Experimental illumination of a fitness landscape

Hietpas, Ryan T.; Jensen, Jeffrey D.; Bolon, Daniel N.

doi:10.1073/pnas.1016024108

Cited by 314 publications

(389 citation statements)

References 39 publications

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“…Deep mutational scanning is a powerful tool for exploring the molecular basis of protein function (7,15,25,26). However, restrictions on functional assays have limited its general applicability, particularly for enzymes.…”

Section: Discussionmentioning

confidence: 99%

“…Previous work in enzyme sequence-function mapping has used in vivo assays that couple an enzyme's function to cellular growth (7,(24)(25)(26). These in vivo selections are limited not only in the types of enzyme functions that can be analyzed, but also by the range of experimental conditions compatible with the intracellular environment.…”

Section: Romero Et Almentioning

confidence: 99%

“…Growth selections or in vitro binding screens can be combined with next-generation DNA sequencing to generate detailed mappings between a protein's sequence and its biochemical properties, such as binding affinity, enzymatic activity, and stability (6)(7)(8)(9). This deep mutational scanning approach has been used to study the structure of the protein fitness landscape, discover new functional sites, improve molecular energy functions, and identify beneficial combinations of mutations for protein engineering.…”

mentioning

confidence: 99%

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Dissecting enzyme function with microfluidic-based deep mutational scanning

Romero

Tran

Abate

2015

Proc. Natl. Acad. Sci. U.S.A.

212

183

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Natural enzymes are incredibly proficient catalysts, but engineering them to have new or improved functions is challenging due to the complexity of how an enzyme's sequence relates to its biochemical properties. Here, we present an ultrahigh-throughput method for mapping enzyme sequence-function relationships that combines droplet microfluidic screening with next-generation DNA sequencing. We apply our method to map the activity of millions of glycosidase sequence variants. Microfluidic-based deep mutational scanning provides a comprehensive and unbiased view of the enzyme function landscape. The mapping displays expected patterns of mutational tolerance and a strong correspondence to sequence variation within the enzyme family, but also reveals previously unreported sites that are crucial for glycosidase function. We modified the screening protocol to include a hightemperature incubation step, and the resulting thermotolerance landscape allowed the discovery of mutations that enhance enzyme thermostability. Droplet microfluidics provides a general platform for enzyme screening that, when combined with DNAsequencing technologies, enables high-throughput mapping of enzyme sequence space.protein engineering | droplet-based microfluidics | high-throughput DNA sequencing E nzymes are powerful biological catalysts capable of remarkably accelerating the rates of chemical transformations (1). The molecular bases of these rate accelerations are often complex, using multiple steps, multiple catalytic mechanisms, and relying on numerous molecular interactions, in addition to those provided by the main catalytic groups. This complexity imposes a significant barrier to understanding how an enzyme's sequence impacts its function and, thus, on our ability to rationally design biocatalysts with new or enhanced functions (2-4).Comprehensive mappings of sequence-function relationships can be used to dissect the molecular basis of protein function in an unbiased manner (5). Growth selections or in vitro binding screens can be combined with next-generation DNA sequencing to generate detailed mappings between a protein's sequence and its biochemical properties, such as binding affinity, enzymatic activity, and stability (6-9). This deep mutational scanning approach has been used to study the structure of the protein fitness landscape, discover new functional sites, improve molecular energy functions, and identify beneficial combinations of mutations for protein engineering. However, these methods rely on functional assays coupled to cell growth or protein binding, severely limiting the types of proteins that can be analyzed. For example, most enzymes of biological or industrial relevance cannot be analyzed using existing methods because they do not catalyze a reaction that can be directly coupled to cell growth. Experimental advances are needed to broaden the applicability of deep mutational scanning to the diverse palette of functions performed by enzymes.In this paper, we present a general method for mapping protein sequence-func...

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Section: Discussionmentioning

confidence: 99%

Section: Romero Et Almentioning

confidence: 99%

mentioning

confidence: 99%

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Dissecting enzyme function with microfluidic-based deep mutational scanning

Romero

Tran

Abate

2015

Proc. Natl. Acad. Sci. U.S.A.

