antibody_drug_development: Скилл для разработки антител

--- name: antibody_drug_development description: "Antibody Drug Development - Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction. Use this skill for biologics tasks involving get uniprotkb entry by accession get biotherapeutic by name ComputeProtPara ComputeHydrophilicity. Combines 4 tools from 3 SCP server(s)." --- # Antibody Drug Development **Discipline**: Biologics | **Tools Used**: 4 | **Servers**: 3 ## Description Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction. ## Tools Used - **`get_uniprotkb_entry_by_accession`** from `uniprot-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt` - **`get_biotherapeutic_by_name`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL` - **`ComputeProtPara`** from `server-29` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio` - **`ComputeHydrophilicity`** from `server-29` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio` ## Workflow 1. Get target protein info 2. Look up biotherapeutic in ChEMBL 3. Compute protein parameters 4. Analyze hydrophilicity ## Test Case ### Input ```json { "target_accession": "P04637", "biotherapeutic": "trastuzumab", "sequence": "MKTIIALSYIFCLVFA" } ``` ### Expected Steps 1. Get target protein info 2. Look up biotherapeutic in ChEMBL 3. Compute protein parameters 4. Analyze hydrophilicity ## 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](https://scphub.intern-ai.org.cn). ```python 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", "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "server-29": "https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio" } 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["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack) sessions["server-29"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", stack) # Execute workflow steps # Step 1: Get target protein info 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: Look up biotherapeutic in ChEMBL result_2 = await sessions["chembl-server"].call_tool("get_biotherapeutic_by_name", 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: Analyze hydrophilicity result_4 = await sessions["server-29"].call_tool("ComputeHydrophilicity", 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()) ```