Large-scale design and refinement of stable proteins using sequence-only models

Singer, Jedediah M.; Novotney, Scott; Strickland, Devin; Haddox, Hugh K.; Leiby, Nicholas; Rocklin, Gabriel J.; Chow, Cameron M.; Bera, Asim K.; Motta, Francis C.; Cao, Longxing; Strauch, Eva-Maria; Chidyausiku, Tamuka M.; Ford, Alex T.; Ho, Ethan; Zaitzeff, Alexander; Mackenzie, Craig O.; Eramian, Hamed; DiMaio, Frank; Grigoryan, Gevorg; Vaughn, Matthew; Stewart, Lance; Baker, David; Klavins, Eric

doi:10.1371/journal.pone.0265020

Cited by 20 publications

(18 citation statements)

References 76 publications

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“…Proteases typically cleave unfolded proteins more quickly than folded ones, and proteolysis assays have been used for decades to measure folding stability (37) and select for high stability proteins (38,39). In 2017, we introduced the high-throughput yeast display proteolysis method for measuring folding stability using next generation sequencing (29,(40)(41)(42)(43)(44)(45)(46). To improve the scale, precision, speed, and cost of stability measurements, we developed cDNA display proteolysis.…”

Section: Massively Parallel Measurement Of Folding Stability By Cdna ...mentioning

confidence: 99%

Mega-scale experimental analysis of protein folding stability in biology and protein design

Tsuboyama

Dauparas

Chen

et al. 2022

Preprint

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Advances in DNA sequencing and machine learning are illuminating protein sequences and structures on an enormous scale. However, the energetics driving folding are invisible in these structures and remain largely unknown. The hidden thermodynamics of folding can drive disease, shape protein evolution, and guide protein engineering, and new approaches are needed to reveal these thermodynamics for every sequence and structure. We present cDNA display proteolysis, a new method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of ~850,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 354 natural and 188 de novo designed protein domains 40-72 amino acids in length. Using this immense dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate, and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.

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Section: Massively Parallel Measurement Of Folding Stability By Cdna ...mentioning

confidence: 99%

Mega-scale experimental analysis of protein folding stability in biology and protein design

Tsuboyama

Dauparas

Chen

et al. 2022

Preprint

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“…Despite these limitations, our study demonstrates how large-scale experimental testing can be applied to solve a challenging design problem and to quantify the biophysical features that influence design stability. In contrast to other studies that use mutagenesis to study determinants of folding stability (19,(42)(43)(44), our method examines the strengths of different biophysical features across thousands of different protein contexts, although these contexts are all related by the αββα fold and design procedure. Simplified low-resolution models like our linear regression are valuable for building biophysical intuition about the strengths of different interactions (45,46) as well as for guiding the construction of high-resolution models like the Rosetta energy function, which is also an additive model (47).…”

Section: Discussionmentioning

confidence: 99%

“…In our approach, we designed thousands of de novo proteins and measured their folding stabilities using a yeast display-based proteolysis assay coupled to next-generation sequencing (1). Several new studies have applied our methodology (16)(17)(18)(19) as it has several advantages. First, measuring folding stability for thousands of proteins makes it possible to statistically quantify biophysical features that contribute to stability.…”

Section: Introductionmentioning

confidence: 99%

Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

Kim¹,

Tsuboyama²,

Houliston

et al. 2021

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Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet some protein folds are easier to design than others. Previous work identified the 43-residue αββ&#945 fold as especially challenging: the best designs had only a 2% success rate, compared to 39-87% success for other simple folds (1). This suggested the αββ&#945 fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over ten thousand new αββ&#945 proteins and found over three thousand of them to fold into stable structures using a high-throughput protease-based assay. Nuclear magnetic resonance, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed αββ&#945 structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the αββ&#945 topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.

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“…The stability score was calculated using the approach in Singer et al, 22 a refinement of the Rocklin et al approach. In brief, fluorescently tagged proteins are expressed on the surface of yeast cells.…”

Section: ■ Computational Methodsmentioning

confidence: 99%

The Role of Configurational Entropy in Miniprotein Stability

et al. 2021

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Predicting protein stability is a challenge due to the many competing thermodynamic effects. Through de novo protein design, one begins with a target structure and searches for a sequence that will fold into it. Previous work by Rocklin et al. introduced a data set of more than 16,000 miniproteins spanning four structural topologies with information on stability. These structures were characterized with a set of 46 structural descriptors, with no explicit inclusion of configurational entropy (S cnf). Our work focused on creating a set of 17 descriptors intended to capture variations in S cnf and its comparison to an extended set of 113 structural and energy model features that extend the Rocklin et al. feature set (R). The S cnf descriptors statistically discriminate between stable and unstable distributions within topologies and best describe EEHEE topology stability (where E = β sheet and H = α helix). Between 50 and 80% of the variation in each S cnf descriptor is described by linear combinations of R features. Despite containing useful information about minipeptide stability, providing S cnf features as inputs to machine learning models does not improve overall performance when predicting protein stability, as the R features sufficiently capture the implicit variations.

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Large-scale design and refinement of stable proteins using sequence-only models

Cited by 20 publications

References 76 publications

Mega-scale experimental analysis of protein folding stability in biology and protein design

Mega-scale experimental analysis of protein folding stability in biology and protein design

Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

The Role of Configurational Entropy in Miniprotein Stability

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