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DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016454/ https://www.ncbi.nlm.nih.gov/pubmed/33325500 http://dx.doi.org/10.1093/bioinformatics/btaa1030 |
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author | Munro, Daniel Singh, Mona |
author_facet | Munro, Daniel Singh, Mona |
author_sort | Munro, Daniel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8016454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-80164542021-04-07 DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction Munro, Daniel Singh, Mona Bioinformatics Original Papers 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. Oxford University Press 2020-12-16 /pmc/articles/PMC8016454/ /pubmed/33325500 http://dx.doi.org/10.1093/bioinformatics/btaa1030 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Munro, Daniel Singh, Mona DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction |
title | DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction |
title_full | DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction |
title_fullStr | DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction |
title_full_unstemmed | DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction |
title_short | DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction |
title_sort | demask: a deep mutational scanning substitution matrix and its use for variant impact prediction |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016454/ https://www.ncbi.nlm.nih.gov/pubmed/33325500 http://dx.doi.org/10.1093/bioinformatics/btaa1030 |
work_keys_str_mv | AT munrodaniel demaskadeepmutationalscanningsubstitutionmatrixanditsuseforvariantimpactprediction AT singhmona demaskadeepmutationalscanningsubstitutionmatrixanditsuseforvariantimpactprediction |