--- name: protein-engineering-454eb66ad31e description: "Protein Engineering Workflow - Engineer a protein: predict structure, identify functional residues, predict beneficial mutations, and calculate properties. Use this skill for protein engineering tasks involving Protein structure prediction ESMFold predict functional residue zero shot sequence prediction calculate protein sequence properties. Combines 4 tools from 2 SCP server(s)." --- # Protein Engineering Workflow **Discipline**: Protein Engineering | **Tools Used**: 4 | **Servers**: 2 ## Description Engineer a protein: predict structure, identify functional residues, predict beneficial mutations, and calculate properties. ## Tools Used - **`Protein_structure_prediction_ESMFold`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`predict_functional_residue`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`zero_shot_sequence_prediction`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`calculate_protein_sequence_properties`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` ## Workflow 1. Predict 3D structure with ESMFold 2. Identify functional residues 3. Predict beneficial mutations 4. Calculate physicochemical properties ## Test Case ### Input ```json { "sequence": "MKTIIALSYIFCLVFAGKRDEFPSTWYV" } ``` ### Expected Steps 1. Predict 3D structure with ESMFold 2. Identify functional residues 3. Predict beneficial mutations 4. Calculate physicochemical properties ## 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", "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-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack) sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) # Execute workflow steps # Step 1: Predict 3D structure with ESMFold result_1 = await sessions["server-1"].call_tool("Protein_structure_prediction_ESMFold", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Identify functional residues result_2 = await sessions["server-1"].call_tool("predict_functional_residue", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Predict beneficial mutations result_3 = await sessions["server-1"].call_tool("zero_shot_sequence_prediction", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Calculate physicochemical properties result_4 = await sessions["server-2"].call_tool("calculate_protein_sequence_properties", 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()) ``` --- Source attribution: SpectrAI-Initiative Original project: https://github.com/SpectrAI-Initiative/InnoClaw License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: SpectrAI-Initiative Original project: https://github.com/SpectrAI-Initiative/InnoClaw Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering, Education & Research Roles: Data Scientist, ML / AI Engineer, Researcher Specialties: API Tools: Scp Hub Api Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 7635d4336de7459357de93e85e92e9f66004cce26ac885723c349f744f1ad488