Analysis of Large-Scale Mutagenesis Data To Assess the Impact of Single Amino Acid Substitutions

Gray, Vanessa E.; Hause, Ronald J.; Fowler, Douglas M.

doi:10.1534/genetics.117.300064

Cited by 113 publications

(121 citation statements)

References 38 publications

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“…In combination with biological competition assays, recent advances in gene sequencing have enabled "deep mutational scanning" of structure/function outcomes for large libraries of amino acid variants (Fowler & Fields, 2014;Gray, Hause, & Fowler, 2017;Roscoe, Thayer, Zeldovich, Fushman, & Bolon, 2013). Although the assay output is (i) a combination of all possible changes in stability and function, and (ii) sensitive to the threshold of the biological competition assay (Mavor et al, 2016), data for a given position usually report results for all The substituted regions (Chan et al, 2017) are colored with alternating green and magenta.…”

Section: Deep Mutational Scanning Of Tim Barrel Isozymesmentioning

confidence: 99%

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

et al. 2018

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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.

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Section: Deep Mutational Scanning Of Tim Barrel Isozymesmentioning

confidence: 99%

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

et al. 2018

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“…High throughput mutational analyses may allow for more comprehensive interrogation and validation of these amino acid interaction networks. For example, the Matthews groups conducted a comprehensive mutational analysis of three InGPS enzymes (i.e., >5000 mutations) expressed in yeast cells in which the endogenous IGPS gene was deleted, following the deep mutational scan approach developed by the Bolon group .…”

Section: Enzymes As Amino Acid Interaction Networkmentioning

confidence: 99%

Engineered control of enzyme structural dynamics and function

2018

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Enzymes undergo a range of internal motions from local, active site fluctuations to large-scale, global conformational changes. These motions are often important for enzyme function, including in ligand binding and dissociation and even preparing the active site for chemical catalysis. Protein engineering efforts have been directed towards manipulating enzyme structural dynamics and conformational changes, including targeting specific amino acid interactions and creation of chimeric enzymes with new regulatory functions. Post-translational covalent modification can provide an additional level of enzyme control. These studies have not only provided insights into the functional role of protein motions, but they offer opportunities to create stimulus-responsive enzymes. These enzymes can be engineered to respond to a number of external stimuli, including light, pH, and the presence of novel allosteric modulators. Altogether, the ability to engineer and control enzyme structural dynamics can provide new tools for biotechnology and medicine.

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“…Briefly explained, a MAVE involves creating a large library of variants and subsequently selecting for a property of interest [9][10][11][12] . MAVEs have been applied to a substantial number of proteins and domains, overall indicating a surprising mutational tolerance as many missense variants retain wild type-like function 13,14 .…”

Section: Introductionmentioning

confidence: 99%

Classifying disease-associated variants using measures of protein activity and stability

Jepsen¹,

Fowler

Hartmann‐Petersen³

et al. 2019

Preprint

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Decreased cost of human exome and genome sequencing provides new opportunities for diagnosing genetic disorders, but we need better and more robust methods for interpreting sequencing results including determining whether and by which mechanism a specific missense variants may be pathogenic. Using the protein PTEN (phosphatase and tensin homolog) as an example, we show how recent developments in both experiments and computational modelling can be used to determine whether a missense variant is likely to be pathogenic. One approach relies on multiplexed experiments that enable determination of the effect of all possible individual missense variants in a cellular assay. Another approach is to use computational methods to predict variant effects. We compare two different multiplexed experiments and two computational methods to classify variant effects in PTEN. We distinguish between methods that focus on effects on protein stability and proteinspecific methods that are more directly related to enzyme activity. Our results on PTEN suggest that ~60% of pathogenic variants cause loss of function because they destabilise the folded protein which is subsequently degraded. Methods that quantify a broader range of effects on PTEN activity perform better at predicting variant effects. Either experimental method performs better than the corresponding computational predictions.

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Analysis of Large-Scale Mutagenesis Data To Assess the Impact of Single Amino Acid Substitutions

Cited by 113 publications

References 38 publications

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

Engineered control of enzyme structural dynamics and function

Classifying disease-associated variants using measures of protein activity and stability

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