DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction
Daniel Munro, Mona Singh
Abstract
MOTIVATION: Accurately predicting the quantitative impact of a substitution on a protein's molecular function would be a great aid in understanding the effects of observed genetic variants across populations. While this remains a challenging task, new approaches can leverage data from the increasing numbers of comprehensive deep mutational scanning (DMS) studies that systematically mutate proteins and measure fitness. RESULTS: We introduce DeMaSk, an intuitive and interpretable method based only upon DMS datasets and sequence homologs that predicts the impact of missense mutations within any protein. DeMaSk first infers a directional amino acid substitution matrix from DMS datasets and then fits a linear model that combines these substitution scores with measures of per-position evolutionary conservation and variant frequency across homologs. Despite its simplicity, DeMaSk has state-of-the-art performance in predicting the impact of amino acid substitutions, and can easily and rapidly be applied to any protein sequence. AVAILABILITY AND IMPLEMENTATION: https://demask.princeton.edu generates fitness impact predictions and visualizations for any user-submitted protein sequence. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.