protein_structure_analysis: Скилл анализа структуры белков

--- name: protein_structure_analysis description: "Protein Structure Comprehensive Analysis - Comprehensive structure analysis: download PDB, extract chains, calculate geometry, quality metrics, and composition. Use this skill for structural biology tasks involving retrieve protein data by pdbcode extract pdb chains calculate pdb structural geometry calculate pdb quality metrics calculate pdb composition info. Combines 5 tools from 1 SCP server(s)." --- # Protein Structure Comprehensive Analysis **Discipline**: Structural Biology | **Tools Used**: 5 | **Servers**: 1 ## Description Comprehensive structure analysis: download PDB, extract chains, calculate geometry, quality metrics, and composition. ## Tools Used - **`retrieve_protein_data_by_pdbcode`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`extract_pdb_chains`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_pdb_structural_geometry`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_pdb_quality_metrics`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_pdb_composition_info`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` ## Workflow 1. Download PDB structure 2. Extract chain sequences 3. Calculate structural geometry 4. Assess quality metrics 5. Analyze composition ## Test Case ### Input ```json { "pdb_code": "1AKE" } ``` ### Expected Steps 1. Download PDB structure 2. Extract chain sequences 3. Calculate structural geometry 4. Assess quality metrics 5. Analyze composition ## 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-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool" } 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-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) # Execute workflow steps # Step 1: Download PDB structure result_1 = await sessions["server-2"].call_tool("retrieve_protein_data_by_pdbcode", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Extract chain sequences result_2 = await sessions["server-2"].call_tool("extract_pdb_chains", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Calculate structural geometry result_3 = await sessions["server-2"].call_tool("calculate_pdb_structural_geometry", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Assess quality metrics result_4 = await sessions["server-2"].call_tool("calculate_pdb_quality_metrics", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Step 5: Analyze composition result_5 = await sessions["server-2"].call_tool("calculate_pdb_composition_info", arguments={}) data_5 = parse(result_5) print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```