alterlab-brenda

$npx mdskill add AlterLab-IEU/AlterLab-Academic-Skills/alterlab-brenda

Retrieve enzyme kinetics and data from BRENDA database via SOAP API.

  • Look up enzyme kinetic parameters like Km, kcat, and Ki.
  • Uses BRENDA SOAP API with credentials for access.
  • Queries by EC number to fetch substrate-specific enzyme data.
  • Returns kinetic data, reactions, and organism info in structured format.

SKILL.md

.github/skills/alterlab-brendaView on GitHub ↗
---
name: alterlab-brenda
description: Access the BRENDA enzyme database via its SOAP API to retrieve kinetic parameters (Km, kcat, Ki), reaction equations, organism data, and substrate-specific enzyme information indexed by EC number. Use when looking up enzyme kinetics, turnover numbers, or substrate specificity for biochemical research and metabolic pathway analysis. Part of the AlterLab Academic Skills suite.
license: MIT
allowed-tools: Read WebFetch Bash(curl:*) Bash(python:*)
compatibility: Requires free BRENDA account credentials (BRENDA_EMAIL/BRENDA_PASSWORD) for the SOAP API
metadata:
    skill-author: AlterLab
    version: "1.0.0"
---

# 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

BRENDA access is organized into nine capability areas. Copy-ready snippets for each live in
`references/capabilities.md`.

1. **Kinetic parameter retrieval** — Km, kcat, Vmax by EC number / organism / substrate.
2. **Reaction information** — reaction equations and stoichiometry.
3. **Enzyme discovery** — find enzymes by substrate, product, or reaction pattern.
4. **Organism-specific data** — compare enzyme properties across organisms.
5. **Environmental parameters** — optimal/stability pH and temperature, cofactors.
6. **Substrate specificity** — Km/Vmax/kcat per substrate, affinity ranking.
7. **Inhibition and activation** — Ki, IC50, activators and mechanisms.
8. **Enzyme engineering support** — thermophilic homologs, pH-stable variants.
9. **Kinetic modeling** — modeling parameters and Michaelis-Menten plots.

## Core Workflow

The typical entry point retrieves kinetic data by EC number, then parses the delimited
response:

```python
from scripts.brenda_client import get_km_values
from scripts.brenda_queries import parse_km_entry

km_data = get_km_values("1.1.1.1", organism="Saccharomyces cerevisiae")
for entry in km_data:
    parsed = parse_km_entry(entry)
    # parse_km_entry keys mirror the raw BRENDA fields: 'kmValue' (string)
    # plus a derived 'km_value_numeric' (float). There is no 'km_value' key.
    print(parsed.get("organism"), parsed.get("substrate"), parsed.get("km_value_numeric"))
```

EC numbers must be fully qualified (e.g. `1.1.1.1`, not `1.1.1`). Wildcards (`*`) broaden
searches. See `references/data_formats.md` for the response format and parsing helpers.

## Installation Requirements

```bash
uv pip install zeep requests pandas matplotlib seaborn
```

## Authentication Setup

BRENDA requires authentication credentials:

1. **Create .env file**:
```
[email protected]
BRENDA_PASSWORD=your_brenda_password
```

2. **Or set environment variables**:
```bash
export BRENDA_EMAIL="[email protected]"
export BRENDA_PASSWORD="your_brenda_password"
```

3. **Register for BRENDA access**:
   - Visit https://www.brenda-enzymes.org/
   - Create an account
   - Check your email for credentials
   - Note: There's also `BRENDA_EMIAL` (note the typo) for legacy support

## Helper Scripts

This skill ships three Python helper scripts under `scripts/`:

- `scripts/brenda_queries.py` — high-level enzyme data analysis (parsing, search, cross-organism
  comparison, environmental parameters, specificity, inhibitors/activators, engineering targets).
- `scripts/brenda_visualization.py` — kinetic/pH/temperature/substrate plots and Michaelis-Menten curves.
- `scripts/enzyme_pathway_builder.py` — enzymatic pathway and retrosynthetic route construction.

Full function inventories and usage are in `references/helper_scripts.md`.

## Reference Index

- **`references/api_reference.md`** — Complete SOAP API method docs, parameter lists/formats,
  EC number structure/validation, response specs, error codes, literature citation formats.
- **`references/capabilities.md`** — Copy-ready code for all nine capability areas.
- **`references/workflows.md`** — Six end-to-end workflows (discovery, cross-organism comparison,
  engineering targets, pathway construction, kinetic analysis, industrial selection).
- **`references/helper_scripts.md`** — Function inventory and usage for the three helper scripts.
- **`references/data_formats.md`** — BRENDA response formats, parsing patterns, rate limits,
  error handling, troubleshooting, and additional resources.

