Affinity Maturation

---
name: affinity_maturation
description: 'Affinity Maturation Pipeline - Affinity maturation: compute binding
  affinity, predict mutations, compute hydrophilicity, and predict drug-target interaction.
  Use this skill for antibody engineering tasks involving ComputeAffinityCalculator
  zero shot sequence prediction ComputeHydrophilicity PredictDrugTargetInteraction.
  Combines 4 tools from 3 SCP server(s).'
i18n:
  zh:
    description: 抗体亲和力成熟流程。
---

# 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
```json
{
    "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](https://scphub.intern-ai.org.cn).

```python
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())
```