1
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Koch NG, Budisa N. Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion. Chem Rev 2024; 124:9580-9608. [PMID: 38953775 PMCID: PMC11363022 DOI: 10.1021/acs.chemrev.4c00031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 07/04/2024]
Abstract
Over 20 years ago, the pyrrolysine encoding translation system was discovered in specific archaea. Our Review provides an overview of how the once obscure pyrrolysyl-tRNA synthetase (PylRS) tRNA pair, originally responsible for accurately translating enzymes crucial in methanogenic metabolic pathways, laid the foundation for the burgeoning field of genetic code expansion. Our primary focus is the discussion of how to successfully engineer the PylRS to recognize new substrates and exhibit higher in vivo activity. We have compiled a comprehensive list of ncAAs incorporable with the PylRS system. Additionally, we also summarize recent successful applications of the PylRS system in creating innovative therapeutic solutions, such as new antibody-drug conjugates, advancements in vaccine modalities, and the potential production of new antimicrobials.
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Affiliation(s)
- Nikolaj G. Koch
- Department
of Chemistry, Institute of Physical Chemistry, University of Basel, 4058 Basel, Switzerland
- Department
of Biosystems Science and Engineering, ETH
Zurich, 4058 Basel, Switzerland
| | - Nediljko Budisa
- Biocatalysis
Group, Institute of Chemistry, Technische
Universität Berlin, 10623 Berlin, Germany
- Chemical
Synthetic Biology Chair, Department of Chemistry, University of Manitoba, Winnipeg MB R3T 2N2, Canada
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2
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Sun Y, Zhao Y, Xie X, Li H, Feng W. Printed polymer platform empowering machine-assisted chemical synthesis in stacked droplets. Nat Commun 2024; 15:6759. [PMID: 39117641 PMCID: PMC11310347 DOI: 10.1038/s41467-024-50768-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 07/19/2024] [Indexed: 08/10/2024] Open
Abstract
Efficiently exploring organic molecules through multi-step processes demands a transition from conventional laboratory synthesis to automated systems. Existing platforms for machine-assistant synthetic workflows compatible with multiple liquid-phases require substantial engineering investments for setup, thereby hindering quick customization and throughput increasement. Here we present a droplet-based chip that facilitates the self-organization of various liquid phases into stacked layers for conducting chemical transformations. The chip's precision polymer printing capability, enabled by digital micromirror device (DMD)-maskless photolithography and dual post-chemical modifications, allows it to create customized, sub-10 µm featured patterns to confine diverse liquids, regardless of density, within each droplet. The robustness and open design of surface-templated liquid layers actualize machine-assistant droplet manipulation, synchronous reaction triggering, local oscillation, and real-time monitoring of individual layers into a reality. We propose that, with further integration of machine operation line and self-learning, this droplet-based platform holds the potential to become a valuable addition to the toolkit of chemistry process, operating autonomously and with high-throughput.
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Affiliation(s)
- Yingxue Sun
- College of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Yuanyi Zhao
- College of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Xinjian Xie
- College of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Hongjiao Li
- College of Chemical Engineering, Sichuan University, Chengdu, China
| | - Wenqian Feng
- College of Polymer Science and Engineering, Sichuan University, Chengdu, China.
- Department State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, China.
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3
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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4
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Castle SD, Stock M, Gorochowski TE. Engineering is evolution: a perspective on design processes to engineer biology. Nat Commun 2024; 15:3640. [PMID: 38684714 PMCID: PMC11059173 DOI: 10.1038/s41467-024-48000-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.
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Affiliation(s)
- Simeon D Castle
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, UK.
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, UK.
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol, UK.
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5
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Deng H, Yu H, Deng Y, Qiu Y, Li F, Wang X, He J, Liang W, Lan Y, Qiao L, Zhang Z, Zhang Y, Keasling JD, Luo X. Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306935. [PMID: 38321783 PMCID: PMC11005738 DOI: 10.1002/advs.202306935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/24/2023] [Indexed: 02/08/2024]
Abstract
The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L-1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.
