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Interpreting protein variant effects with computational predictors and deep mutational scanning

Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their...

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Autores principales: Livesey, Benjamin J., Marsh, Joseph A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Company of Biologists Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235876/
https://www.ncbi.nlm.nih.gov/pubmed/35736673
http://dx.doi.org/10.1242/dmm.049510
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author Livesey, Benjamin J.
Marsh, Joseph A.
author_facet Livesey, Benjamin J.
Marsh, Joseph A.
author_sort Livesey, Benjamin J.
collection PubMed
description Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.
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spelling pubmed-92358762022-06-28 Interpreting protein variant effects with computational predictors and deep mutational scanning Livesey, Benjamin J. Marsh, Joseph A. Dis Model Mech Review Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors. The Company of Biologists Ltd 2022-06-23 /pmc/articles/PMC9235876/ /pubmed/35736673 http://dx.doi.org/10.1242/dmm.049510 Text en © 2022. Published by The Company of Biologists Ltd https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Review
Livesey, Benjamin J.
Marsh, Joseph A.
Interpreting protein variant effects with computational predictors and deep mutational scanning
title Interpreting protein variant effects with computational predictors and deep mutational scanning
title_full Interpreting protein variant effects with computational predictors and deep mutational scanning
title_fullStr Interpreting protein variant effects with computational predictors and deep mutational scanning
title_full_unstemmed Interpreting protein variant effects with computational predictors and deep mutational scanning
title_short Interpreting protein variant effects with computational predictors and deep mutational scanning
title_sort interpreting protein variant effects with computational predictors and deep mutational scanning
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235876/
https://www.ncbi.nlm.nih.gov/pubmed/35736673
http://dx.doi.org/10.1242/dmm.049510
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