DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction.

TitleDeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction.
Publication TypeJournal Article
Year of Publication2020
AuthorsMunro, D, Singh, M
JournalBioinformatics
Date Published2020 Dec 16
ISSN1367-4811
Abstract

<p><b>MOTIVATION: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>AVAILABILITY: </b>https://demask.princeton.edu generates fitness impact predictions and visualizations for any user-submitted protein sequence.</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>

DOI10.1093/bioinformatics/btaa1030
Alternate JournalBioinformatics
PubMed ID33325500
Grant ListR01 GM076275 / GM / NIGMS NIH HHS / United States
T32 HG003284 / HG / NHGRI NIH HHS / United States