brenda-database

BRENDA Enzyme Database

Overview

BRENDA (BRaunschweig ENzyme DAtabase) is the world's most comprehensive enzyme information system, containing 80,000+ enzyme entries covering all classified enzymes (EC numbers). It holds 7M+ experimentally measured kinetic parameters (Km, Vmax, kcat, Ki, inhibition constants), substrate specificity data, cofactor requirements, tissue expression, and organism-specific enzyme variants from 200,000+ literature references. Programmatic access is via a SOAP-based web service (Python zeep library) with free academic registration.

When to Use

• Retrieving kinetic parameters (Km, kcat, Vmax, Ki) for a specific enzyme and substrate combination
• Comparing kinetic parameters across organisms or mutant variants for an enzyme
• Finding natural substrates, inhibitors, and cofactors for an EC number
• Building kinetic models for metabolic simulations requiring Michaelis-Menten parameters
• Identifying enzyme-specific structural data (recommended pH, temperature optima)
• Cross-referencing EC numbers with UniProt accessions and organism taxonomy
• For metabolic network simulation use cobrapy-metabolic-modeling; for metabolite structures use hmdb-database

Prerequisites

• Python packages: zeep (SOAP client), pandas, requests
• Data requirements: EC numbers (e.g., 1.1.1.1), enzyme names, or organism names
• Environment: internet connection; free academic registration at https://www.brenda-enzymes.org/register.php
• Rate limits: no explicit limit stated; avoid bulk automated queries; space requests with sleep

pip install zeep pandas requests
# Register at https://www.brenda-enzymes.org/register.php to obtain API credentials

Quick Start

from zeep import Client

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = "your_sha256_hashed_password"  # Use hashlib.sha256

# Get Km values for lactate dehydrogenase (EC 1.1.1.27) and pyruvate
ec_number = "1.1.1.27"
params = (EMAIL, PASSWORD_SHA256,
          f"ecNumber*{ec_number}", "substrate*pyruvate", "", "", "", "", "")
result = client.service.getKmValue(*params)
print(f"Km values for LDH with pyruvate: {len(result)} records")
for r in result[:3]:
    print(f"  Km={r.kmValue} {r.kmValueMaximum or ''} mM | org: {r.organism} | PMID: {r.literature}")

Core API

Query 1: Km Values for Enzyme-Substrate Pair

Retrieve Michaelis constant (Km) values for a specific enzyme and substrate.

from zeep import Client
import hashlib, pandas as pd

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD = "your_password"
PASSWORD_SHA256 = hashlib.sha256(PASSWORD.encode()).hexdigest()

def get_km_values(ec_number, substrate=""):
    """Retrieve Km values for an EC number, optionally filtered by substrate."""
    substrate_param = f"substrate*{substrate}" if substrate else ""
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", substrate_param, "", "", "", "", "")
    return client.service.getKmValue(*params)

# Km for glucokinase (EC 2.7.1.2) with glucose
results = get_km_values("2.7.1.2", substrate="glucose")
print(f"Km (glucose, glucokinase): {len(results)} measurements")

rows = []
for r in results[:10]:
    rows.append({
        "km_value": r.kmValue,
        "km_max": r.kmValueMaximum,
        "unit": "mM",
        "organism": r.organism,
        "commentary": r.commentary[:80] if r.commentary else "",
        "pmid": r.literature,
    })
df = pd.DataFrame(rows)
print(df.to_string(index=False))

# Get ALL Km values (all substrates) for an EC number
all_km = get_km_values("1.1.1.1")  # Alcohol dehydrogenase
print(f"\nAlcohol dehydrogenase - total Km records: {len(all_km)}")
substrate_counts = {}
for r in all_km:
    sub = r.substrate or "unknown"
    substrate_counts[sub] = substrate_counts.get(sub, 0) + 1
top_substrates = sorted(substrate_counts.items(), key=lambda x: -x[1])[:5]
print("Top substrates by measurement count:")
for sub, cnt in top_substrates:
    print(f"  {sub}: {cnt} measurements")

