Stabilizing Proteins from Sequence Statistics: The Interplay of Conservation and Correlation in Triosephosphate Isomerase Stability

Sullivan, Brandon J.; Nguyen, Tran; Durani, Venuka; Mathur, Deepti; Rojas, Samantha; Thomas, Miriam; Syu, Trixy; Magliery, Thomas J.

doi:10.1016/j.jmb.2012.04.025

Cited by 98 publications

(90 citation statements)

References 52 publications

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“…To investigate how our results compare with the natural sequence record, we used a large GH1 multiple sequence alignment to calculate a relative entropy sequence conservation score (22,23). Bgl3's mutational tolerance shows a strong correspondence with the observed GH1 sequence conservation.…”

Section: Significancementioning

confidence: 99%

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: Significancementioning

confidence: 99%

Dissecting enzyme function with microfluidic-based deep mutational scanning

Romero

Tran

Abate

2015

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

212

183

View full text Add to dashboard Cite

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“…These observations also suggested an algorithm to improve consensus design, by making consensus mutations at more-conserved positions and eliminating mutations at highly correlated positions [63,64]. The result in yeast TIM was a 90+% success rate in identifying stabilizing mutations, and large numbers of the mutations could be productively aggregated.…”

Section: Effects Of Sequence Correlationmentioning

confidence: 99%

Protein stability: computation, sequence statistics, and new experimental methods

Magliery

2015

Current Opinion in Structural Biology

136

123

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Calculating protein stability and predicting stabilizing mutations remain exceedingly difficult tasks, largely due to the inadequacy of potential functions, the difficulty of modeling entropy and the unfolded state, and challenges of sampling, particularly of backbone conformations. Yet, computational design has produced some remarkably stable proteins in recent years, apparently owing to near ideality in structure and sequence features. With caveats, computational prediction of stability can be used to guide mutation, and mutations derived from consensus sequence analysis, especially improved by recent co-variation filters, are very likely to stabilize without sacrificing function. The combination of computational and statistical approaches with library approaches, including new technologies such as deep sequencing and high throughput stability measurements, point to a very exciting near term future for stability engineering, even with difficult computational issues remaining.

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“…Consensus residues do not always improve protein stability. Sullivan et al (33) reported that removal of consensus mutations at positions for which their effect is statistically coupled to a residue(s) at another site(s) tends to improve stability. Therefore, the stability of ConsNDK might be increased if its sequence was optimized according to the finding of Sullivan et al…”

Section: Robustness Of the Tree Topologies Used To Infer The Ancestralmentioning

confidence: 99%

Experimental evidence for the thermophilicity of ancestral life

Akanuma

Nakajima

Yokobori

et al. 2013

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

157

195

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Theoretical studies have focused on the environmental temperature of the universal common ancestor of life with conflicting conclusions. Here we provide experimental support for the existence of a thermophilic universal common ancestor. We present the thermal stabilities and catalytic efficiencies of nucleoside diphosphate kinases (NDK), designed using the information contained in predictive phylogenetic trees, that seem to represent the last common ancestors of Archaea and of Bacteria. These enzymes display extreme thermal stabilities, suggesting thermophilic ancestries for Archaea and Bacteria. The results are robust to the uncertainties associated with the sequence predictions and to the tree topologies used to infer the ancestral sequences. Moreover, mutagenesis experiments suggest that the universal ancestor also possessed a very thermostable NDK. Because, as we show, the stability of an NDK is directly related to the environmental temperature of its host organism, our results indicate that the last common ancestor of extant life was a thermophile that flourished at a very high temperature.ancient protein | crystal structure | last universal common ancestor | molecular resurrection | phylogenetic analysis

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Stabilizing Proteins from Sequence Statistics: The Interplay of Conservation and Correlation in Triosephosphate Isomerase Stability

Cited by 98 publications

References 52 publications

Dissecting enzyme function with microfluidic-based deep mutational scanning

Dissecting enzyme function with microfluidic-based deep mutational scanning

Protein stability: computation, sequence statistics, and new experimental methods

Experimental evidence for the thermophilicity of ancestral life

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