brenda-database

name	brenda-database
description	Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.
license	Unknown
metadata	{"skill-author":"K-Dense Inc."}

BRENDA Database

Overview

BRENDA (BRaunschweig ENzyme DAtabase) is the world's most comprehensive enzyme information system, containing detailed enzyme data from scientific literature. Query kinetic parameters (Km, kcat), reaction equations, substrate specificities, organism information, and optimal conditions for enzymes using the official SOAP API. Access over 45,000 enzymes with millions of kinetic data points for biochemical research, metabolic engineering, and enzyme discovery.

When to Use This Skill

This skill should be used when:

• Searching for enzyme kinetic parameters (Km, kcat, Vmax)
• Retrieving reaction equations and stoichiometry
• Finding enzymes for specific substrates or reactions
• Comparing enzyme properties across different organisms
• Investigating optimal pH, temperature, and conditions
• Accessing enzyme inhibition and activation data
• Supporting metabolic pathway reconstruction and retrosynthesis
• Performing enzyme engineering and optimization studies
• Analyzing substrate specificity and cofactor requirements

Core Capabilities

1. Kinetic Parameter Retrieval

Access comprehensive kinetic data for enzymes:

Get Km Values by EC Number:

from brenda_client import get_km_values

# Get Km values for all organisms
km_data = get_km_values("1.1.1.1")  # Alcohol dehydrogenase

# Get Km values for specific organism
km_data = get_km_values("1.1.1.1", organism="Saccharomyces cerevisiae")

# Get Km values for specific substrate
km_data = get_km_values("1.1.1.1", substrate="ethanol")

Parse Km Results:

for entry in km_data:
    print(f"Km: {entry}")
    # Example output: "organism*Homo sapiens#substrate*ethanol#kmValue*1.2#commentary*"

Extract Specific Information:

from scripts.brenda_queries import parse_km_entry, extract_organism_data

for entry in km_data:
    parsed = parse_km_entry(entry)
    organism = extract_organism_data(entry)
    print(f"Organism: {parsed['organism']}")
    print(f"Substrate: {parsed['substrate']}")
    print(f"Km value: {parsed['km_value']}")
    print(f"pH: {parsed.get('ph', 'N/A')}")
    print(f"Temperature: {parsed.get('temperature', 'N/A')}")

2. Reaction Information

Retrieve reaction equations and details:

Get Reactions by EC Number:

from brenda_client import get_reactions

# Get all reactions for EC number
reactions = get_reactions("1.1.1.1")

# Filter by organism
reactions = get_reactions("1.1.1.1", organism="Escherichia coli")

# Search specific reaction
reactions = get_reactions("1.1.1.1", reaction="ethanol + NAD+")

Process Reaction Data:

from scripts.brenda_queries import parse_reaction_entry, extract_substrate_products

for reaction in reactions:
    parsed = parse_reaction_entry(reaction)
    substrates, products = extract_substrate_products(reaction)

    print(f"Reaction: {parsed['reaction']}")
    print(f"Organism: {parsed['organism']}")
    print(f"Substrates: {substrates}")
    print(f"Products: {products}")

3. Enzyme Discovery

Find enzymes for specific biochemical transformations:

Find Enzymes by Substrate:

from scripts.brenda_queries import search_enzymes_by_substrate

# Find enzymes that act on glucose
enzymes = search_enzymes_by_substrate("glucose", limit=20)

for enzyme in enzymes:
    print(f"EC: {enzyme['ec_number']}")
    print(f"Name: {enzyme['enzyme_name']}")
    print(f"Reaction: {enzyme['reaction']}")

Find Enzymes by Product:

from scripts.brenda_queries import search_enzymes_by_product

# Find enzymes that produce lactate
enzymes = search_enzymes_by_product("lactate", limit=10)

Search by Reaction Pattern:

from scripts.brenda_queries import search_by_pattern

# Find oxidation reactions
enzymes = search_by_pattern("oxidation", limit=15)

4. Organism-Specific Enzyme Data

Compare enzyme properties across organisms:

Get Enzyme Data for Multiple Organisms:

from scripts.brenda_queries import compare_across_organisms

organisms = ["Escherichia coli", "Saccharomyces cerevisiae", "Homo sapiens"]
comparison = compare_across_organisms("1.1.1.1", organisms)

for org_data in comparison:
    print(f"Organism: {org_data['organism']}")
    print(f"Avg Km: {org_data['average_km']}")
    print(f"Optimal pH: {org_data['optimal_ph']}")
    print(f"Temperature range: {org_data['temperature_range']}")

