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RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions
Author(s) -
Hodges Abby M.,
Fenton Aron W.,
Dougherty Larissa L.,
Overholt Andrew C.,
SwintKruse Liskin
Publication year - 2018
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23616
Subject(s) - calculator , biology , stability (learning theory) , function (biology) , computational biology , amino acid substitution , substitution (logic) , mutation , aggregate (composite) , range (aeronautics) , genetics , bioinformatics , computer science , machine learning , gene , programming language , materials science , composite material , operating system
Abstract Human mutations often cause amino acid changes (variants) that can alter protein function or stability. Some variants fall at protein positions that experimentally exhibit “rheostatic” mutation outcomes (different amino acid substitutions lead to a range of functional outcomes). In ongoing studies of rheostat positions, we encountered the need to aggregate experimental results from multiple variants, to describe the overall roles of individual positions. Here, we present “RheoScale” which generates quantitative scores to discriminate rheostat positions from those with “toggle” (most substitutions abolish function) or “neutral” (most substitutions have wild‐type function) outcomes. RheoScale scores facilitate correlations of experimental data (such as binding affinity or stability) with structural and bioinformatic analyses. The RheoScale calculator is encoded into a Microsoft Excel workbook and an R script. Example analyses are shown for three model protein systems, including one assessed via deep mutational scanning. The RheoScale calculator quickly and efficiently provided quantitative descriptions that were in good agreement with prior qualitative observations. As an example application, scores were compared to the example proteins’ structures; strong rheostat positions tended to occur in dynamic locations. In the future, RheoScale scores can be easily integrated into computational studies to facilitate improved algorithms for predicting outcomes of human variants.