— 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**: “`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**: “` [email protected] BRENDA_PASSWORD=your_brenda_password “` 2. **Or set environment variables**: “`bash 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**: “`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
f:T3df1,# BRENDA Database API Reference ## Overview This document provides detailed reference information for the BRENDA (BRaunschweig ENzyme DAtabase) SOAP API and the Python client implementation. BRENDA is the world's most comprehensive enzyme information system, containing over 45,000 enzymes with millions of kinetic data points. ## SOAP API Endpoints ### Base WSDL URL “` https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl “` ### Authentication All BRENDA API calls require authentication using email and password: **Parameters:** – `email`: Your registered BRENDA email address – `password`: Your BRENDA account password **Authentication Process:** 1. Password is hashed using SHA-256 before transmission 2. Email and hashed password are included as the first two parameters in every API call 3. Legacy support for `BRENDA_EMIAL` environment variable (note the typo) ## Available SOAP Actions ### getKmValue Retrieves Michaelis constant (Km) values for enzymes. **Parameters:** 1. `email`: BRENDA account email 2. `passwordHash`: SHA-256 hashed password 3. `ecNumber*: EC number of the enzyme (wildcards allowed) 4. `organism*: Organism name (wildcards allowed, default: "*") 5. `kmValue*: Km value field (default: "*") 6. `kmValueMaximum*: Maximum Km value field (default: "*") 7. `substrate*: Substrate name (wildcards allowed, default: "*") 8. `commentary*: Commentary field (default: "*") 9. `ligandStructureId*: Ligand structure ID field (default: "*") 10. `literature*: Literature reference field (default: "*") **Wildcards:** – `*`: Matches any sequence – Can be used with partial EC numbers (e.g., "1.1.*") **Response Format:** “` organism*Escherichia coli#substrate*glucose#kmValue*0.12#kmValueMaximum*#commentary*pH 7.4, 25°C#ligandStructureId*#literature* “` **Example Response Fields:** – `organism`: Source organism – `substrate`: Substrate name – `kmValue`: Michaelis constant value (typically in mM) – `kmValueMaximum`: Maximum Km value (if available) – `commentary`: Experimental conditions (pH, temperature, etc.) – `ligandStructureId`: BRENDA ligand structure identifier – `literature`: Reference to primary literature ### getReaction Retrieves reaction equations and stoichiometry for enzymes. **Parameters:** 1. `email`: BRENDA account email 2. `passwordHash`: SHA-256 hashed password 3. `ecNumber*: EC number of the enzyme (wildcards allowed) 4. `organism*: Organism name (wildcards allowed, default: "*") 5. `reaction*: Reaction equation (wildcards allowed, default: "*") 6. `commentary*: Commentary field (default: "*") 7. `literature*: Literature reference field (default: "*") **Response Format:** “` ecNumber*1.1.1.1#organism*Saccharomyces cerevisiae#reaction*ethanol + NAD+ <=> acetaldehyde + NADH + H+#commentary*#literature* “` **Example Response Fields:** – `ecNumber`: Enzyme Commission number – `organism`: Source organism – `reaction`: Balanced chemical equation (using <=> for equilibrium, -> for direction) – `commentary`: Additional information – `literature`: Reference citation ## Data Field Specifications ### EC Number Format EC numbers follow the standard hierarchical format: `A.B.C.D` – **A**: Main class (1-6) – 1: Oxidoreductases – 2: Transferases – 3: Hydrolases – 4: Lyases – 5: Isomerases – 6: Ligases – **B**: Subclass – **C**: Sub-subclass – **D**: Serial number **Examples:** – `1.1.1.1`: Alcohol dehydrogenase – `1.1.1.2`: Alcohol dehydrogenase (NADP+) – `3.2.1.23`: Beta-galactosidase – `2.7.1.1`: Hexokinase ### Organism Names Organism names should use proper binomial nomenclature: **Correct Format:** – `Escherichia coli` – `Saccharomyces cerevisiae` – `Homo sapiens` **Wildcards:** – `Escherichia*`: