Protein Similarity Search

---
name: protein_similarity_search
description: 'Protein Similarity Search - Search for similar proteins: extract sequence
  from PDB, search structures with FoldSeek, find homologs with STRING, and check
  UniProt. Use this skill for bioinformatics tasks involving extract pdb sequence
  foldseek search get best similarity hits between species search uniprotkb entries.
  Combines 4 tools from 3 SCP server(s).'
i18n:
  zh:
    description: 蛋白质相似性搜索。
---

# Protein Similarity Search

**Discipline**: Bioinformatics | **Tools Used**: 4 | **Servers**: 3

## Description

Search for similar proteins: extract sequence from PDB, search structures with FoldSeek, find homologs with STRING, and check UniProt.

## Tools Used

- **`extract_pdb_sequence`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`
- **`foldseek_search`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`
- **`get_best_similarity_hits_between_species`** from `string-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING`
- **`search_uniprotkb_entries`** from `uniprot-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt`

## Workflow

1. Extract sequence from PDB
2. Search similar structures via FoldSeek
3. Find cross-species homologs with STRING
4. Search UniProt for related entries

## Test Case

### Input
```json
{
    "pdb_id": "1AKE"
}
```

### Expected Steps
1. Extract sequence from PDB
2. Search similar structures via FoldSeek
3. Find cross-species homologs with STRING
4. Search UniProt for related entries

## Usage Example

> **Note:** Replace `<YOUR_SCP_HUB_API_KEY>` 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 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",
    "string-server": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING",
    "uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt"
}

async def connect(url, transport_type):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
    read, write, _ = await transport.__aenter__()
    ctx = ClientSession(read, write)
    session = await ctx.__aenter__()
    await session.initialize()
    return session, ctx, transport

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():
    # Connect to required servers
    sessions = {}
    sessions["server-1"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "sse")
    sessions["string-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", "streamable-http")
    sessions["uniprot-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", "streamable-http")

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

    # Step 2: Search similar structures via FoldSeek
    result_2 = await sessions["server-1"].call_tool("foldseek_search", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Find cross-species homologs with STRING
    result_3 = await sessions["string-server"].call_tool("get_best_similarity_hits_between_species", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Search UniProt for related entries
    result_4 = await sessions["uniprot-server"].call_tool("search_uniprotkb_entries", 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())
```