protein-solubility-optimization

Protein Solubility Optimization - Optimize protein solubility: calculate properties, predict solubility, predict hydrophilicity, and suggest mutations. Use this skill for protein engineering tasks involving calculate protein sequence properties predict protein function ComputeHydrophilicity zero shot sequence prediction. Combines 4 tools from 3 SCP server(s).

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Repository: SpectrAI-Initiative/InnoClaw
Letzte Quellaktivität: 1. April 2026 um 17:03
Erkannte Sprache von SKILL.md: Englisch
Sterne: 390
Forks: 28

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npx skills add https://github.com/SpectrAI-Initiative/InnoClaw --skill protein-solubility-optimization

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name	protein_solubility_optimization
description	Protein Solubility Optimization - Optimize protein solubility: calculate properties, predict solubility, predict hydrophilicity, and suggest mutations. Use this skill for protein engineering tasks involving calculate protein sequence properties predict protein function ComputeHydrophilicity zero shot sequence prediction. Combines 4 tools from 3 SCP server(s).

Protein Solubility Optimization

Discipline: Protein Engineering | Tools Used: 4 | Servers: 3

Description

Optimize protein solubility: calculate properties, predict solubility, predict hydrophilicity, and suggest mutations.

Tools Used

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
ComputeHydrophilicity from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio
zero_shot_sequence_prediction from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory

Workflow

Calculate physicochemical properties
Predict solubility
Compute hydrophilicity profile
Predict beneficial mutations for solubility

Test Case

Input

{
    "sequence"

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", "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "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["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) sessions["server-29"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", stack) # Execute workflow steps # Step 1: Calculate physicochemical properties result_1 = await sessions["server-2"].call_tool("calculate_protein_sequence_properties", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Predict solubility result_2 = await sessions["server-1"].call_tool("predict_protein_function", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Compute hydrophilicity profile result_3 = await sessions["server-29"].call_tool("ComputeHydrophilicity", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Predict beneficial mutations for solubility result_4 = await sessions["server-1"].call_tool("zero_shot_sequence_prediction", 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())

protein-solubility-optimization

Installationsoptionen

Quelldateien prüfen

Protein Solubility Optimization

Description

Tools Used

Workflow

Test Case

Input

Protein Solubility Optimization

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example

protein-solubility-optimization

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Protein Solubility Optimization

Description

Tools Used

Workflow

Test Case

Input

Protein Solubility Optimization

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example