disease_protein_profiling
Disease Protein Profiling - Profile a disease protein: UniProt data, AlphaFold structure, InterPro domains, phenotype associations from Ensembl. Use this skill for medical proteomics tasks involving query uniprot download alphafold structure query interpro get phenotype gene. Combines 4 tools from 2 SCP server(s).
Best use case
disease_protein_profiling is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Disease Protein Profiling - Profile a disease protein: UniProt data, AlphaFold structure, InterPro domains, phenotype associations from Ensembl. Use this skill for medical proteomics tasks involving query uniprot download alphafold structure query interpro get phenotype gene. Combines 4 tools from 2 SCP server(s).
Teams using disease_protein_profiling should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/disease_protein_profiling/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How disease_protein_profiling Compares
| Feature / Agent | disease_protein_profiling | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Disease Protein Profiling - Profile a disease protein: UniProt data, AlphaFold structure, InterPro domains, phenotype associations from Ensembl. Use this skill for medical proteomics tasks involving query uniprot download alphafold structure query interpro get phenotype gene. Combines 4 tools from 2 SCP server(s).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Disease Protein Profiling
**Discipline**: Medical Proteomics | **Tools Used**: 4 | **Servers**: 2
## Description
Profile a disease protein: UniProt data, AlphaFold structure, InterPro domains, phenotype associations from Ensembl.
## Tools Used
- **`query_uniprot`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`
- **`download_alphafold_structure`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`
- **`query_interpro`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`
- **`get_phenotype_gene`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl`
## Workflow
1. Get UniProt protein data
2. Download AlphaFold predicted structure
3. Get InterPro domain info
4. Get phenotype associations
## Test Case
### Input
```json
{
"uniprot_id": "P04637",
"gene_symbol": "TP53",
"species": "homo_sapiens"
}
```
### Expected Steps
1. Get UniProt protein data
2. Download AlphaFold predicted structure
3. Get InterPro domain info
4. Get phenotype associations
## 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",
"ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl"
}
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["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack)
# Execute workflow steps
# Step 1: Get UniProt protein data
result_1 = await sessions["server-1"].call_tool("query_uniprot", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Download AlphaFold predicted structure
result_2 = await sessions["server-1"].call_tool("download_alphafold_structure", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get InterPro domain info
result_3 = await sessions["server-1"].call_tool("query_interpro", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Get phenotype associations
result_4 = await sessions["ensembl-server"].call_tool("get_phenotype_gene", 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())
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