alpha-cpi

Using AlphaFold's Protein Embeddings to predict Compound-Protein Interactions

Running Notebook on EC2

1. Launch EC2 instance
2. Create jupyter password jupyter notebook password
3. Create SSL certificate: cd ~/ && mkdir ssl && cd ssl && openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout jupyter.key -out jupyter_cert.pem && cd ..
4. Start server (using nohup runs it in background): nohup jupyter notebook --certfile=~/ssl/jupyter_cert.pem --keyfile ~/ssl/jupyter.key &
5. Forward all requests to localhost: ssh <USER>@<IP_ADDRESS> -i ~/.ssh/id_rsa -N -f -L 8888:localhost:8888
6. Sign in to https://localhost:8888

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- Manuscript drafts, presentations, etc.
│
├── models             <- Trained and serialized models.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's GH username, and a short `-` delimited description, e.g.
│                         `1.0-wfondrie-initial-data-exploration`.
│
├── results            <- Generated analyses, such as figures and reports.
│
├── environment.yml    <- Specifies the dependencies to build a conda environment.
│                         Create the environment with `conda env create -f environment.yml`
│
├── pyproject.toml     <- Specifies Python build tools and setttings (flake8, black, etc.)
│
├── setup.cfg          <- Defines metadata about the project. 
│
├── setup.py           <- Makes project pip installable (pip install -e .) so src can be imported
│
└── src                <- Source code for use in this project.
    └── __init__.py    <- Makes src a Python module

This project is based on the Talus Cookiecutter Data Science Template

Tips and Tricks

Setting up your environment

First make sure that your environment.yml contains the tools you need for your analysis. The Makefile included in the repository already contains the rules to create or update the environment for your project, based on the environment.yml. From the root of your project, run:

$ make env

Then go ahead and activate your new environment

$ conda activate alpha-cpi

The src directory works like a Python package!

The default files we include allow the src directory to work like a Python package. If you've installed and activate you're environment above, then its ready to go! You can now use the functions and classes defined in src within your Jupyter notebooks:

# OPTIONAL: Load the "autoreload" extension so that code can change
%load_ext autoreload

# OPTIONAL: always reload modules so that as you change code in src, it gets loaded
%autoreload 2

from src.data import make_dataset

Enable Python code formatting with black

Black is a Python code formatting tool that helps us maintain uniform code formats throughout our projects. The easiest way to use Black is to set it up as a pre-commit hook. This way Black will run whenever you commit changes to your repository.

To enable Black, run from the root of your project:

$ pre-commit install