BRENDA Database Smart

A scientific computing skill for querying the BRENDA enzyme database — the world's most comprehensive enzyme information system containing kinetic parameters, substrates, inhibitors, and functional data extracted from scientific literature for thousands of enzymes classified by EC number.

When to Use This Skill

Choose BRENDA Database Smart when:

• Looking up enzyme kinetic parameters (Km, Vmax, kcat, Ki)
• Finding substrates, products, and cofactors for specific enzymes
• Retrieving enzyme inhibitor information for drug discovery research
• Comparing enzyme properties across organisms and isoforms

Consider alternatives when:

• You need metabolic pathway context (use KEGG)
• You need protein structure data (use PDB or AlphaFold)
• You need gene expression data (use GEO or ArrayExpress)
• You need chemical compound details (use ChEBI or PubChem)

Quick Start

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claude "Look up kinetic parameters for human hexokinase (EC 2.7.1.1)"

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from bioservices import BRENDA
import hashlib

# Initialize BRENDA client
brenda = BRENDA(
    email="[email protected]",
    password="your_password"  # Register at brenda-enzymes.org
)

# Query enzyme by EC number
ec_number = "2.7.1.1"  # Hexokinase

# Get Km values
km_data = brenda.getKmValue(
    ecNumber=ec_number,
    organism="Homo sapiens"
)
for entry in km_data:
    print(f"Substrate: {entry['substrate']}")
    print(f"  Km: {entry['kmValue']} mM")
    print(f"  Reference: {entry['reference']}")

# Get turnover numbers (kcat)
kcat_data = brenda.getTurnoverNumber(
    ecNumber=ec_number,
    organism="Homo sapiens"
)
for entry in kcat_data:
    print(f"Substrate: {entry['substrate']}")
    print(f"  kcat: {entry['turnoverNumber']} s⁻¹")

Core Concepts

BRENDA Data Categories

Category	Method	Data Type
Km values	getKmValue()	Michaelis constant (mM)
kcat values	getTurnoverNumber()	Turnover number (s⁻¹)
Ki values	getKiValue()	Inhibition constant (mM)
Substrates	getSubstrate()	Natural and artificial substrates
Products	getProduct()	Reaction products
Cofactors	getCofactor()	Required cofactors
Inhibitors	getInhibitors()	Known inhibitors
pH Optimum	getPhOptimum()	Optimal pH
Temperature	getTemperatureOptimum()	Optimal temperature
Specific Activity	getSpecificActivity()	Activity (µmol/min/mg)

Comprehensive Enzyme Profile

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def enzyme_profile(brenda, ec_number, organism="Homo sapiens"):
    """Build a comprehensive profile for an enzyme"""
    profile = {"ec": ec_number, "organism": organism}

    # Kinetics
    km = brenda.getKmValue(ecNumber=ec_number, organism=organism)
    profile["km_values"] = [
        {"substrate": e["substrate"], "km_mM": e["kmValue"]}
        for e in km
    ]

    kcat = brenda.getTurnoverNumber(ecNumber=ec_number, organism=organism)
    profile["kcat_values"] = [
        {"substrate": e["substrate"], "kcat_per_s": e["turnoverNumber"]}
        for e in kcat
    ]

    # Conditions
    ph = brenda.getPhOptimum(ecNumber=ec_number, organism=organism)
    profile["ph_optimum"] = [e["phOptimum"] for e in ph]

    temp = brenda.getTemperatureOptimum(ecNumber=ec_number, organism=organism)
    profile["temp_optimum"] = [e["temperatureOptimum"] for e in temp]

    # Inhibitors
    inhib = brenda.getInhibitors(ecNumber=ec_number, organism=organism)
    profile["inhibitors"] = list(set(e["inhibitor"] for e in inhib))

    return profile

profile = enzyme_profile(brenda, "2.7.1.1")

Cross-Species Comparison

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def compare_enzyme_across_species(brenda, ec_number, organisms):
    """Compare kinetic parameters across species"""
    comparison = {}
    for org in organisms:
        km = brenda.getKmValue(ecNumber=ec_number, organism=org)
        kcat = brenda.getTurnoverNumber(ecNumber=ec_number, organism=org)
        comparison[org] = {
            "km_values": {e["substrate"]: e["kmValue"] for e in km},
            "kcat_values": {e["substrate"]: e["turnoverNumber"] for e in kcat}
        }
    return comparison

species = ["Homo sapiens", "Mus musculus", "Escherichia coli"]
comp = compare_enzyme_across_species(brenda, "2.7.1.1", species)

Configuration

Parameter	Description	Default
email	BRENDA account email	Required
password	BRENDA account password	Required
cache	Cache API responses	True
timeout	Request timeout in seconds	30
organism_filter	Default organism for queries	None (all organisms)

Best Practices

1. Filter by organism for relevant results. BRENDA contains data from thousands of organisms. Without filtering, you'll get kinetic parameters from bacterial, plant, and mammalian sources mixed together. Always specify the organism when you need data for a specific system.

2. Cross-reference with literature. BRENDA data is manually curated from publications, but curation has varying depth across enzymes. For critical kinetic parameters, check the original reference cited in the BRENDA entry to verify experimental conditions match your use case.

3. Use EC numbers, not enzyme names. Enzyme names can be ambiguous (multiple names for the same enzyme, or one name used for different enzymes). EC numbers are unambiguous — look up your enzyme's EC number in the EC nomenclature database first.

4. Account for experimental conditions. Km and kcat values depend heavily on pH, temperature, buffer composition, and substrate purity. Compare values measured under similar conditions rather than averaging all reported values.

5. Cache results for batch analyses. When profiling multiple enzymes in a metabolic pathway, cache BRENDA responses to avoid redundant API calls. Enable BioServices' built-in caching or implement your own local cache.

Common Issues

Authentication fails with BRENDA API. BRENDA requires a free account registered at brenda-enzymes.org. The password is transmitted as an MD5 hash by the BioServices wrapper. If authentication fails, verify your credentials work on the BRENDA website, then check that the BioServices version supports the current BRENDA API version.

No data returned for a valid EC number. Not all enzymes have been equally curated in BRENDA. Some EC numbers have extensive data while others have minimal entries. Try broader queries without organism filtering to see if data exists for other species, or check alternative databases (SABIO-RK for kinetics).

Kinetic values vary widely for the same enzyme. This is expected — different studies use different conditions, substrates, and protein preparations. Report ranges rather than single values. For modeling purposes, use values from the most relevant experimental conditions (same organism, physiological pH and temperature).