More from AlterLab-IEU/AlterLab-Academic-Skills

SkillDescription
alterlab-abm-mesaBuilds agent-based models of social systems with Mesa 3 — the current AgentSet API (model.agents.shuffle_do('step'), auto-assigned unique_id, mandatory super().__init__(seed=...)), cell spaces (mesa.discrete_space OrthogonalMooreGrid / classic mesa.space grids), the DataCollector, batch_run parameter sweeps, and SolaraViz — for emergence, segregation, diffusion, opinion dynamics, and cooperation models. It uses the Mesa 3.x API (the old mesa.time schedulers like RandomActivation are removed) and treats the model as a generative theory to be validated, not just run. Use when the request mentions an agent-based model, Mesa, simulating interacting agents, or emergent macro behavior from micro rules. For discrete-event (queueing/process) simulation prefer alterlab-simpy; for reinforcement learning prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.
alterlab-academic-careerPrepares academic career documents and professional-development materials for faculty and researchers. Use when drafting academic CVs, research statements, teaching philosophies, diversity statements, faculty-position cover letters, tenure dossiers, promotion narratives, academic portfolios, or mentorship statements, and when planning conference-networking strategy, building academic web presence, setting up ORCID profiles, or understanding impact metrics. Part of the AlterLab Academic Skills suite.
alterlab-accreditation-aolScaffolds program-level Assurance-of-Learning (AoL) documentation for AACSB (2020 Standard 5) and ABET (Criterion 3 Student Outcomes, Criterion 4 Continuous Improvement) accreditation — program learning outcomes / competency goals, curriculum-to-outcome mapping matrices, direct- and indirect-assessment plans, rubric design, and closing-the-loop continuous-improvement narratives — and validates the structure of an outcome-mapping matrix with scripts/aol_matrix.py. Use when the user needs AACSB or ABET assurance-of-learning material, a program-learning-outcomes set, a curriculum/outcome map or coverage matrix, a direct/indirect assessment plan, a closing-the-loop report, or accreditation self-study text. For single-course design or course rubrics prefer alterlab-teaching-design; for post-award grant reports prefer alterlab-grant-reporting. Part of the AlterLab Academic Skills suite.
alterlab-adaptyvSubmits and tracks protein-testing experiments on the Adaptyv Bio Foundry cloud lab (wet-lab validation), and optimizes protein sequences before submission with computational tools (NetSolP, SoluProt, SolubleMPNN, ESM). Use when designing proteins that need wet-lab validation - binding/affinity screening, expression testing, thermostability, or fluorescence assays - or when submitting experiments to the Foundry API, browsing the target catalog, tracking experiment status, retrieving results, or pre-screening sequences for solubility/expression. Triggers on "Adaptyv", "Foundry API", "cloud lab", "biolayer interferometry / BLI", "wet-lab validation". Part of the AlterLab Academic Skills suite.
alterlab-aeonRuns time series machine learning with the aeon library — classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search via scikit-learn compatible APIs. Use when working with temporal data, sequential patterns, or time-indexed observations (univariate or multivariate) that need specialized algorithms beyond standard ML approaches. Part of the AlterLab Academic Skills suite.
alterlab-akademik-tesvikComputes the annual Turkish academic-incentive (akademik teşvik) score for Devlet (state) university öğretim elemanı (academic staff) from a classified activity list, applying the verified Akademik Teşvik Ödeneği Yönetmeliği (2018/11834) Faaliyet Hesaplama Tablosu headline puanları (Proje 20, Araştırma 15, Yayın 30, Patent 30, Atıf 30, Tebliğ 20, Ödül 20) with the k (author-count), p (Q1 1/Q2 0.8/Q3 0.5/Q4 0.25), and r (project-role) coefficients via türü puanı = Σ(faaliyet oranları) × headline (MADDE 8/2), then enforcing the per-type headline ceilings, the 100 cap (MADDE 8/3), and the net-30 payment gate (MADDE 10/3) in a deterministic scripts/tesvik_score.py. Use when the user wants to calculate akademik teşvik puanı, check the en az 30 (net-30) threshold, apply TABLO 4 ceilings, or estimate teşvik eligibility; keeps its coefficients separate from doçentlik scoring. For doçentlik point eligibility prefer alterlab-docentlik-eligibility. Part of the AlterLab Academic Skills suite.
alterlab-alpha-vantageAccesses real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API (requires a free API key from alphavantage.co). Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Part of the AlterLab Academic Skills suite.
alterlab-alphafoldPredict protein 3D structures with AlphaFold2 via ColabFold — MMseqs2-accelerated MSAs, monomer and AlphaFold2-Multimer complex folding, and confidence-based validation (pLDDT, pTM/ipTM, PAE). Use when folding a protein sequence or complex from FASTA, generating a predicted structure with confidence metrics, ranking models, or checking self-consistency of a design. For co-folding a protein WITH a small-molecule ligand or predicting binding affinity prefer alterlab-boltz; for antibody–antigen or one-FASTA multi-entity complexes prefer alterlab-chai; to LOOK UP an already-computed structure prefer alterlab-alphafold-db; for ESM embeddings or inverse folding prefer alterlab-esm. Part of the AlterLab Academic Skills suite.
alterlab-alphafold-dbAccess the AlphaFold DB of 200M+ AI-PREDICTED protein structures — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computationally predicted 3D structure or when no experimental structure exists, for homology modeling, protein engineering, or structure-based drug discovery; for EXPERIMENTALLY determined structures (X-ray, cryo-EM, NMR) prefer alterlab-pdb, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.
alterlab-anndataBuild, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the QC, normalization, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for RNA velocity from spliced/unspliced layers prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.