Protein ML Colab Notebooks

on Education, Protein, and ML

Seven Google Colab notebooks made for the CSBERG Synthetic Biology course. Content delivered in Summer 2021.

The table of contents Colab notebook is here.

1. Introduction

  • Basic numpy and pytorch vectorized operations
  • .backward(), .grad, manual gradient optimization
  • Model saving and loading
  • Curse of dimensionality exercise
  • Loading .csv and .fasta files of sequences, one-hot encoding
  • PyTorch Dataset and DataLoader

2. Discriminative Models

  • Two layer fully-connected neural network for catalytic activity prediction
  • Rough Mount Fuji model

3. Generative Models

  • Representing multiple sequence alignments as matrices
  • Variational Auto-Encoders trained on Pfam aligned sequences
  • Sampling sequences from VAEs and visualizing results with sequence logos

4. Model-based Optimization

  • Latent space optimization
  • Conditioning by Adaptive Sampling (CbAS)

5. Inductive Bias

  • Potts model implementation in PyTorch
  • Attention (WIP) and nn.Embedding

6. Language Models

  • bio_embeddings
  • Exploratory code to benchmark random embeddings for protein property prediction

7. Structure-based Models

  • py3Dmol for visualizing structures in Colab
  • Distance matrix, orientograms from trRosetta
  • Molecular dynamics with OpenMM

Slides

The accompanying slides to notebooks 1, 2 and 3. Slides 1-28 can be delivered in about 2 hours.