blast_protein_analysis: Скилл для анализа белковых последовательностей

--- name: blast_protein_analysis description: "BLAST & Protein Analysis Pipeline - BLAST search followed by comprehensive protein analysis: BLAST, then structure prediction, properties, and function. Use this skill for sequence bioinformatics tasks involving blast search pred protein structure esmfold calculate protein sequence properties predict protein function. Combines 4 tools from 4 SCP server(s)." --- # BLAST & Protein Analysis Pipeline **Discipline**: Sequence Bioinformatics | **Tools Used**: 4 | **Servers**: 4 ## Description BLAST search followed by comprehensive protein analysis: BLAST, then structure prediction, properties, and function. ## Tools Used - **`blast_search`** from `server-17` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools` - **`pred_protein_structure_esmfold`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model` - **`calculate_protein_sequence_properties`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`predict_protein_function`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` ## Workflow 1. Run BLAST search 2. Predict structure for top hit 3. Calculate protein properties 4. Predict protein function ## Test Case ### Input ```json { "sequence": "MKTIIALSYIFCLVFA" } ``` ### Expected Steps 1. Run BLAST search 2. Predict structure for top hit 3. Calculate protein properties 4. Predict protein function ## 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-17": "https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools", "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory" } 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-17"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools", stack) sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack) sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack) # Execute workflow steps # Step 1: Run BLAST search result_1 = await sessions["server-17"].call_tool("blast_search", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Predict structure for top hit result_2 = await sessions["server-3"].call_tool("pred_protein_structure_esmfold", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Calculate protein properties result_3 = await sessions["server-2"].call_tool("calculate_protein_sequence_properties", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Predict protein function result_4 = await sessions["server-1"].call_tool("predict_protein_function", 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()) ```