Protein Similarity Search

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

{
    "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.

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())