Automated in Vivo Enzyme Engineering Accelerates Biocatalyst Optimization

Overview

Journal Nat Commun

Specialty Biology

Date 2024 Apr 24

PMID 38658554

Authors

Enrico Orsi

Lennart Schada von Borzyskowski

Stephan Noack

Pablo I Nikel

Steffen N Lindner

Affiliations

Soon will be listed here.

Abstract

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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References

Sandberg T, Salazar M, Weng L, Palsson B, Feist A . The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology. Metab Eng. 2019; 56:1-16. PMC: 6944292. DOI: 10.1016/j.ymben.2019.08.004. View

Eng T, Banerjee D, Menasalvas J, Chen Y, Gin J, Choudhary H . Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering. Cell Rep. 2023; 42(9):113087. DOI: 10.1016/j.celrep.2023.113087. View

Esvelt K, Carlson J, Liu D . A system for the continuous directed evolution of biomolecules. Nature. 2011; 472(7344):499-503. PMC: 3084352. DOI: 10.1038/nature09929. View

Helleckes L, Puchta D, Czech H, Morschett H, Geinitz B, Wiechert W . From frozen cell bank to product assay: high-throughput strain characterisation for autonomous Design-Build-Test-Learn cycles. Microb Cell Fact. 2023; 22(1):130. PMC: 10349472. DOI: 10.1186/s12934-023-02140-z. View

Molina R, Rix G, Mengiste A, Alvarez B, Seo D, Chen H . In vivo hypermutation and continuous evolution. Nat Rev Methods Primers. 2023; 2. PMC: 10108624. DOI: 10.1038/s43586-022-00130-w. View

Nava A, Fear A, Lee N, Mellinger P, Lan G, McCauley J . Automated Platform for the Plasmid Construction Process. ACS Synth Biol. 2023; 12(12):3506-3513. PMC: 10729297. DOI: 10.1021/acssynbio.3c00292. View

Blazquez B, Leon D, Torres-Bacete J, Gomez-Luengo A, Kniewel R, Martinez I . Golden Standard: a complete standard, portable, and interoperative MoClo tool for model and non-model proteobacteria. Nucleic Acids Res. 2023; 51(19):e98. PMC: 10602866. DOI: 10.1093/nar/gkad758. View

Malci K, Watts E, Roberts T, Auxillos J, Nowrouzi B, Oss Boll H . Standardization of Synthetic Biology Tools and Assembly Methods for and Emerging Yeast Species. ACS Synth Biol. 2022; 11(8):2527-2547. PMC: 9396660. DOI: 10.1021/acssynbio.1c00442. View

Watson J, Juergens D, Bennett N, Trippe B, Yim J, Eisenach H . De novo design of protein structure and function with RFdiffusion. Nature. 2023; 620(7976):1089-1100. PMC: 10468394. DOI: 10.1038/s41586-023-06415-8. View

10.

Volk M, Tran V, Tan S, Mishra S, Fatma Z, Boob A . Metabolic Engineering: Methodologies and Applications. Chem Rev. 2022; 123(9):5521-5570. DOI: 10.1021/acs.chemrev.2c00403. View

11.

Moore C, Papa 3rd L, Shoulders M . A Processive Protein Chimera Introduces Mutations across Defined DNA Regions In Vivo. J Am Chem Soc. 2018; 140(37):11560-11564. PMC: 6166643. DOI: 10.1021/jacs.8b04001. View

12.

Buerger J, Gronenberg L, Genee H, Sommer M . Wiring cell growth to product formation. Curr Opin Biotechnol. 2019; 59:85-92. DOI: 10.1016/j.copbio.2019.02.014. View

13.

Wu Y, Jameel A, Xing X, Zhang C . Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution. Trends Biotechnol. 2021; 40(1):38-59. DOI: 10.1016/j.tibtech.2021.04.002. View

14.

Yu T, Boob A, Singh N, Su Y, Zhao H . In vitro continuous protein evolution empowered by machine learning and automation. Cell Syst. 2023; 14(8):633-644. DOI: 10.1016/j.cels.2023.04.006. View

15.

Chica R, Doucet N, Pelletier J . Semi-rational approaches to engineering enzyme activity: combining the benefits of directed evolution and rational design. Curr Opin Biotechnol. 2005; 16(4):378-84. DOI: 10.1016/j.copbio.2005.06.004. View

16.

Nezhad N, Rahman R, Normi Y, Oslan S, Mohd Shariff F, Leow T . Thermostability engineering of industrial enzymes through structure modification. Appl Microbiol Biotechnol. 2022; 106(13-16):4845-4866. DOI: 10.1007/s00253-022-12067-x. View

17.

Tellechea-Luzardo J, Otero-Muras I, Goni-Moreno A, Carbonell P . Fast biofoundries: coping with the challenges of biomanufacturing. Trends Biotechnol. 2022; 40(7):831-842. DOI: 10.1016/j.tibtech.2021.12.006. View

18.

Zelle R, Harrison J, Pronk J, van Maris A . Anaplerotic role for cytosolic malic enzyme in engineered Saccharomyces cerevisiae strains. Appl Environ Microbiol. 2010; 77(3):732-8. PMC: 3028732. DOI: 10.1128/AEM.02132-10. View

19.

Volke D, Orsi E, Nikel P . Emergent CRISPR-Cas-based technologies for engineering non-model bacteria. Curr Opin Microbiol. 2023; 75:102353. DOI: 10.1016/j.mib.2023.102353. View

20.

Pitts K, Dobbin P, Reyes-Ramirez F, Thomson A, Richardson D, Seward H . Characterization of the Shewanella oneidensis MR-1 decaheme cytochrome MtrA: expression in Escherichia coli confers the ability to reduce soluble Fe(III) chelates. J Biol Chem. 2003; 278(30):27758-65. DOI: 10.1074/jbc.M302582200. View