Query 2: kcat (Turnover Number) Values

Retrieve catalytic rate constants (kcat) for an enzyme.

from zeep import Client
import hashlib, pandas as pd

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

def get_kcat_values(ec_number, substrate=""):
    substrate_param = f"substrate*{substrate}" if substrate else ""
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", substrate_param, "", "", "", "", "")
    return client.service.getTurnoverNumber(*params)

results = get_kcat_values("1.1.1.27")  # Lactate dehydrogenase
print(f"kcat records for LDH: {len(results)}")

rows = []
for r in results[:10]:
    rows.append({
        "kcat": r.turnoverNumber,
        "unit": "1/s",
        "substrate": r.substrate,
        "organism": r.organism,
    })
df = pd.DataFrame(rows)
print(df.head())

Query 3: Substrates and Products

Retrieve natural substrates and products for an enzyme.

from zeep import Client
import hashlib, pandas as pd

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

def get_substrates_products(ec_number):
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", "", "", "", "", "", "")
    return client.service.getSubstrates(*params)

results = get_substrates_products("4.2.1.1")  # Carbonic anhydrase
print(f"Substrates for carbonic anhydrase (EC 4.2.1.1):")
substrates_seen = set()
for r in results[:10]:
    if r.substrate not in substrates_seen:
        print(f"  {r.substrate} | organism: {r.organism}")
        substrates_seen.add(r.substrate)

# Get inhibitors
def get_inhibitors(ec_number):
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", "", "", "", "", "", "")
    return client.service.getInhibitors(*params)

inhibitors = get_inhibitors("4.2.1.1")
print(f"\nInhibitors of carbonic anhydrase: {len(inhibitors)} records")
inhib_names = list(set(r.inhibitor for r in inhibitors if r.inhibitor))
print("Sample inhibitors:", inhib_names[:8])

Query 4: Organism-Specific Enzyme Data

Query kinetic parameters filtered by organism.

from zeep import Client
import hashlib

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

def get_km_by_organism(ec_number, organism):
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", "", f"organism*{organism}", "", "", "", "")
    return client.service.getKmValue(*params)

# Human GAPDH Km values
human_km = get_km_by_organism("1.2.1.12", "Homo sapiens")
print(f"Human GAPDH (EC 1.2.1.12) Km values: {len(human_km)} records")
for r in human_km[:5]:
    print(f"  Substrate: {r.substrate:30s} Km={r.kmValue} mM")

Query 5: pH and Temperature Optima

Retrieve optimal pH and temperature data for an enzyme.

from zeep import Client
import hashlib

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

def get_ph_optimum(ec_number):
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", "", "", "", "", "", "")
    return client.service.getPhOptimum(*params)

def get_temp_optimum(ec_number):
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", "", "", "", "", "", "")
    return client.service.getTemperatureOptimum(*params)

ec = "3.4.21.4"  # Trypsin
ph_data = get_ph_optimum(ec)
temp_data = get_temp_optimum(ec)

print(f"Trypsin (EC {ec}):")
ph_values = [r.phOptimum for r in ph_data[:10] if r.phOptimum]
temp_values = [r.temperatureOptimum for r in temp_data[:10] if r.temperatureOptimum]
if ph_values:
    print(f"  pH optima: {sorted(ph_values)}")
if temp_values:
    print(f"  Temperature optima (°C): {sorted(temp_values)}")

Query 6: EC Number to UniProt Cross-Reference

Map EC numbers to UniProt accession numbers.

from zeep import Client
import hashlib

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

def get_uniprot_accessions(ec_number):
    params = (EMAIL, PASSWORD_SHA256,
              f"ecNumber*{ec_number}", "", "", "", "", "", "")
    return client.service.getUniprotAccession(*params)

results = get_uniprot_accessions("1.1.1.27")  # LDH
print(f"UniProt accessions for LDH (EC 1.1.1.27):")
seen = set()
for r in results[:10]:
    acc = r.uniprotAccessionNumber
    org = r.organism
    if acc and acc not in seen:
        print(f"  {acc:12s} ({org})")
        seen.add(acc)

Key Concepts

SOAP Interface and Authentication

BRENDA uses SOAP (not REST) via a WSDL definition. The zeep Python library parses the WSDL and generates typed method calls. Authentication requires a SHA256-hashed password (not plain text). Each service method takes (email, password_sha256, param1, param2, ..., "") arguments with pipe-delimited field filters.