212

183

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show abstract

“…Coupling of selection and high-throughput DNA sequencing has enabled methods to measure the function of large numbers (up to millions) of mutated versions of a protein (referred to here as variants) (2)(3)(4). These methods, known as "deep mutational scanning" (4), link the function of each variant with its abundance in a population of variants under selection for that function.…”

mentioning

confidence: 99%

A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function

Araya¹,

Fowler

Chen³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

243

264

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The ability of a protein to carry out a given function results from fundamental physicochemical properties that include the protein's structure, mechanism of action, and thermodynamic stability. Traditional approaches to study these properties have typically required the direct measurement of the property of interest, oftentimes a laborious undertaking. Although protein properties can be probed by mutagenesis, this approach has been limited by its low throughput. Recent technological developments have enabled the rapid quantification of a protein's function, such as binding to a ligand, for numerous variants of that protein. Here, we measure the ability of 47,000 variants of a WW domain to bind to a peptide ligand and use these functional measurements to identify stabilizing mutations without directly assaying stability. Our approach is rooted in the well-established concept that protein function is closely related to stability. Protein function is generally reduced by destabilizing mutations, but this decrease can be rescued by stabilizing mutations. Based on this observation, we introduce partner potentiation, a metric that uses this rescue ability to identify stabilizing mutations, and identify 15 candidate stabilizing mutations in the WW domain. We tested six candidates by thermal denaturation and found two highly stabilizing mutations, one more stabilizing than any previously known mutation. Thus, physicochemical properties such as stability are latent within these large-scale protein functional data and can be revealed by systematic analysis. This approach should allow other protein properties to be discovered.deep mutational scanning | epistasis | high-throughput DNA sequencing

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“…Next-generation sequencing technologies are beginning to allow us to overcome the first problem (Kvitek and Sherlock 2011;Ensminger et al 2012;Traverse et al 2013;Herron and Doebeli 2013), especially for the relatively simple phenotypic traits of molecules, where the second problem is not an issue. For such molecular traits, one can obtain complete genotypic information of many individuals in an evolving population (Hietpas et al 2011;Hayden et al 2011). The purpose of this paper is to study the evolution of both phenotypic and genotypic variation in an especially transparent molecular system, a population of evolving RNA enzymes.…”

mentioning

confidence: 99%

The Effects of Stabilizing and Directional Selection on Phenotypic and Genotypic Variation in a Population of RNA Enzymes

et al. 2013

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The distribution of variation in a quantitative trait and its underlying distribution of genotypic diversity can both be shaped by stabilizing and directional selection. Understanding either distribution is important, because it determines a population's response to natural selection. Unfortunately, existing theory makes conflicting predictions about how selection shapes these distributions, and very little pertinent experimental evidence exists. Here we study a simple genetic system, an evolving RNA enzyme (ribozyme) in which a combination of high throughput genotyping and measurement of a biochemical phenotype allow us to address this question. We show that directional selection, compared to stabilizing selection, increases the genotypic diversity of an evolving ribozyme population. In contrast, it leaves the variance in the phenotypic trait unchanged. AbstractThe distribution of variation in a quantitative trait and its underlying distribution of genotypic diversity can both be shaped by stabilizing and directional selection. Understanding either distribution is important, because it determines a population's response to natural selection. Unfortunately, existing theory makes conflicting predictions about how selection shapes these distributions, and very little pertinent experimental evidence exists. Here we study a simple genetic system, an evolving RNA enzyme (ribozyme) in which a combination of high throughput genotyping and measurement of a biochemical phenotype allow us to address this question. We show that directional selection, compared to stabilizing selection, increases the genotypic diversity of an evolving ribozyme population. In contrast, it leaves the variance in the phenotypic trait unchanged.3

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Experimental illumination of a fitness landscape

Cited by 314 publications

References 39 publications

Dissecting enzyme function with microfluidic-based deep mutational scanning

Dissecting enzyme function with microfluidic-based deep mutational scanning

A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function

The Effects of Stabilizing and Directional Selection on Phenotypic and Genotypic Variation in a Population of RNA Enzymes

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