comprehensive-protein-analysis

name	comprehensive-protein-analysis
description	Comprehensive protein analysis combining InterProScan domain identification with BLAST similarity search to provide complete functional and evolutionary annotation.
license	MIT license
metadata	{"skill-author":"PJLab"}
i18n	{"zh":{"description":"结合InterProScan与。"}}

Comprehensive Protein Analysis

Usage

1. MCP Server Definition

Use the same BioInfoToolsClient class as defined in the protein-blast-search skill.

2. Comprehensive Protein Analysis Workflow

This workflow combines InterProScan domain analysis with BLAST similarity search to provide a complete functional and evolutionary annotation of a protein sequence.

Workflow Steps:

1. Validate Input - Check protein sequence format
2. Run InterProScan - Identify functional domains and GO terms
3. Run BLAST Search - Find similar sequences and homologs
4. Integrate Results - Combine domain and homology information for comprehensive annotation

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 analyze
protein_sequence = """
MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN
"""

sequence_id = "INS_HUMAN"

## Step 1, 2 & 3: Run comprehensive analysis (InterProScan + BLAST)
result = await client.session.call_tool(
    "analyze_protein",
    arguments={
        "sequence": protein_sequence.strip(),
        "sequence_id": sequence_id,
        "databases": ["Pfam"],     # InterProScan databases
        "evalue": 1e-5,             # BLAST E-value threshold (more stringent)
        "max_hits": 10              # BLAST max hits
    },
    read_timeout_seconds=timedelta(seconds=1200)  # Allow up to 20 minutes
)

## Step 4: Parse and display comprehensive results
result_data = client.parse_result(result)

print(f"{'='*80}")
print(f"Comprehensive Protein Analysis: {sequence_id}")
print(f"{'='*80}\n")


ips_result = result_data.get(, {})
 ips_result.get():
    ips_data = ips_result.get(, {})
    domains = ips_data.get(, [])
    go_terms = ips_data.get(, [])

    ()
    ()
    ()
    ()

     domains:
        ()
         domain  domains:
            ()
             domain.get():
                ()
            locations = domain.get(, [])
             locations:
                loc = locations[]
                ()
        ()

     go_terms:
        ()
         go  go_terms[:]:  
            ()
            ()
         (go_terms) > :
            ()
        ()
:
    ()


blast_result = result_data.get(, {})
 blast_result.get():
    hits = blast_result.get(, [])

    ()
    ()
    ()
    ()

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


()
 domains:
    ()
 hits:
    most_similar = hits[]
    ()
    ()
()

 client.disconnect()

Tool Descriptions

BioInfo-Tools Server:

• analyze_protein: Comprehensive protein analysis combining InterProScan and BLAST
  • Args:
    • sequence (str): Protein sequence in amino acid single-letter code
    • sequence_id (str, optional): Identifier for the query sequence
    • databases (list, optional): InterProScan databases (default: ["Pfam"])
    • evalue (float, optional): BLAST E-value threshold (default: 0.01)
    • max_hits (int, optional): Maximum BLAST hits (default: 10)
  • Returns:
    • interproscan (dict): InterProScan analysis results
      • success (bool): Whether InterProScan completed
      • results (dict): Domains and GO terms
      • time_seconds (float): Execution time
    • blast (dict): BLAST search results
      • success (bool): Whether BLAST completed
      • hits (list): Similar proteins
      • total_hits (int): Number of matches
      • time_seconds (float): Execution time

Input/Output

Input:

• sequence: Protein sequence (amino acid single-letter code)
• sequence_id: Optional identifier for the query
• databases: List of InterProScan databases to query
• evalue: BLAST E-value threshold (lower = more stringent)
• max_hits: Maximum number of BLAST hits to return

Output:

• InterProScan Results:
  • Functional domains with positions
  • Protein family classifications
  • Gene Ontology annotations
• BLAST Results:
  • Homologous proteins across species
  • Sequence identity and alignment statistics
  • Evolutionary relationships

Analysis Strategy

This comprehensive approach provides:

1. Structural Information (InterProScan):

   • Domain architecture and organization
   • Functional motifs and active sites
   • Protein family membership

2. Evolutionary Context (BLAST):

   • Homologs in other species
   • Sequence conservation patterns
   • Potential orthologs and paralogs

3. Functional Prediction:

   • Combining domain and homology information
   • GO term annotations for molecular function
   • Biological process involvement

Performance Notes

• Total execution time: 2-20 minutes depending on sequence length
  • InterProScan: 30 seconds to 15 minutes
  • BLAST: 10-90 seconds
  • Both run sequentially in this workflow
• Timeout recommendation: Set to at least 1200 seconds (20 minutes)
• E-value tuning: Use lower E-values (e.g., 1e-10) for highly conserved proteins, higher (e.g., 0.01) for divergent families

Use Cases

• Complete functional annotation of unknown proteins
• Validate predicted protein functions
• Study protein evolution and conservation
• Identify potential drug targets
• Annotate proteomes and genome sequences
• Compare protein function across species

Interpretation Tips

• High domain coverage + high homology: Well-characterized protein with known function
• Domains but no homologs: Novel protein with conserved domains, function can be inferred from domains
• Homologs but no domains: May need more sensitive domain detection or represents a novel fold
• Neither domains nor homologs: Potentially novel protein, may require experimental characterization