disease_protein_profiling: Скилл анализа белков заболеваний

--- name: disease_protein_profiling description: "Disease Protein Profiling - Profile a disease protein: UniProt data, AlphaFold structure, InterPro domains, phenotype associations from Ensembl. Use this skill for medical proteomics tasks involving query uniprot download alphafold structure query interpro get phenotype gene. Combines 4 tools from 2 SCP server(s)." --- # Disease Protein Profiling **Discipline**: Medical Proteomics | **Tools Used**: 4 | **Servers**: 2 ## Description Profile a disease protein: UniProt data, AlphaFold structure, InterPro domains, phenotype associations from Ensembl. ## Tools Used - **`query_uniprot`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`download_alphafold_structure`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`query_interpro`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`get_phenotype_gene`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` ## Workflow 1. Get UniProt protein data 2. Download AlphaFold predicted structure 3. Get InterPro domain info 4. Get phenotype associations ## Test Case ### Input ```json { "uniprot_id": "P04637", "gene_symbol": "TP53", "species": "homo_sapiens" } ``` ### Expected Steps 1. Get UniProt protein data 2. Download AlphaFold predicted structure 3. Get InterPro domain info 4. Get phenotype associations ## 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 = { "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl" } 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["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack) sessions["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack) # Execute workflow steps # Step 1: Get UniProt protein data result_1 = await sessions["server-1"].call_tool("query_uniprot", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Download AlphaFold predicted structure result_2 = await sessions["server-1"].call_tool("download_alphafold_structure", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get InterPro domain info result_3 = await sessions["server-1"].call_tool("query_interpro", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Get phenotype associations result_4 = await sessions["ensembl-server"].call_tool("get_phenotype_gene", 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()) ```