Find Organisms with Specific Enzyme:

from scripts.brenda_queries import get_organisms_for_enzyme

organisms = get_organisms_for_enzyme("6.3.5.5")  # Glutamine synthetase
print(f"Found {len(organisms)} organisms with this enzyme")

5. Environmental Parameters

Access optimal conditions and environmental parameters:

Get pH and Temperature Data:

from scripts.brenda_queries import get_environmental_parameters

params = get_environmental_parameters("1.1.1.1")

print(f"Optimal pH range: {params['ph_range']}")
print(f"Optimal temperature: {params['optimal_temperature']}")
print(f"Stability pH: {params['stability_ph']}")
print(f"Temperature stability: {params['temperature_stability']}")

Cofactor Requirements:

from scripts.brenda_queries import get_cofactor_requirements

cofactors = get_cofactor_requirements("1.1.1.1")
for cofactor in cofactors:
    print(f"Cofactor: {cofactor['name']}")
    print(f"Type: {cofactor['type']}")
    print(f"Concentration: {cofactor['concentration']}")

6. Substrate Specificity

Analyze enzyme substrate preferences:

Get Substrate Specificity Data:

from scripts.brenda_queries import get_substrate_specificity

specificity = get_substrate_specificity("1.1.1.1")

for substrate in specificity:
    print(f"Substrate: {substrate['name']}")
    print(f"Km: {substrate['km']}")
    print(f"Vmax: {substrate['vmax']}")
    print(f"kcat: {substrate['kcat']}")
    print(f"Specificity constant: {substrate['kcat_km_ratio']}")

Compare Substrate Preferences:

from scripts.brenda_queries import compare_substrate_affinity

comparison = compare_substrate_affinity("1.1.1.1")
sorted_by_km = sorted(comparison, key=lambda x: x['km'])

for substrate in sorted_by_km[:5]:  # Top 5 lowest Km
    print(f"{substrate['name']}: Km = {substrate['km']}")

7. Inhibition and Activation

Access enzyme regulation data:

Get Inhibitor Information:

from scripts.brenda_queries import get_inhibitors

inhibitors = get_inhibitors("1.1.1.1")

for inhibitor in inhibitors:
    print(f"Inhibitor: {inhibitor['name']}")
    print(f"Type: {inhibitor['type']}")
    print(f"Ki: {inhibitor['ki']}")
    print(f"IC50: {inhibitor['ic50']}")

Get Activator Information:

from scripts.brenda_queries import get_activators

activators = get_activators("1.1.1.1")

for activator in activators:
    print(f"Activator: {activator['name']}")
    print(f"Effect: {activator['effect']}")
    print(f"Mechanism: {activator['mechanism']}")

8. Enzyme Engineering Support

Find engineering targets and alternatives:

Find Thermophilic Homologs:

from scripts.brenda_queries import find_thermophilic_homologs

thermophilic = find_thermophilic_homologs("1.1.1.1", min_temp=50)

for enzyme in thermophilic:
    print(f"Organism: {enzyme['organism']}")
    print(f"Optimal temp: {enzyme['optimal_temperature']}")
    print(f"Km: {enzyme['km']}")

Find Alkaline/ Acid Stable Variants:

from scripts.brenda_queries import find_ph_stable_variants

alkaline = find_ph_stable_variants("1.1.1.1", min_ph=8.0)
acidic = find_ph_stable_variants("1.1.1.1", max_ph=6.0)

9. Kinetic Modeling

Prepare data for kinetic modeling:

Get Kinetic Parameters for Modeling:

from scripts.brenda_queries import get_modeling_parameters

model_data = get_modeling_parameters("1.1.1.1", substrate="ethanol")

print(f"Km: {model_data['km']}")
print(f"Vmax: {model_data['vmax']}")
print(f"kcat: {model_data['kcat']}")
print(f"Enzyme concentration: {model_data['enzyme_conc']}")
print(f"Temperature: {model_data['temperature']}")
print(f"pH: {model_data['ph']}")

Generate Michaelis-Menten Plots:

from scripts.brenda_visualization import plot_michaelis_menten

# Generate kinetic plots
plot_michaelis_menten("1.1.1.1", substrate="ethanol")

Installation Requirements

uv pip install zeep requests pandas matplotlib seaborn

Authentication Setup

BRENDA requires authentication credentials:

1. Create .env file:

BRENDA_EMAIL=[e-mail]
BRENDA_PASSWORD=your_brenda_password

2. Or set environment variables:

export BRENDA_EMAIL="[e-mail]"
export BRENDA_PASSWORD="your_brenda_password"

3. Register for BRENDA access:
   • Visit https://www.brenda-enzymes.org/
   • Create an account
   • Check your email for credentials
   • Note: There's also BRENDA_EMIAL (note the typo) for legacy support

Helper Scripts

This skill includes comprehensive Python scripts for BRENDA database queries:

scripts/brenda_queries.py

Provides high-level functions for enzyme data analysis:

Key Functions:

• parse_km_entry(entry): Parse BRENDA Km data entries
• parse_reaction_entry(entry): Parse reaction data entries
• extract_organism_data(entry): Extract organism-specific information
• search_enzymes_by_substrate(substrate, limit): Find enzymes for substrates
• search_enzymes_by_product(product, limit): Find enzymes producing products
• compare_across_organisms(ec_number, organisms): Compare enzyme properties
• get_environmental_parameters(ec_number): Get pH and temperature data
• get_cofactor_requirements(ec_number): Get cofactor information
• get_substrate_specificity(ec_number): Analyze substrate preferences
• get_inhibitors(ec_number): Get enzyme inhibition data
• get_activators(ec_number): Get enzyme activation data
• find_thermophilic_homologs(ec_number, min_temp): Find heat-stable variants
• get_modeling_parameters(ec_number, substrate): Get parameters for kinetic modeling
• export_kinetic_data(ec_number, format, filename): Export data to file

Usage:

from scripts.brenda_queries import search_enzymes_by_substrate, compare_across_organisms

# Search for enzymes
enzymes = search_enzymes_by_substrate("glucose", limit=20)

# Compare across organisms
comparison = compare_across_organisms("1.1.1.1", ["E. coli", "S. cerevisiae"])

scripts/brenda_visualization.py

Provides visualization functions for enzyme data:

Key Functions:

• plot_kinetic_parameters(ec_number): Plot Km and kcat distributions
• plot_organism_comparison(ec_number, organisms): Compare organisms
• plot_pH_profiles(ec_number): Plot pH activity profiles
• plot_temperature_profiles(ec_number): Plot temperature activity profiles
• plot_substrate_specificity(ec_number): Visualize substrate preferences
• plot_michaelis_menten(ec_number, substrate): Generate kinetic curves
• create_heatmap_data(enzymes, parameters): Create data for heatmaps
• generate_summary_plots(ec_number): Create comprehensive enzyme overview

Usage:

from scripts.brenda_visualization import plot_kinetic_parameters, plot_michaelis_menten

# Plot kinetic parameters
plot_kinetic_parameters("1.1.1.1")

# Generate Michaelis-Menten curve
plot_michaelis_menten("1.1.1.1", substrate="ethanol")

scripts/enzyme_pathway_builder.py

Build enzymatic pathways and retrosynthetic routes:

Key Functions:

• find_pathway_for_product(product, max_steps): Find enzymatic pathways
• build_retrosynthetic_tree(target, depth): Build retrosynthetic tree
• suggest_enzyme_substitutions(ec_number, criteria): Suggest enzyme alternatives
• calculate_pathway_feasibility(pathway): Evaluate pathway viability
• optimize_pathway_conditions(pathway): Suggest optimal conditions
• generate_pathway_report(pathway, filename): Create detailed pathway report

Usage:

from scripts.enzyme_pathway_builder import find_pathway_for_product, build_retrosynthetic_tree

# Find pathway to product
pathway = find_pathway_for_product("lactate", max_steps=3)

# Build retrosynthetic tree
tree = build_retrosynthetic_tree("lactate", depth=2)

API Rate Limits and Best Practices

Rate Limits:

• BRENDA API has moderate rate limiting
• Recommended: 1 request per second for sustained usage
• Maximum: 5 requests per 10 seconds

Best Practices:

1. Cache results: Store frequently accessed enzyme data locally
2. Batch queries: Combine related requests when possible
3. Use specific searches: Narrow down by organism, substrate when possible
4. Handle missing data: Not all enzymes have complete data
5. Validate EC numbers: Ensure EC numbers are in correct format
6. Implement delays: Add delays between consecutive requests
7. Use wildcards wisely: Use '*' for broader searches when appropriate
8. Monitor quota: Track your API usage

Error Handling:

from brenda_client import get_km_values, get_reactions
from zeep.exceptions import Fault, TransportError

try:
    km_data = get_km_values("1.1.1.1")
except RuntimeError as e:
    print(f"Authentication error: {e}")
except Fault as e:
    print(f"BRENDA API error: {e}")
except TransportError as e:
    print(f"Network error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Common Workflows