EC Number Classification

Enzyme Commission (EC) numbers follow the format X.X.X.X where each level specifies the reaction class (oxidoreductases=1, transferases=2, hydrolases=3, lyases=4, isomerases=5, ligases=6, translocases=7). BRENDA organizes all data by EC number.

Common Workflows

Workflow 1: Kinetic Parameter Extraction for Metabolic Modeling

Goal: For a set of enzymes in a metabolic pathway, extract Km and kcat values to parameterize a kinetic model.

from zeep import Client
import hashlib, pandas as pd, time

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

# Glycolysis enzymes
enzymes = {
    "Hexokinase": "2.7.1.1",
    "Phosphoglucose isomerase": "5.3.1.9",
    "Phosphofructokinase": "2.7.1.11",
    "Aldolase": "4.1.2.13",
}

rows = []
for name, ec in enzymes.items():
    params = (EMAIL, PASSWORD_SHA256, f"ecNumber*{ec}", "", "organism*Homo sapiens", "", "", "", "")
    try:
        km_results = client.service.getKmValue(*params)
        kcat_results = client.service.getTurnoverNumber(*params)
        km_vals = [r.kmValue for r in km_results if r.kmValue]
        kcat_vals = [r.turnoverNumber for r in kcat_results if r.turnoverNumber]
        rows.append({
            "enzyme": name,
            "ec": ec,
            "n_km_records": len(km_vals),
            "km_median_mM": pd.Series(km_vals).median() if km_vals else None,
            "n_kcat_records": len(kcat_vals),
            "kcat_median_1_s": pd.Series(kcat_vals).median() if kcat_vals else None,
        })
    except Exception as e:
        rows.append({"enzyme": name, "ec": ec, "error": str(e)})
    time.sleep(0.5)

df = pd.DataFrame(rows)
df.to_csv("glycolysis_kinetics.csv", index=False)
print(df.to_string(index=False))

Workflow 2: Inhibitor Comparison Across Enzyme Family

Goal: Compare inhibitor landscape across a set of related enzymes for drug discovery prioritization.

from zeep import Client
import hashlib, pandas as pd, time
from collections import Counter

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

# Carbonic anhydrase isoforms
ca_ecs = ["4.2.1.1"]  # All carbonic anhydrases share this EC

rows = []
for ec in ca_ecs:
    params = (EMAIL, PASSWORD_SHA256, f"ecNumber*{ec}", "", "", "", "", "", "")
    try:
        inhib_results = client.service.getInhibitors(*params)
        for r in inhib_results[:30]:
            rows.append({
                "ec": ec,
                "inhibitor": r.inhibitor,
                "organism": r.organism,
                "ic50": r.ic50Value if hasattr(r, "ic50Value") else None,
            })
    except Exception as e:
        print(f"Error for {ec}: {e}")
    time.sleep(0.5)

df = pd.DataFrame(rows)
print(f"Total inhibitor records: {len(df)}")
top_inhib = Counter(df["inhibitor"]).most_common(10)
print("\nMost reported inhibitors:")
for inhib, count in top_inhib:
    print(f"  {inhib}: {count} records")

Key Parameters

Parameter	Module	Default	Range / Options	Effect
ecNumber*	All queries	required	EC number string	Filter by enzyme class
substrate*	Km, kcat	—	substrate name	Filter by substrate
organism*	All queries	—	species name	Filter by organism (e.g., "Homo sapiens")
commentary*	All queries	—	text substring	Filter by comment text
ligandStructureId*	Compound-based	—	BRENDA structure ID	Filter by ligand ID
Password	Auth	required	SHA256 hash	Authentication (hashlib.sha256)

Best Practices

1. Hash your password correctly: BRENDA requires SHA256 hash of the plain-text password, not the password itself. Use hashlib.sha256("your_password".encode()).hexdigest().

