Affinity Maturation

Affinity Maturation Pipeline

Discipline: Antibody Engineering | Tools Used: 4 | Servers: 3

Description

Affinity maturation: compute binding affinity, predict mutations, compute hydrophilicity, and predict drug-target interaction.

Tools Used

• ComputeAffinityCalculator from server-28 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent
• zero_shot_sequence_prediction from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory
• ComputeHydrophilicity from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio
• PredictDrugTargetInteraction from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio

Workflow

1. Compute baseline affinity
2. Predict beneficial mutations
3. Analyze hydrophilicity changes
4. Predict improved interactions

Test Case

Input

{
    "sequence": "MKTIIALSYIFCLVFA"
}

Expected Steps

1. Compute baseline affinity
2. Predict beneficial mutations
3. Analyze hydrophilicity changes
4. Predict improved interactions

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-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent",
    "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"
}

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-28"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "sse")
    sessions["server-1"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "sse")
    sessions["server-29"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", "sse")

    # Execute workflow steps
    # Step 1: Compute baseline affinity
    result_1 = await sessions["server-28"].call_tool("ComputeAffinityCalculator", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

    # Step 2: Predict beneficial mutations
    result_2 = await sessions["server-1"].call_tool("zero_shot_sequence_prediction", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

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

    # Step 4: Predict improved interactions
    result_4 = await sessions["server-29"].call_tool("PredictDrugTargetInteraction", 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())