Deep mutational scanning: a new style of protein science

Fowler, Douglas M.; Fields, Stanley

doi:10.1038/nmeth.3027

Cited by 1,049 publications

(984 citation statements)

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“…Although functional analysis at this scale is made possible by deep mutational scanning (Fowler and Fields 2014), a central challenge is that any single assay may not recapitulate all the activities of a given protein in human disease. To address this challenge, we hypothesized that integrating the results of assays of multiple biochemical functions would strengthen estimates of the effects of mutations on disease risk (strategy outlined in Figure 1A).…”

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Massively Parallel Functional Analysis of BRCA1 RING Domain Variants

et al. 2015

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Interpreting variants of uncertain significance (VUS) is a central challenge in medical genetics. One approach is to experimentally measure the functional consequences of VUS, but to date this approach has been post hoc and low throughput. Here we use massively parallel assays to measure the effects of nearly 2000 missense substitutions in the RING domain of BRCA1 on its E3 ubiquitin ligase activity and its binding to the BARD1 RING domain. From the resulting scores, we generate a model to predict the capacities of full-length BRCA1 variants to support homology-directed DNA repair, the essential role of BRCA1 in tumor suppression, and show that it outperforms widely used biological-effect prediction algorithms. We envision that massively parallel functional assays may facilitate the prospective interpretation of variants observed in clinical sequencing.KEYWORDS deep mutational scanning; BRCA1; variants of uncertain significance; human genetic variation; protein function I N an era of increasingly widespread genetic testing, DNA sequencing identifies many missense substitutions with unknown effects on protein function and disease risk. In the absence of genetic evidence, experimental measurement is the most reliable way to determine the functional impact of a variant of uncertain significance (VUS). However, initiating an experiment for each new variant observed in a gene is often impractical. When experiments are done, they are nearly always performed in a retrospective manner (Bouwman et al. 2013), such that the resulting data are not useful for the patient in whom the VUS was observed.By prospectively measuring, in a high-throughput fashion, the consequences of all possible missense mutations on a gene's function, we can generate a look-up table for interpreting newly observed VUS. Although functional analysis at this scale is made possible by deep mutational scanning (Fowler and Fields 2014), a central challenge is that any single assay may not recapitulate all the activities of a given protein in human disease. To address this challenge, we hypothesized that integrating the results of assays of multiple biochemical functions would strengthen estimates of the effects of mutations on disease risk (strategy outlined in Figure 1A). As a proof-ofconcept, we initiated massively parallel functional analysis of BRCA1, a protein for which there are multiple biochemical functions as well as known pathogenic and benign missense substitutions to benchmark results.BRCA1 has been subject to intense study since its implication in hereditary, early onset breast and ovarian cancer (Miki et al. 1994). All missense substitutions in BRCA1 that are known to be pathogenic occur in either the amino-terminal RING domain or the carboxy-terminal BRCT repeat (http:// brca.iarc.fr/LOVD/home.php?select_db=BRCA1). Although the RING domain represents only 5% of the BRCA1 protein, 58% of the pathogenic missense substitutions occur within this domain. Sixty-two missense substitutions in the RING domain have been observed in patient...

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Massively Parallel Functional Analysis of BRCA1 RING Domain Variants

et al. 2015

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“…[16][17][18] Finally, deep mutational scans query the effect of amino acid substitutions on protein function. 19 Because these multiplexed functional assays have the capacity to test 10 4 -10 6 variants per experiment, they are already at the scale required for tackling the VUS problem.…”

Section: Introductionmentioning

confidence: 99%

Variant Interpretation: Functional Assays to the Rescue

Starita

Ahituv

Dunham

et al. 2017

The American Journal of Human Genetics

322

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Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of ''lookup tables'' of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.

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“…Deep mutational scanning (DMS) (Fowler et al , 2010; Fowler & Fields, 2014; Starita et al , 2014), a strategy for large‐scale functional testing of variants, can functionally annotate a large fraction of amino acid substitutions for a substantial subset of residue positions. Recent DMS studies, for example, covered the critical RING domain of BRCA1 (Starita et al , 2015) associated with breast cancer risk, and the PPARG protein associated with Mendelian lipodystrophy and increased risk of type 2 diabetes (Majithia et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

A framework for exhaustively mapping functional missense variants

Weile

Sun

Coté

et al. 2017

Molecular Systems Biology

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Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.

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Deep mutational scanning: a new style of protein science

Cited by 1,049 publications

References 52 publications

Massively Parallel Functional Analysis of BRCA1 RING Domain Variants

Massively Parallel Functional Analysis of BRCA1 RING Domain Variants

Variant Interpretation: Functional Assays to the Rescue

A framework for exhaustively mapping functional missense variants

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