2. Store credentials in environment variables: Never hard-code credentials. Use os.environ["BRENDA_EMAIL"] and os.environ["BRENDA_PASSWORD"] patterns.

3. Add time.sleep() between queries: BRENDA's SOAP service may be slow; space large batch queries with 0.5–1 second sleeps to avoid timeouts.

4. Filter by organism for modeling: Kinetic parameters vary dramatically between organisms; always filter by the organism relevant to your model (e.g., organism*Homo sapiens).

5. Use median/IQR for parameter aggregation: Multiple literature measurements for the same substrate often span an order of magnitude; use median + IQR rather than mean to summarize distributions.

Common Recipes

Recipe: Get All Substrates for an EC Number

When to use: Understand the substrate scope of an enzyme for pathway analysis.

from zeep import Client
import hashlib

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

ec = "1.1.1.1"  # Alcohol dehydrogenase
params = (EMAIL, PASSWORD_SHA256, f"ecNumber*{ec}", "", "", "", "", "", "")
results = client.service.getSubstrates(*params)
substrates = list(set(r.substrate for r in results if r.substrate))
print(f"Substrates of EC {ec} ({len(substrates)} unique): {substrates[:10]}")

Recipe: kcat/Km Efficiency Ratio

When to use: Compute catalytic efficiency (kcat/Km) from BRENDA data.

import pandas as pd

# After fetching km_results and kcat_results for same ec + substrate
# km_values = [r.kmValue for r in km_results if r.kmValue]  # mM
# kcat_values = [r.turnoverNumber for r in kcat_results if r.turnoverNumber]  # 1/s

km_median = 0.1   # mM (example)
kcat_median = 500  # s^-1 (example)

efficiency = kcat_median / (km_median * 1e-3)  # Convert Km to M
print(f"Catalytic efficiency (kcat/Km): {efficiency:.2e} M^-1 s^-1")
# Diffusion limit ≈ 10^8-10^9 M^-1 s^-1

Recipe: Find EC Number from Enzyme Name

When to use: Resolve enzyme common name to EC number for BRENDA queries.

from zeep import Client
import hashlib

WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)

EMAIL = "[email protected]"
PASSWORD_SHA256 = hashlib.sha256("your_password".encode()).hexdigest()

# Search enzymes by name
params = (EMAIL, PASSWORD_SHA256, "recommendedName*lactate dehydrogenase", "", "", "", "", "", "")
results = client.service.getEcNumber(*params)
print(f"EC numbers for 'lactate dehydrogenase':")
for r in results[:5]:
    print(f"  EC {r.ecNumber}: {r.recommendedName}")

Troubleshooting

Problem	Cause	Solution
zeep.exceptions.Fault: Authentication failed	Wrong password or SHA256 format	Ensure hashlib.sha256(password.encode()).hexdigest() — hexdigest not digest
Empty result list	EC number or substrate not found	Verify EC format (X.X.X.X with dots); try without substrate filter first
SOAP timeout	Large query or slow connection	Use organism filter to reduce result set; set zeep transport timeout
AttributeError on result field	Field not available for this query	Use getattr(r, "field", None) to safely access optional fields
Slow response for popular enzymes	Large datasets (TP53 = 10K+ records)	Filter by organism and substrate to reduce data transfer
zeep.exceptions.TransportError	Network connectivity issue	Check VPN, retry after 30 seconds

Related Skills

• cobrapy-metabolic-modeling — Constraint-based metabolic modeling using Km/Vmax from BRENDA as kinetic constraints
• hmdb-database — Metabolite structure and biological context for BRENDA substrates
• kegg-database — Pathway context for BRENDA enzymes via EC number cross-references
• uniprot-protein-database — Protein sequence and structure data for enzymes found in BRENDA

References

• BRENDA database — Main BRENDA portal and manual search
• BRENDA web service documentation — SOAP API reference and parameter descriptions
• zeep Python SOAP client — Python library for SOAP web services
• Chang et al. (2021) BRENDA update — BRENDA 2021 database update paper