Matches all E. coli strains – `*coli`: Matches all coli species – `*`: Matches all organisms ### Substrate Names Substrate names follow IUPAC or common biochemical conventions: **Common Formats:** – Chemical names: `glucose`, `ethanol`, `pyruvate` – IUPAC names: `β-D-glucose`, `ethanol`, `2-oxopropanoic acid` – Abbreviations: `ATP`, `NAD+`, `CoA` **Special Cases:** – Cofactors: `NAD+`, `NADH`, `NADP+`, `NADPH` – Metal ions: `Mg2+`, `Zn2+`, `Fe2+` – Inorganic compounds: `H2O`, `CO2`, `O2` ### Commentary Field Format Commentary fields contain experimental conditions and other metadata: **Common Information:** – **pH**: `pH 7.4`, `pH 6.5-8.0` – **Temperature**: `25°C`, `37°C`, `50-60°C` – **Buffer systems**: `phosphate buffer`, `Tris-HCl` – **Purity**: `purified enzyme`, `crude extract` – **Assay conditions**: `spectrophotometric`, `radioactive` – **Inhibition**: `inhibited by heavy metals`, `activated by Mg2+` **Examples:** – `pH 7.4, 25°C, phosphate buffer` – `pH 6.5-8.0 optimum, thermostable enzyme` – `purified enzyme, specific activity 125 U/mg` – `inhibited by iodoacetate, activated by Mn2+` ### Reaction Equation Format Reactions use standard biochemical notation: **Symbols:** – `+`: Separate reactants/products – `<=>`: Reversible reactions – `->`: Irreversible (directional) reactions – `=`: Alternative notation for reactions **Common Patterns:** – **Oxidation/reduction**: `alcohol + NAD+ <=> aldehyde + NADH + H+` – **Phosphorylation**: `glucose + ATP <=> glucose-6-phosphate + ADP` – **Hydrolysis**: `ester + H2O <=> acid + alcohol` – **Carboxylation**: `acetyl-CoA + CO2 + H2O <=> malonyl-CoA` **Cofactor Requirements:** – **Oxidoreductases**: NAD+, NADH, NADP+, NADPH, FAD, FADH2 – **Transferases**: ATP, ADP, GTP, GDP – **Ligases**: ATP, CoA ## Rate Limiting and Usage ### API Rate Limits – **Maximum**: 5 requests per second – **Sustained**: 1 request per second recommended – **Daily quota**: Varies by account type ### Best Practices 1. **Implement delays**: Add 0.5-1 second between requests 2. **Cache results**: Store frequently accessed data locally 3. **Use specific searches**: Narrow by organism and substrate when possible 4. **Batch operations**: Group related queries 5. **Handle errors gracefully**: Check for HTTP and SOAP errors 6. **Use wildcards judiciously**: Broad searches return large datasets ### Error Handling **Common SOAP Errors:** – `Authentication failed`: Check email/password – `No data found`: Verify EC number, organism, substrate spelling – `Rate limit exceeded`: Reduce request frequency – `Invalid parameters`: Check parameter format and order **Network Errors:** – Connection timeouts – SSL/TLS errors – Service unavailable ## Python Client Reference ### brenda_client Module #### Core Functions **`load_env_from_file(path=".env")`** – **Purpose**: Load environment variables from .env file – **Parameters**: `path` – Path to .env file (default: ".env") – **Returns**: None (populates os.environ) **`_get_credentials() -> tuple[str, str]`** – **Purpose**: Retrieve BRENDA credentials from environment – **Returns**: Tuple of (email, password) – **Raises**: RuntimeError if credentials missing **`_get_client() -> Client`** – **Purpose**: Initialize or retrieve SOAP client – **Returns**: Zeep Client instance – **Features**: Singleton pattern, custom transport settings **`_hash_password(password: str) -> str`** – **Purpose**: Generate SHA-256 hash of password – **Parameters**: `password` – Plain text password – **Returns**: Hexadecimal SHA-256 hash **`call_brenda(action: str, parameters: List[str]) -> str`** – **Purpose**: Execute BRENDA SOAP action – **Parameters**: – `action` – SOAP action name (e.g., "getKmValue") – `parameters` – List of parameters in correct order – **Returns**: Raw response string from BRENDA #### Convenience Functions **`get_km_values(ec_number: str, organism: str = "*", substrate: str = "*") -> List[str]`** – **Purpose**: Retrieve Km values for specified enzyme – **Parameters**: – `ec_number`: Enzyme Commission number – `organism`: Organism name (wildcard allowed, default: "*") – `substrate`: Substrate name (wildcard allowed, default: "*") – **Returns**: List of parsed data strings **`get_reactions(ec_number: str, organism: str = "*", reaction: str = "*") -> List[str]`** – **Purpose**: Retrieve reaction data for specified enzyme – **Parameters**: – `ec_number`: Enzyme Commission number – `organism`: Organism name (wildcard allowed, default: "*") – `reaction`: Reaction pattern (wildcard allowed, default: "*") – **Returns**: List of reaction data strings #### Utility Functions **`split_entries(return_text: str) -> List[str]`** – **Purpose**: Normalize BRENDA responses to list format – **Parameters**: `return_text` – Raw response from BRENDA – **Returns**: List of individual data entries – **Features**: Handles both string and complex object responses ## Data Structures and Parsing ### Km Entry Structure **Parsed Km Entry Dictionary:** “`python { 'ecNumber': '1.1.1.1', 'organism': 'Escherichia coli', 'substrate': 'ethanol', 'kmValue': '0.12', 'km_value_numeric': 0.12, # Extracted numeric value 'kmValueMaximum': '', 'commentary': 'pH 7.4, 25°C', 'ph': 7.4, # Extracted from commentary 'temperature': 25.0, # Extracted from commentary 'ligandStructureId': '', 'literature': '' } “` ### Reaction Entry Structure **Parsed Reaction Entry Dictionary:** “`python { 'ecNumber': '1.1.1.1', 'organism': 'Saccharomyces cerevisiae', 'reaction': 'ethanol + NAD+ <=> acetaldehyde + NADH + H+', 'reactants': ['ethanol', 'NAD+'], 'products': ['acetaldehyde', 'NADH', 'H+'], 'commentary': '', 'literature': '' } “` ## Query Patterns and Examples ### Basic Queries **Get all Km values for an enzyme:** “`python from brenda_client import get_km_values # Get all alcohol dehydrogenase Km values km_data = get_km_values("1.1.1.1") “` **Get Km values for specific organism:** “`python # Get human alcohol dehydrogenase Km values human_km = get_km_values("1.1.1.1", organism="Homo sapiens") “` **Get Km values for specific substrate:** “`python # Get Km for ethanol oxidation ethanol_km = get_km_values("1.1.1.1", substrate="ethanol") “` ### Wildcard Searches **Search for enzyme families:** “`python # All alcohol dehydrogenases alcohol_dehydrogenases = get_km_values("1.1.1.*") # All hexokinases hexokinases = get_km_values("2.7.1.*") “` **Search for organism groups:** “`python # All E. coli strains e_coli_enzymes = get_km_values("*", organism="Escherichia coli") # All Bacillus species bacillus_enzymes = get_km_values("*", organism="Bacillus*") “` ### Combined Searches **Specific enzyme-substrate combination:** “`python # Get Km values for glucose oxidation in yeast glucose_km = get_km_values("1.1.1.1", organism="Saccharomyces cerevisiae", substrate="glucose") “` ### Reaction Queries **Get all reactions for an enzyme:** “`python from brenda_client import get_reactions reactions = get_reactions("1.1.1.1") “` **Search for reactions with specific substrates:** “`python # Find reactions involving glucose glucose_reactions = get_reactions("*", reaction="*glucose*") “` ## Data Analysis Patterns ### Kinetic Parameter Analysis **Extract numeric Km values:** “`python from scripts.brenda_queries import parse_km_entry km_data = get_km_values("1.1.1.1", substrate="ethanol") numeric_kms = [] for entry in km_data: parsed = parse_km_entry(entry) if 'km_value_numeric' in parsed: numeric_kms.append(parsed['km_value_numeric']) if numeric_kms: print(f"Average Km: {sum(numeric_kms)/len(numeric_kms):.3f}") print(f"Range: {min(numeric_kms):.3f} – {max(numeric_kms):.3f}") “` ### Organism Comparison **Compare enzyme properties across 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: if org_data.get('data_points', 0) > 0: print(f"{org_data['organism']}: {org_data['average_km']:.3f}") “` ### Substrate Specificity **Analyze substrate preferences:** “`python from scripts.brenda_queries import get_substrate_specificity specificity = get_substrate_specificity("1.1.1.1") for sub