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.
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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.
This skill should be used when:
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')}")
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}")
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)
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")
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']}")
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']}")
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']}")
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)
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")
uv pip install zeep requests pandas matplotlib seaborn
BRENDA requires authentication credentials:
[email protected]
BRENDA_PASSWORD=your_brenda_password
export BRENDA_EMAIL="[email protected]"
export BRENDA_PASSWORD="your_brenda_password"
BRENDA_EMIAL (note the typo) for legacy supportThis skill includes comprehensive Python scripts for BRENDA database queries:
Provides high-level functions for enzyme data analysis:
Key Functions:
parse_km_entry(entry): Parse BRENDA Km data entriesparse_reaction_entry(entry): Parse reaction data entriesextract_organism_data(entry): Extract organism-specific informationsearch_enzymes_by_substrate(substrate, limit): Find enzymes for substratessearch_enzymes_by_product(product, limit): Find enzymes producing productscompare_across_organisms(ec_number, organisms): Compare enzyme propertiesget_environmental_parameters(ec_number): Get pH and temperature dataget_cofactor_requirements(ec_number): Get cofactor informationUsage:
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"])
Provides visualization functions for enzyme data:
Key Functions:
plot_kinetic_parameters(ec_number): Plot Km and kcat distributionsplot_organism_comparison(ec_number, organisms): Compare organismsplot_pH_profiles(ec_number): Plot pH activity profilesplot_temperature_profiles(ec_number): Plot temperature activity profilesplot_substrate_specificity(ec_number): Visualize substrate preferencesplot_michaelis_menten(ec_number, substrate): Generate kinetic curvescreate_heatmap_data(enzymes, parameters): Create data for heatmapsgenerate_summary_plots(ec_number): Create comprehensive enzyme overviewUsage:
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")
Build enzymatic pathways and retrosynthetic routes:
Key Functions:
find_pathway_for_product(product, max_steps): Find enzymatic pathwaysbuild_retrosynthetic_tree(target, depth): Build retrosynthetic treesuggest_enzyme_substitutions(ec_number, criteria): Suggest enzyme alternativescalculate_pathway_feasibility(pathway): Evaluate pathway viabilityoptimize_pathway_conditions(pathway): Suggest optimal conditionsgenerate_pathway_report(pathway, filename): Create detailed pathway reportUsage:
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)
Rate Limits:
Best Practices:
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}")
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}")
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']}")
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']}")
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']}")
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']}")
print(f" Vmax: {model_data['vmax']}")
# Generate visualization
plot_kinetic_parameters(ec_number)
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', 'N/A')}")
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*
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()]
For detailed BRENDA documentation, see references/api_reference.md. This includes:
Authentication Errors:
No Results Returned:
Rate Limiting:
Network Errors:
Data Format Issues:
Performance Issues:
get_substrate_specificity(ec_number)get_inhibitors(ec_number): Get enzyme inhibition dataget_activators(ec_number): Get enzyme activation datafind_thermophilic_homologs(ec_number, min_temp): Find heat-stable variantsget_modeling_parameters(ec_number, substrate): Get parameters for kinetic modelingexport_kinetic_data(ec_number, format, filename): Export data to file
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).