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Inducible directed evolution of complex phenotypes in bacteria.

ABSTRACT: Directed evolution is a powerful method for engineering biology in the absence of detailed sequence-function relationships. To enable directed evolution of complex phenotypes encoded by multigene pathways, we require large library sizes for DNA sequences >5-10 kb in length, elimination of genomic hitchhiker mutations, and decoupling of diversification and screening steps. To meet these challenges, we developed Inducible Directed Evolution (IDE), which uses a temperate bacteriophage to package large plasmids and transfer them to naive cells after intracellular mutagenesis. To demonstrate IDE, we evolved a 5-gene pathway from Bacillus licheniformis that accelerates tagatose catabolism in Escherichia coli, resulting in clones with 65% shorter lag times during growth on tagatose after only two rounds of evolution. Next, we evolved a 15.4 kb, 10-gene pathway from Bifidobacterium breve UC2003 that aids E. coli's utilization of melezitose. After three rounds of IDE, we isolated evolved pathways that both reduced lag time by more than 2-fold and enabled 150% higher final optical density. Taken together, this work enhances the capacity and utility of a whole pathway directed evolution approach in E. coli.

SUBMITTER: Al'Abri IS

PROVIDER: S-EPMC9177967 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Inducible directed evolution of complex phenotypes in bacteria.

Al'Abri Ibrahim S IS Haller Daniel J DJ Li Zidan Z Crook Nathan N

Nucleic acids research 20220601 10

Directed evolution is a powerful method for engineering biology in the absence of detailed sequence-function relationships. To enable directed evolution of complex phenotypes encoded by multigene pathways, we require large library sizes for DNA sequences >5-10 kb in length, elimination of genomic hitchhiker mutations, and decoupling of diversification and screening steps. To meet these challenges, we developed Inducible Directed Evolution (IDE), which uses a temperate bacteriophage to package la ...[more]

PMID: 35150576

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Project description:Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Dataset Information

Inducible directed evolution of complex phenotypes in bacteria.

Publications

Inducible directed evolution of complex phenotypes in bacteria.

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