brenda-database
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.
npx skills add majiayu000/claude-skill-registry --skill brenda-database-hxk622-tokendance-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill provides access to the BRENDA enzyme database through a SOAP API to retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.
How it works
- Core capabilities include: retrieving Km values by EC number (with optional organism or substrate filtering), parsing Km results, and extracting organism data.
- It retrieves reaction information by EC number, processes reaction data to obtain substrates and products, and filters by organism or specific reaction.
- It supports enzyme discovery by substrate, product, or reaction pattern, returning EC numbers, enzyme names, and reactions.
- It can compare enzyme data across multiple organisms for a given EC number, returning organism, average Km, optimal pH, and temperature range data.
- It provides organism-specific enzyme data and environmental parameters such as pH and temperature data, cofactor requirements, and substrate specificity.
- It includes inhibition/activation data, and enzyme engineering aids (thermophilic homologs and pH-stable variants).
- It offers kinetic modeling support, including parameters for modeling and Michaelis-Menten plotting; also includes a visualization module for plots.
- It includes helper scripts brenda_queries.py, brenda_visualization.py, and enzyme_pathway_builder.py with functions named for their respective tasks.
- Installation notes show a command snippet to install dependencies, and authentication setup describes creating a .env file or environment variables for BRENDA credentials.
- The API usage guidance covers rate limits, best practices, and error handling examples with specific function calls and exception types.
When to use it
Use this skill when you need:
- Km, kcat, Vmax data and enzyme kinetics
- Reaction equations and stoichiometry
- Enzymes related to a substrate or product of interest
- Cross-organism comparisons of enzyme properties
- Environmental parameters like optimal pH and temperature
- Inhibition/activation data and cofactors
- Engineering targets for thermostability or pH stability
- Data preparation for kinetic modeling and pathway design
What it can touch
- It relies on Python scripts under scripts/ (brenda_queries.py, brenda_visualization.py, enzyme_pathway_builder.py) to retrieve and process data from the BRENDA API.
- It references functions such as get_km_values, get_reactions, parse_km_entry, search_enzymes_by_substrate, compare_across_organisms, get_environmental_parameters, get_cofactor_requirements, get_substrate_specificity, get_inhibitors, get_activators, find_thermophilic_homologs, find_ph_stable_variants, get_modeling_parameters, plot_kinetic_parameters, plot_michaelis_menten, export_kinetic_data.
Caveats
- Authentication is required for BRENDA access via credentials or environment variables.
- The skill notes API rate limits and recommends caching results and batching queries.
- Some sections include example code and usage patterns; real execution requires valid BRENDA credentials and network access.
# 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**: ```pyt
- Overview
- When to Use This Skill
- Core Capabilities
- 1. Kinetic Parameter Retrieval
- 2. Reaction Information
- 3. Enzyme Discovery
- 4. Organism-Specific Enzyme Data
- 5. Environmental Parameters
- 6. Substrate Specificity
- 7. Inhibition and Activation
- 8. Enzyme Engineering Support
- 9. Kinetic Modeling
- Installation Requirements
- Authentication Setup
uv pip install zeep requests pandas matplotlib seaborn export BRENDA_EMAIL="your.email@example.com" export BRENDA_PASSWORD="your_brenda_password"
What does the brenda-database skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill brenda-database-hxk622-tokendance-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.