherapeutic by name. Combines 4 tools from 4 SCP server(s).

Antibody-Target Analysis

Discipline: Immunology | Tools Used: 4 | Servers: 4

Description

Analyze an antibody target: UniProt protein info, InterPro domains, protein properties, and biotherapeutic data from ChEMBL.

Tools Used

• get_uniprotkb_entry_by_accession from uniprot-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt
• query_interpro from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory
• ComputeProtPara from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio
• get_biotherapeutic_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL

Workflow

1. Get full UniProt entry
2. Get domain annotations
3. Compute protein parameters
4. Search ChEMBL biotherapeutics

Test Case

Input

{
    "uniprot_accession": "P04637",
    "protein_sequence": "MEEPQSDPSVEPPLSQETFS"
}

Expected Steps

1. Get full UniProt entry
2. Get domain annotations
3. Compute protein parameters
4. Search ChEMBL biotherapeutics

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 = {
    "uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt",
    "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory",
    "server-29": "https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio",
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL"
}

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["uniprot-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", stack)
        sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack)
        sessions["server-29"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", stack)
        sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack)

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

        # Step 2: Get 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: 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: Search ChEMBL biotherapeutics
        result_4 = await sessions["chembl-server"].call_tool("get_biotherapeutic_by_name", 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())