Protein Structure Prediction

SkillAI & models

Use ESMFold model to predict 3D structure of the input protein sequence.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Protein Structure Prediction skill

What this skill tells your AI

The instructions your AI receives, as published by spectrai-initiative/innoclaw in .claude/skills/drugsda-esmfold/SKILL.md and read by ahel’s review.

Usage

1. MCP Server Definition

import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class DrugSDAClient:
    def __init__(self, server_url: str):
        self.server_url = server_url
        self.session = None

    async def connect(self):
        print(f"server url: {self.server_url}")
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
            )
            self._stack = AsyncExitStack()
            await self._stack.__aenter__()
            self.read, self.write, self.get_session_id = await self._stack.enter_async_context(self.transport)

            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self._stack.enter_async_context(self.session_ctx)

            await self.session.initialize()
            session_id = self.get_session_id()

            print(f"✓ connect success")
            return True

        except Exception as e:
            print(f"✗ connect failure: {e}")
            import traceback
            traceback.print_exc()
            return False

    async def disconnect(self):
        """Disconnect from server"""
        try:
            if hasattr(self, '_stack'):
                await self._stack.aclose()
            print("✓ already disconnect")
        except Exception as e:
            print(f"✗ disconnect error: {e}")
    def parse_result(self, result):
        try:
            if hasattr(result, 'content') and result.content:
                content = result.content[0]
                if hasattr(content, 'text'):
                    return json.loads(content.text)
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}

2. ESMFold

The description of tool pred_protein_structure_esmfold.

Use the ESMFold model for protein 3D structure prediction.
Args:
    sequence (str): Protein sequence
Return:
    status: success/error
    msg: message
    pdb_path (str): The predicted pdb file path

How to use tool pred_protein_structure_esmfold :

client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
    print("connection failed")
    return

response = await client.session.call_tool(
    "pred_protein_structure_esmfold",
    arguments={
        "sequence": sequence
    }
)
result = client.parse_result(response)
pred_protein_structure = result["pdb_path"]

await client.disconnect()

Signals

GitHub stars
392
Forks
28
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
drugsda-esmfold-spectrai-initiative
Source
github.com/spectrai-initiative/innoclaw