Db Brenda

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:

[email protected]
BRENDA_PASSWORD=your_brenda_password

2. Or set environment variables:

export BRENDA_EMAIL="[email protected]"
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