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

TY - JOUR

T1 - DeMaSk

T2 - A deep mutational scanning substitution matrix and its use for variant impact prediction

AU - Munro, Daniel

AU - Singh, Mona

N1 - Publisher Copyright: © 2020 The Author(s) 2020. Published by Oxford University Press.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85107926715

UR - https://www.scopus.com/pages/publications/85107926715#tab=citedBy

U2 - 10.1093/bioinformatics/btaa1030

DO - 10.1093/bioinformatics/btaa1030

M3 - Article

C2 - 33325500

SN - 1367-4803

VL - 36

SP - 5322

EP - 5329

JO - Bioinformatics

JF - Bioinformatics

IS - 22-23

ER -