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 ```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()) ```