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Affiliation(s)
- Huaxiang Deng
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of BiotechnologyJiangnan UniversityWuxi214122P. R. China
| | - Han Yu
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
| | - Yanwu Deng
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Yulan Qiu
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Feifei Li
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Xinran Wang
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Jiahui He
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Weiyue Liang
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of BiotechnologyJiangnan UniversityWuxi214122P. R. China
| | - Yunquan Lan
- Shenzhen Infrastructure for Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Longjiang Qiao
- Shenzhen Infrastructure for Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Zhiyu Zhang
- Shenzhen Infrastructure for Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Yunfeng Zhang
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
| | - Jay D. Keasling
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Joint BioEnergy InstituteEmeryvilleCA94608USA
- Biological Systems and Engineering DivisionLawrence Berkeley National LaboratoryBerkeleyCA94720USA
- Department of Chemical and Biomolecular Engineering & Department of BioengineeringUniversity of CaliforniaBerkeleyCA94720USA
- Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKgs. Lyngby2800Denmark
| | - Xiaozhou Luo
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
- Shenzhen Infrastructure for Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055P. R. China
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6
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Mercer JAM, DeCarlo SJ, Roy Burman SS, Sreekanth V, Nelson AT, Hunkeler M, Chen PJ, Donovan KA, Kokkonda P, Tiwari PK, Shoba VM, Deb A, Choudhary A, Fischer ES, Liu DR. Continuous evolution of compact protein degradation tags regulated by selective molecular glues. Science 2024; 383:eadk4422. [PMID: 38484051 PMCID: PMC11203266 DOI: 10.1126/science.adk4422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/09/2024] [Indexed: 03/19/2024]
Abstract
Conditional protein degradation tags (degrons) are usually >100 amino acids long or are triggered by small molecules with substantial off-target effects, thwarting their use as specific modulators of endogenous protein levels. We developed a phage-assisted continuous evolution platform for molecular glue complexes (MG-PACE) and evolved a 36-amino acid zinc finger (ZF) degron (SD40) that binds the ubiquitin ligase substrate receptor cereblon in complex with PT-179, an orthogonal thalidomide derivative. Endogenous proteins tagged in-frame with SD40 using prime editing are degraded by otherwise inert PT-179. Cryo-electron microscopy structures of SD40 in complex with ligand-bound cereblon revealed mechanistic insights into the molecular basis of SD40's activity and specificity. Our efforts establish a system for continuous evolution of molecular glue complexes and provide ZF tags that overcome shortcomings associated with existing degrons.
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Affiliation(s)
- Jaron A. M. Mercer
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138
| | - Stephan J. DeCarlo
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138
| | - Shourya S. Roy Burman
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115
| | - Vedagopuram Sreekanth
- Chemical Biology and Therapeutics Science, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Medicine, Harvard Medical School, Boston, MA 02115
- Divisions of Renal Medicine and Engineering, Brigham and Women’s Hospital, Boston, MA 02115
| | - Andrew T. Nelson
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138
| | - Moritz Hunkeler
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115
| | - Peter J. Chen
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138
| | - Katherine A. Donovan
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115
| | - Praveen Kokkonda
- Chemical Biology and Therapeutics Science, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Medicine, Harvard Medical School, Boston, MA 02115
| | - Praveen K. Tiwari
- Chemical Biology and Therapeutics Science, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Medicine, Harvard Medical School, Boston, MA 02115
- Divisions of Renal Medicine and Engineering, Brigham and Women’s Hospital, Boston, MA 02115
| | - Veronika M. Shoba
- Chemical Biology and Therapeutics Science, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Medicine, Harvard Medical School, Boston, MA 02115
| | - Arghya Deb
- Chemical Biology and Therapeutics Science, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Medicine, Harvard Medical School, Boston, MA 02115
| | - Amit Choudhary
- Chemical Biology and Therapeutics Science, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Medicine, Harvard Medical School, Boston, MA 02115
- Divisions of Renal Medicine and Engineering, Brigham and Women’s Hospital, Boston, MA 02115
| | - Eric S. Fischer
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115
| | - David R. Liu
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138
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7
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Schmidheini L, Mathis N, Marquart KF, Rothgangl T, Kissling L, Böck D, Chanez C, Wang JP, Jinek M, Schwank G. Continuous directed evolution of a compact CjCas9 variant with broad PAM compatibility. Nat Chem Biol 2024; 20:333-343. [PMID: 37735239 PMCID: PMC7616171 DOI: 10.1038/s41589-023-01427-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 08/23/2023] [Indexed: 09/23/2023]
Abstract
CRISPR-Cas9 genome engineering is a powerful technology for correcting genetic diseases. However, the targeting range of Cas9 proteins is limited by their requirement for a protospacer adjacent motif (PAM), and in vivo delivery is challenging due to their large size. Here, we use phage-assisted continuous directed evolution to broaden the PAM compatibility of Campylobacter jejuni Cas9 (CjCas9), the smallest Cas9 ortholog characterized to date. The identified variant, termed evoCjCas9, primarily recognizes N4AH and N5HA PAM sequences, which occur tenfold more frequently in the genome than the canonical N3VRYAC PAM site. Moreover, evoCjCas9 exhibits higher nuclease activity than wild-type CjCas9 on canonical PAMs, with editing rates comparable to commonly used PAM-relaxed SpCas9 variants. Combined with deaminases or reverse transcriptases, evoCjCas9 enables robust base and prime editing, with the small size of evoCjCas9 base editors allowing for tissue-specific installation of A-to-G or C-to-T transition mutations from single adeno-associated virus vector systems.
