Growth-coupled high throughput selection for directed enzyme evolution

Li, Zhengqun; Deng, Yuting; Yang, Guang-Yu

doi:10.1016/j.biotechadv.2023.108238

Cited by 9 publications

(5 citation statements)

References 99 publications

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“…In principle, growth-based in vivo selectionsalso referred to as growth-coupled or life-or-death selectionshave the potential to meet all these criteria. − First, they can rapidly assess all library members of vast populations simultaneously by applying a selective pressure that effectively eliminates undesired enzyme variants (Figure C). Moreover, growth-based selections are cheap and straightforward to implement in the laboratory, as identifying improved biocatalysts typically involves either plating populations on selective media or serially (or continuously) culturing populations under selective conditions.…”

Section: Introductionmentioning

confidence: 99%

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Jansen,

Mayer

2024

JACS Au

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Growth-based selections evaluate the fitness of individual organisms at a population level. In enzyme engineering, such growth selections allow for the rapid and straightforward identification of highly efficient biocatalysts from extensive libraries. However, selection-based improvement of (synthetically useful) biocatalysts is challenging, as they require highly dependable strategies that artificially link their activities to host survival. Here, we showcase a robust and scalable growth-based selection platform centered around the complementation of noncanonical amino acid-dependent bacteria. Specifically, we demonstrate how serial passaging of populations featuring millions of carbamoylase variants autonomously selects biocatalysts with up to 90,000-fold higher initial rates. Notably, selection of replicate populations enriched diverse biocatalysts, which feature distinct amino acid motifs that drastically boost carbamoylase activity. As beneficial substitutions also originated from unintended copying errors during library preparation or cell division, we anticipate that our growth-based selection platform will be applicable to the continuous, autonomous evolution of diverse biocatalysts in the future.

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Section: Introductionmentioning

confidence: 99%

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Jansen,

Mayer

2024

JACS Au

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“…In recent years, in vivo-directed evolution approaches have emerged as promising tools to use in protein engineering campaigns 11 , 16 . The use of these approaches combined with growth-coupled selection (meaning coupling the enzymatic activity of interest to microbial fitness) has been applied for different optimization strategies 17 , 18 . At the same time, automated biofoundries are becoming pivotal in supporting high-throughput efforts for engineering biology 19 – 22 .…”

Section: Introductionmentioning

confidence: 99%

“…This feature exhibits the advantage of creating different intensities of selective pressure, which can be exploited for screening purposes. In other words, selection strains are convenient platforms to explore for enzyme evolution purposes 17 , 18 , and their throughput is limited only by the transformation efficiency 66 , 67 .…”

Section: Introductionmentioning

confidence: 99%

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Automated in vivo enzyme engineering accelerates biocatalyst optimization

Orsi,

Schada von Borzyskowski,

Noack

et al. 2024

Nat Commun

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Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

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“…The combination of a largersized and higher-quality library enables more efficient exploration of the protein space, particularly when the exploration is not constrained by screening/selection capacity. This review deliberately omits the discussion of screening or selection as it has been extensively covered in our textbook [5] and several recent, excellent reviews [66][67][68][69][70][71]. Readers are encouraged to consult these references for a comprehensive understanding of this aspect.…”

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confidence: 99%

Crafting Genetic Diversity: Unlocking the Potential of Protein Evolution

Gali,

Tee,

Wong

2024

SynBio

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Genetic diversity is the foundation of evolutionary resilience, adaptive potential, and the flourishing vitality of living organisms, serving as the cornerstone for robust ecosystems and the continuous evolution of life on Earth. The landscape of directed evolution, a powerful biotechnological tool inspired by natural evolutionary processes, has undergone a transformative shift propelled by innovative strategies for generating genetic diversity. This shift is fuelled by several factors, encompassing the utilization of advanced toolkits like CRISPR-Cas and base editors, the enhanced comprehension of biological mechanisms, cost-effective custom oligo pool synthesis, and the seamless integration of artificial intelligence and automation. This comprehensive review looks into the myriad of methodologies employed for constructing gene libraries, both in vitro and in vivo, categorized into three major classes: random mutagenesis, focused mutagenesis, and DNA recombination. The objectives of this review are threefold: firstly, to present a panoramic overview of recent advances in genetic diversity creation; secondly, to inspire novel ideas for further innovation in genetic diversity generation; and thirdly, to provide a valuable resource for individuals entering the field of directed evolution.

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Growth-coupled high throughput selection for directed enzyme evolution

Cited by 9 publications

References 99 publications

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Crafting Genetic Diversity: Unlocking the Potential of Protein Evolution

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