BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-database.
---
name: "brenda-database"
description: "BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-database."
license: "CC-BY-4.0"
---
# 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
```bash
pip install zeep pandas requests
# Register at https://www.brenda-enzymes.org/register.php to obtain API credentials
```
## Quick Start
```python
from zeep import Client
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib, pandas as pd
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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))
```
```python
# 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.
```python
from zeep import Client
import hashlib, pandas as pd
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib, pandas as pd
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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)
```
```python
# 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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib, pandas as pd, time
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
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 = "your@email.com"
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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](https://www.brenda-enzymes.org/) — Main BRENDA portal and manual search
- [BRENDA web service documentation](https://www.brenda-enzymes.org/soap.php) — SOAP API reference and parameter descriptions
- [zeep Python SOAP client](https://docs.python-zeep.org/) — Python library for SOAP web services
- [Chang et al. (2021) BRENDA update](https://doi.org/10.1093/nar/gkaa1025) — BRENDA 2021 database update paper
---
name: "brenda-database"
description: "BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-database."
license: "CC-BY-4.0"
---
# 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
```bash
pip install zeep pandas requests
# Register at https://www.brenda-enzymes.org/register.php to obtain API credentials
```
## Quick Start
```python
from zeep import Client
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib, pandas as pd
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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))
```
```python
# 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.
```python
from zeep import Client
import hashlib, pandas as pd
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib, pandas as pd
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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)
```
```python
# 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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
from zeep import Client
import hashlib, pandas as pd, time
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
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 = "your@email.com"
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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.
```python
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.
```python
from zeep import Client
import hashlib
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl"
client = Client(WSDL)
EMAIL = "your@email.com"
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](https://www.brenda-enzymes.org/) — Main BRENDA portal and manual search
- [BRENDA web service documentation](https://www.brenda-enzymes.org/soap.php) — SOAP API reference and parameter descriptions
- [zeep Python SOAP client](https://docs.python-zeep.org/) — Python library for SOAP web services
- [Chang et al. (2021) BRENDA update](https://doi.org/10.1093/nar/gkaa1025) — BRENDA 2021 database update paper
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.