Kinopedia API

Backend API for kinase database built with FastAPI.

Features

• RESTful API for kinase data
• 8 kinases with ~15,000 substrates from KEA3
• Automatic API documentation (Swagger UI)
• Business logic enforcement (STK33 pseudokinase, CASK disputed status)
• Docker deployment ready
• 70%+ test coverage

Quick Start

Local Development

# Clone repository
git clone https://github.com/CogDisResLab/kinopedia-api
cd kinopedia-api

# Create virtual environment
py -m venv venv
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run server
uvicorn app.main:app --reload

Visit http://localhost:8000/docs for interactive API documentation.

Docker

# Build image
docker build -t kinopedia-api .

# Run container
docker run -p 8000:8000 kinopedia-api

### Using Docker Compose

docker-compose up

Visit: http://localhost:8000/docs

**Note:** Docker files are included and tested. Windows users may need to run Docker Desktop as Administrator.

### Without Docker

If Docker is not available, you can run directly:

py -m venv venv

venv\\Scripts\\activate

pip install -r requirements.txt

uvicorn app.main:app --reload

API Endpoints

Kinases

• GET /kinases/ - List all kinases (with optional filtering)
• GET /kinases/{id} - Get kinase details with substrate counts
• GET /kinases/{id}/substrates - Get all substrates for a kinase
• GET /kinases/{id}/conflicts - Get documented data conflicts

Substrates

• POST /substrates/ - Add new substrate annotation (with validation)

Structures

• GET /structures/pdb/{pdb_id} - Get PDB experimental structure
• GET /structures/alphafold/{protein_id} - Get AlphaFold predicted model

Business Logic

STK33 Pseudokinase Enforcement

STK33 is a confirmed catalytically inactive pseudokinase. Attempts to add substrates via POST will return 403 Forbidden with message: "Catalytically inactive pseudokinase cannot have functional substrates."

CASK Disputed Activity

CASK kinase activity status is disputed in the literature. GET requests include special metadata fields:

• status: "disputed_activity"
• note: "Literature contains conflicting reports on catalytic activity."

Testing

# Run all tests
pytest tests\ -v

# Run with coverage
pytest tests\ --cov=app --cov-report=term-missing

Current coverage: 88%

Database

Currently uses SQLite (kinopedia.db) for simplicity and portability.

For PostgreSQL setup in production, see POSTGRESQL_SETUP.md.

Project Structure

kinopedia-api/
├── app/                     # Application code
│   ├── __init__.py
│   ├── main.py              # FastAPI app initialization
│   ├── database.py          # Async database connection
│   ├── models.py            # SQLAlchemy ORM models
│   ├── schemas.py           # Pydantic validation schemas
│   └── routers/             # API route handlers
│       ├── __init__.py
│       ├── kinases.py       # Kinase endpoints
│       ├── substrates.py    # Substrate endpoints
│       └── structures.py    # Structure endpoints
├── tests/                   # Unit tests
│   ├── __init__.py
│   ├── test_kinases.py
│   └── test_substrates.py
├── kinopedia.db             # SQLite database (8 kinases, ~15K substrates)
├── Dockerfile               # Docker container configuration
├── docker-compose.yml       # Docker Compose orchestration
├── requirements.txt         # Python package dependencies
├── pytest.ini               # Pytest configuration
├── .gitignore               # Git ignore rules
└── README.md                # Project documentation

Technology Stack

• Framework: FastAPI 0.110+
• ORM: SQLAlchemy 2.0 (async)
• Database: SQLite (aiosqlite driver)
• Validation: Pydantic v2
• Testing: pytest + pytest-asyncio
• Python: 3.12+

Development

Created by: Sakshi Dhumma
Date: April 2026
Version: 1.0.0

Based on trial assignment database with 8 curated kinases: AKT1, CDK1, MAPK1, EGFR, SRC, CAMK2A, CASK, STK33

License

This project is for academic/research purposes.