protein-blast-search

name	protein-blast-search
description	Search for similar protein sequences in UniProt Swiss-Prot database using BLAST to identify homologous proteins and functional relationships.
license	MIT license
metadata	{"skill-author":"PJLab"}

Protein BLAST Sequence Similarity Search

Usage

1. MCP Server Definition

import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class BioInfoToolsClient:
    """BioInfo-Tools MCP Client"""

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

    async def connect(self):
        """Establish connection and initialize session"""
        print(f"server url: {self.server_url}")
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": self.api_key}
            )
            self.read, self.write, self.get_session_id = await self.transport.__aenter__()

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

            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 self.session:
                await self.session_ctx.__aexit__(None, None, None)
            if hasattr(self, 'transport'):
                await self.transport.__aexit__(None, None, None)
            print("✓ already disconnect")
        except Exception as e:
            print(f"✗ disconnect error: {e}")

    def parse_result(self, result):
        """Parse MCP tool call 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. Protein BLAST Search Workflow

This workflow searches for similar protein sequences in the UniProt Swiss-Prot database using BLAST, identifying homologous proteins and their functional relationships.

Workflow Steps:

1. Validate Input - Ensure protein sequence is in valid amino acid format
2. Execute BLAST Search - Query UniProt Swiss-Prot database for similar sequences
3. Parse Results - Extract matching proteins with identity, E-value, and organism information

Implementation:

from datetime import timedelta

## Initialize client
client = BioInfoToolsClient(
    "https://scp.intern-ai.org.cn/api/v1/mcp/17/BioInfo-Tools",
    "<your-api-key>"
)

if not await client.connect():
    print("connection failed")
    exit()

## Input: Protein sequence to search
protein_sequence = """
MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
"""

## Step 1 & 2: Execute BLAST search against UniProt Swiss-Prot
result = await client.session.call_tool(
    "blast_search",
    arguments={
        "sequence": protein_sequence.strip(),
        "sequence_id": "HBB_HUMAN",  # Optional identifier
        "evalue": 0.01,              # E-value threshold (default: 0.01)
        "max_hits": 50               # Maximum number of hits to return
    },
    read_timeout_seconds=timedelta(seconds=300)  # Allow up to 5 minutes
)

## Step 3: Parse and display results
result_data = client.parse_result(result)

if result_data.get("success"):
    print(f"✅ BLAST search completed successfully")
    print(f"Execution time: {result_data.get('time_seconds', '?')} seconds")
    print(f"Total hits found: {result_data.get('total_hits', )}\n")

    hits = result_data.get(, [])

    
     i, hit  (hits[:], ):
        ()
        ()
        ()
        ()
        ()
:
    ()

 client.disconnect()

Tool Descriptions

BioInfo-Tools Server:

• blast_search: Search for similar protein sequences in UniProt Swiss-Prot database
  • Args:
    • sequence (str): Protein sequence in amino acid single-letter code
    • sequence_id (str, optional): Identifier for the query sequence
    • evalue (float, optional): E-value threshold (default: 0.01)
    • max_hits (int, optional): Maximum number of hits to return (default: 50)
  • Returns:
    • success (bool): Whether search completed successfully
    • total_hits (int): Number of matching sequences found
    • hits (list): List of matching proteins with details
    • time_seconds (float): Execution time

Input/Output

Input:

• sequence: Protein sequence (amino acid single-letter code)
• sequence_id: Optional identifier for the query
• evalue: E-value threshold (lower = more stringent, default: 0.01)
• max_hits: Maximum number of results to return (default: 50)

Output:

• List of similar proteins, each containing:
  • uniprot_id: UniProt accession number
  • description: Protein description and name
  • organism: Species/organism name
  • identity_percent: Sequence identity percentage (0-100)
  • evalue: E-value (statistical significance, lower is better)
  • alignment_length: Length of sequence alignment
  • query_coverage: Percentage of query sequence covered

E-value Interpretation

• E-value < 1e-10: Highly significant match, very likely homologous
• E-value < 1e-5: Significant match, likely homologous
• E-value < 0.01: Potentially homologous (default threshold)
• E-value > 0.01: May be spurious matches

Use Cases

• Identify protein function by homology
• Find evolutionarily related proteins
• Discover orthologs and paralogs across species
• Annotate unknown protein sequences
• Study protein evolution and phylogeny

Performance Notes

• Typical execution time: 10-90 seconds depending on sequence length and max_hits
• Shorter sequences (<50 aa): May return more non-specific matches
• Longer sequences (>500 aa): May take longer but provide more specific matches
• Timeout recommendation: Set to at least 300 seconds (5 minutes) for reliability