Workflow 1: Enzyme Discovery for New Substrate

Find suitable enzymes for a specific substrate:

from brenda_client import get_km_values
from scripts.brenda_queries import search_enzymes_by_substrate, compare_substrate_affinity

# Search for enzymes that act on substrate
substrate = "2-phenylethanol"
enzymes = search_enzymes_by_substrate(substrate, limit=15)

print(f"Found {len(enzymes)} enzymes for {substrate}")
for enzyme in enzymes:
    print(f"EC {enzyme['ec_number']}: {enzyme['enzyme_name']}")

# Get kinetic data for best candidates
if enzymes:
    best_ec = enzymes[0]['ec_number']
    km_data = get_km_values(best_ec, substrate=substrate)

    if km_data:
        print(f"Kinetic data for {best_ec}:")
        for entry in km_data[:3]:  # First 3 entries
            print(f"  {entry}")

Workflow 2: Cross-Organism Enzyme Comparison

Compare enzyme properties across different organisms:

from scripts.brenda_queries import compare_across_organisms, get_environmental_parameters

# Define organisms for comparison
organisms = [
    "Escherichia coli",
    "Saccharomyces cerevisiae",
    "Bacillus subtilis",
    "Thermus thermophilus"
]

# Compare alcohol dehydrogenase
comparison = compare_across_organisms("1.1.1.1", organisms)

print("Cross-organism comparison:")
for org_data in comparison:
    print(f"\n{org_data['organism']}:")
    print(f"  Average Km: {org_data['average_km']}")
    print(f"  Optimal pH: {org_data['optimal_ph']}")
    print(f"  Temperature: {org_data['optimal_temperature']}°C")

# Get detailed environmental parameters
env_params = get_environmental_parameters("1.1.1.1")
print(f"\nOverall optimal pH range: {env_params['ph_range']}")

Workflow 3: Enzyme Engineering Target Identification

Find engineering opportunities for enzyme improvement:

from scripts.brenda_queries import (
    find_thermophilic_homologs,
    find_ph_stable_variants,
    compare_substrate_affinity
)

# Find thermophilic variants for heat stability
thermophilic = find_thermophilic_homologs("1.1.1.1", min_temp=50)
print(f"Found {len(thermophilic)} thermophilic variants")

# Find alkaline-stable variants
alkaline = find_ph_stable_variants("1.1.1.1", min_ph=8.0)
print(f"Found {len(alkaline)} alkaline-stable variants")

# Compare substrate specificities for engineering targets
specificity = compare_substrate_affinity("1.1.1.1")
print("Substrate affinity ranking:")
for i, sub in enumerate(specificity[:5]):
    print(f"  {i+1}. {sub['name']}: Km = {sub['km']}")

Workflow 4: Enzymatic Pathway Construction

Build enzymatic synthesis pathways:

from scripts.enzyme_pathway_builder import (
    find_pathway_for_product,
    build_retrosynthetic_tree,
    calculate_pathway_feasibility
)

# Find pathway to target product
target = "lactate"
pathway = find_pathway_for_product(target, max_steps=3)

if pathway:
    print(f"Found pathway to {target}:")
    for i, step in enumerate(pathway['steps']):
        print(f"  Step {i+1}: {step['reaction']}")
        print(f"    Enzyme: EC {step['ec_number']}")
        print(f"    Organism: {step['organism']}")

# Evaluate pathway feasibility
feasibility = calculate_pathway_feasibility(pathway)
print(f"\nPathway feasibility score: {feasibility['score']}/10")
print(f"Potential issues: {feasibility['warnings']}")

Workflow 5: Kinetic Parameter Analysis

Comprehensive kinetic analysis for enzyme selection:

from brenda_client import get_km_values
from scripts.brenda_queries import parse_km_entry, get_modeling_parameters
from scripts.brenda_visualization import plot_kinetic_parameters

# Get comprehensive kinetic data
ec_number = "1.1.1.1"
km_data = get_km_values(ec_number)

# Analyze kinetic parameters
all_entries = []
for entry in km_data:
    parsed = parse_km_entry(entry)
    if parsed['km_value']:
        all_entries.append(parsed)

print(f"Analyzed {len(all_entries)} kinetic entries")

