brenda-database

# 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**: ```python 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**: ```python for entry in km_data: print(f"Km: {entry}") # Example output: "organism*Homo sapiens#substrate*ethanol#kmValue*1.2#commentary*" ``` **Extract Specific Information**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python from scripts.brenda_visualization import plot_michaelis_menten # Generate kinetic plots plot_michaelis_menten("1.1.1.1", substrate="ethanol") ``` ## Installation Requirements ```bash uv pip install zeep requests pandas matplotlib seaborn ``` ## Authentication Setup BRENDA requires authentication credentials: 1. **Create .env file**: ``` BRENDA_EMAIL=your.email@example.com BRENDA_PASSWORD=your_brenda_password ``` 2. **Or set environment variables**: ```bash export BRENDA_EMAIL="your.email@example.com" 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**: ```python 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