This repository contains the code to create EVE models as per our paper "Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning" (https://www.biorxiv.org/content/10.1101/2020.12.21.423785v1).

EVE is a set of protein-specific models providing for any single amino acid mutation of interest a score reflecting the propensity of the resulting protein to be pathogenic. For each protein family, a Bayesian VAE learns a distribution over amino acid sequences from evolutionary data. It enables the computation of an evolutionary index for each mutant, which approximates the log-likelihood ratio of the mutant vs the wild type. A global-local mixture of Gaussian Mixture Models separates variants into benign and pathogenic clusters based on that index. The EVE scores reflect probabilistic assignments to the pathogenic cluster.

The end to end process to compute EVE scores consists of three consecutive steps:

1. Train the Bayesian VAE on a re-weighted multiple sequence alignment (MSA) for the protein of interest => train_VAE.py
2. Compute the evolutionary indices for all single amino acid mutations => compute_evol_indices.py
3. Train a GMM to cluster variants on the basis of the evol indices then output scores and uncertainties on the class assignments => train_GMM_and_compute_EVE_scores.py We also provide all EVE scores for all single amino acid mutations for thousands of proteins at the following address: http://evemodel.org/.

The "examples" folder contains sample bash scripts to obtain EVE scores for the PTEN protein. The corresponding MSA and ClinVar labels are provided in the data folder.

The only data required to train EVE models and obtain scores from scratch are the multiple sequence alignments for the corresponding proteins. The third script (train_GMM_and_compute_EVE_scores.py) provides functionalities to compare EVE scores with reference labels (e.g., ClinVar) to be provided by the user.

The entire codebase is written in python. Package requirements are as follows:

• python=3.7
• pytorch=1.7
• cudatoolkit=11.0
• scikit-learn=0.24.1
• numpy=1.20.1
• pandas=1.2.4
• scipy=1.6.2
• tqdm
• matplotlib
• seaborn

The corresponding environment may be created via conda and the provided protein_env.yml file as follows:

  conda env create -f protein_env.yml
  conda activate protein_env

This project is available under the MIT license.

If you use this code, please cite the following paper:

Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning
Jonathan Frazer, Pascal Notin, Mafalda Dias, Aidan Gomez, Kelly Brock, Yarin Gal, Debora S. Marks
bioRxiv 2020.12.21.423785
doi: https://doi.org/10.1101/2020.12.21.423785