# Interproscan Pipeline

> InterProScan Analysis Pipeline - Run InterProScan for domain analysis, then enrich with UniProt data and STRING interactions. Use this skill for functional proteomics tasks involving interproscan analyze get uniprotkb entry by accession get functional enrichment query interpro. Combines 4 tools from 4 SCP server(s).

- Skill: `gabrielmoreira/interproscan-pipeline` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/interproscan-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/interproscan-pipeline/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/interproscan-pipeline

---


# InterProScan Analysis Pipeline

**Discipline**: Functional Proteomics | **Tools Used**: 4 | **Servers**: 4

## Description

Run InterProScan for domain analysis, then enrich with UniProt data and STRING interactions.

## Tools Used

- **`interproscan_analyze`** from `server-17` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools`
- **`get_uniprotkb_entry_by_accession`** from `uniprot-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt`
- **`get_functional_enrichment`** from `string-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING`
- **`query_interpro`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`

## Workflow

1. Run InterProScan
2. Get UniProt entry
3. Run functional enrichment
4. Get InterPro annotations

## Test Case

### Input
```json
{
    "sequence": "MKTIIALSYIFCLVFA",
    "accession": "P04637"
}
```

### Expected Steps
1. Run InterProScan
2. Get UniProt entry
3. Run functional enrichment
4. Get InterPro annotations

## 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",
    "uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt",
    "string-server": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING",
    "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["uniprot-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", stack)
        sessions["string-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", stack)
        sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack)

        # Execute workflow steps
        # Step 1: Run InterProScan
        result_1 = await sessions["server-17"].call_tool("interproscan_analyze", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get UniProt entry
        result_2 = await sessions["uniprot-server"].call_tool("get_uniprotkb_entry_by_accession", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Run functional enrichment
        result_3 = await sessions["string-server"].call_tool("get_functional_enrichment", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Get InterPro annotations
        result_4 = await sessions["server-1"].call_tool("query_interpro", 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())
```