qzzqzzb/comprehensive-protein-analysis

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")

# InterProScan Results
ips_result = result_data.get("interproscan", {})
if ips_result.get("success"):
    ips_data = ips_result.get("results", {})
    domains = ips_data.get('domains', [])
    go_terms = ips_data.get('go_terms', [])

    print("=== DOMAIN ANALYSIS (InterProScan) ===")
    print(f"Execution time: {ips_result.get('time_seconds', '?')} seconds")
    print(f"Domains found: {len(domains)}")
    print(f"GO annotations: {len(go_terms)}\n")

    if domains:
        print("Functional Domains:")
        for domain in domains:
            print(f"  • {domain.get('name', 'N/A')} ({domain.get('database', 'N/A')})")
            if domain.get('description'):
                print(f"    Description: {domain.get('description')}")
            locations = domain.get('locations', [])
            if locations:
                loc = locations[0]
                print(f"    Position: {loc.get('start')}-{loc.get('end')} aa")
        print()

    if go_terms:
        print("Gene Ontology Annotations:")
        for go in go_terms[:5]:  # Show top 5
            print(f"  • {go.get('id', 'N/A')}: {go.get('name', 'N/A')}")
            print(f"    Category: {go.get('category', 'N/A')}")
        if len(go_terms) > 5:
            print(f"  ... and {len(go_terms) - 5} more")
        print()
else:
    print(f"❌ InterProScan failed: {ips_result.get('error', 'Unknown')}\n")

# BLAST Results
blast_result = result_data.get("blast", {})
if blast_result.get("success"):
    hits = blast_result.get('hits', [])

    print("=== HOMOLOGY SEARCH (BLAST) ===")
    print(f"Execution time: {blast_result.get('time_seconds', '?')} seconds")
    print(f"Similar sequences found: {blast_result.get('total_hits', 0)}")
    print(f"E-value threshold: {1e-5}\n")

    if hits:
        print("Top Homologous Proteins:")
        for i, hit in enumerate(hits[:5], 1):
            print(f"  {i}. {hit['uniprot_id']} - {hit.get('organism', 'N/A')}")
            print(f"     Description: {hit['description']}")
            print(f"     Identity: {hit['identity_percent']:.1f}%, E-value: {hit['evalue']:.2e}")
        if len(hits) > 5:
            print(f"  ... and {len(hits) - 5} more matches")
        print()
    else:
        print("No significant homologs found (E-value threshold may be too stringent)\n")
else:
    print(f"❌ BLAST failed: {blast_result.get('error', 'Unknown')}\n")

# Summary
print("=== FUNCTIONAL SUMMARY ===")
if domains:
    print(f"Protein Family: {domains[0].get('name', 'Unknown')}")
if hits:
    most_similar = hits[0]
    print(f"Most Similar Protein: {most_similar['uniprot_id']} ({most_similar['identity_percent']:.1f}% identity)")
    print(f"Organism: {most_similar.get('organism', 'Unknown')}")
print(f"{'='*80}")

await 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