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About
Use cases: backend, data, research
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
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- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/brenda-database
What's inside
- Skill 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
- Apache-2.0
- Metadata
- skill-author: K-Dense Inc.
- Instructions
- 712 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- body: 712 lines (spec recommends under 500)
Files (8)
- SKILL.md
- api_reference.md · references/
- brenda_queries.cpython-313.pyc · scripts/__pycache__/
- brenda_visualization.cpython-313.pyc · scripts/__pycache__/
- enzyme_pathway_builder.cpython-313.pyc · scripts/__pycache__/
- brenda_queries.py · scripts/
- brenda_visualization.py · scripts/
- enzyme_pathway_builder.py · scripts/
SKILL.md (first lines)
# 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**:
```pythonFrom lamm-mit/scienceclaw, Apache-2.0.
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Signals
- Stars
- 250
- Forks
- 47
- Open issues
- 1
- Last commit (this folder)
- Mar 14, 2026 (7 months ago)
- Last release
- Jun 23, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/lamm-mit/scienceclaw
- Website
- agent-skills-hub.github.io/
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
brenda-database skill. Agent Mart. https://agentmart.dev/skills/lamm-mit-brenda-database (last updated 2026-10-24)