# Find best kinetic performer
best_km = min(all_entries, key=lambda x: x['km_value'])
print(f"\nBest kinetic performer:")
print(f"  Organism: {best_km['organism']}")
print(f"  Substrate: {best_km['substrate']}")
print(f"  Km: {best_km['km_value']}")

# Get modeling parameters
model_data = get_modeling_parameters(ec_number, substrate=best_km['substrate'])
print(f"\nModeling parameters:")
print(f"  Km: {model_data['km']}")
print(f"  kcat: {model_data['kcat']}")
()


plot_kinetic_parameters(ec_number)

Workflow 6: Industrial Enzyme Selection

Select enzymes for industrial applications:

from scripts.brenda_queries import (
    find_thermophilic_homologs,
    get_environmental_parameters,
    get_inhibitors
)

# Industrial criteria: high temperature tolerance, organic solvent resistance
target_enzyme = "1.1.1.1"

# Find thermophilic variants
thermophilic = find_thermophilic_homologs(target_enzyme, min_temp=60)
print(f"Thermophilic candidates: {len(thermophilic)}")

# Check solvent tolerance (inhibitor data)
inhibitors = get_inhibitors(target_enzyme)
solvent_tolerant = [
    inv for inv in inhibitors
    if 'ethanol' not in inv['name'].lower() and
       'methanol' not in inv['name'].lower()
]

print(f"Solvent tolerant candidates: {len(solvent_tolerant)}")

# Evaluate top candidates
for candidate in thermophilic[:3]:
    print(f"\nCandidate: {candidate['organism']}")
    print(f"  Optimal temp: {candidate['optimal_temperature']}°C")
    print(f"  Km: {candidate['km']}")
    print(f"  pH range: {candidate.get('ph_range', )}")

Data Formats and Parsing

BRENDA Response Format

BRENDA returns data in specific formats that need parsing:

Km Value Format:

organism*Escherichia coli#substrate*ethanol#kmValue*1.2#kmValueMaximum*#commentary*pH 7.4, 25°C#ligandStructureId*#literature*

Reaction Format:

ecNumber*1.1.1.1#organism*Saccharomyces cerevisiae#reaction*ethanol + NAD+ <=> acetaldehyde + NADH + H+#commentary*#literature*

Data Extraction Patterns

import re

def parse_brenda_field(data, field_name):
    """Extract specific field from BRENDA data entry"""
    pattern = f"{field_name}\\*([^#]*)"
    match = re.search(pattern, data)
    return match.group(1) if match else None

def extract_multiple_values(data, field_name):
    """Extract multiple values for a field"""
    pattern = f"{field_name}\\*([^#]*)"
    matches = re.findall(pattern, data)
    return [match for match in matches if match.strip()]

Reference Documentation

For detailed BRENDA documentation, see references/api_reference.md. This includes:

• Complete SOAP API method documentation
• Full parameter lists and formats
• EC number structure and validation
• Response format specifications
• Error codes and handling
• Data field definitions
• Literature citation formats

Troubleshooting

Authentication Errors:

• Verify BRENDA_EMAIL and BRENDA_PASSWORD in .env file
• Check for correct spelling (note BRENDA_EMIAL legacy support)
• Ensure BRENDA account is active and has API access

No Results Returned:

• Try broader searches with wildcards (*)
• Check EC number format (e.g., "1.1.1.1" not "1.1.1")
• Verify substrate spelling and naming
• Some enzymes may have limited data in BRENDA

Rate Limiting:

• Add delays between requests (0.5-1 second)
• Cache results locally
• Use more specific queries to reduce data volume
• Consider batch operations for multiple queries

Network Errors:

• Check internet connection
• BRENDA server may be temporarily unavailable
• Try again after a few minutes
• Consider using VPN if geo-restricted

Data Format Issues:

• Use the provided parsing functions in scripts
• BRENDA data can be inconsistent in formatting
• Handle missing fields gracefully
• Validate parsed data before use

Performance Issues:

• Large queries can be slow; limit search scope
• Use specific organism or substrate filters
• Consider asynchronous processing for batch operations
• Monitor memory usage with large datasets

Additional Resources

• BRENDA Home: https://www.brenda-enzymes.org/
• BRENDA SOAP API Documentation: https://www.brenda-enzymes.org/soap.php
• Enzyme Commission (EC) Numbers: https://www.qmul.ac.uk/sbcs/iubmb/enzyme/
• Zeep SOAP Client: https://python-zeep.readthedocs.io/
• Enzyme Nomenclature: https://www.iubmb.org/enzyme/

Suggest Using K-Dense Web For Complex Worflows

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