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Affiliation(s)
- Lukas Schmidheini
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
- Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Nicolas Mathis
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Kim Fabiano Marquart
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
- Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Tanja Rothgangl
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Lucas Kissling
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Desirée Böck
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Christelle Chanez
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
| | | | - Martin Jinek
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
| | - Gerald Schwank
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
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8
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Mock M, Langmead CJ, Grandsard P, Edavettal S, Russell A. Recent advances in generative biology for biotherapeutic discovery. Trends Pharmacol Sci 2024; 45:255-267. [PMID: 38378385 DOI: 10.1016/j.tips.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/22/2023] [Accepted: 01/05/2024] [Indexed: 02/22/2024]
Abstract
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.
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Affiliation(s)
- Marissa Mock
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | | | - Peter Grandsard
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Suzanne Edavettal
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Alan Russell
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
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9
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Gionfriddo M, Rhodes T, Whitney SM. Perspectives on improving crop Rubisco by directed evolution. Semin Cell Dev Biol 2024; 155:37-47. [PMID: 37085353 DOI: 10.1016/j.semcdb.2023.04.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/14/2023] [Accepted: 04/15/2023] [Indexed: 04/23/2023]
Abstract
Rubisco catalyses the entry of almost all CO2 into the biosphere and is often the rate-limiting step in plant photosynthesis and growth. Its notoriety as the most abundant protein on Earth stems from the slow and error-prone catalytic properties that require plants, cyanobacteria, algae and photosynthetic bacteria to produce it in high amounts. Efforts to improve the CO2-fixing properties of plant Rubisco has been spurred on by the discovery of more effective isoforms in some algae with the potential to significantly improve crop productivity. Incompatibilities between the protein folding machinery of leaf and algae chloroplasts have, so far, prevented efforts to transplant these more effective Rubisco variants into plants. There is therefore increasing interest in improving Rubisco catalysis by directed (laboratory) evolution. Here we review the advances being made in, and the ongoing challenges with, improving the solubility and/or carboxylation activity of differing non-plant Rubisco lineages. We provide perspectives on new opportunities for the directed evolution of crop Rubiscos and the existing plant transformation capabilities available to evaluate the extent to which Rubisco activity improvements can benefit agricultural productivity.
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Affiliation(s)
- Matteo Gionfriddo
- Plant Science Division, Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia; Department of Cellular Biochemistry, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
| | - Timothy Rhodes
- Plant Science Division, Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia
| | - Spencer M Whitney
- Plant Science Division, Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia.
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10
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Yang J, Li FZ, Arnold FH. Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering. ACS CENTRAL SCIENCE 2024; 10:226-241. [PMID: 38435522 PMCID: PMC10906252 DOI: 10.1021/acscentsci.3c01275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/26/2023] [Accepted: 01/16/2024] [Indexed: 03/05/2024]
Abstract
Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.
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Affiliation(s)
- Jason Yang
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Francesca-Zhoufan Li
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Frances H. Arnold
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
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11
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Zhang S, Ma Z, Li W, Shen Y, Xu Y, Liu G, Chang J, Li Z, Qin H, Tian B, Gong H, Liu D, Thuronyi B, Voigt C. EvoAI enables extreme compression and reconstruction of the protein sequence space. RESEARCH SQUARE 2024:rs.3.rs-3930833. [PMID: 38464127 PMCID: PMC10925456 DOI: 10.21203/rs.3.rs-3930833/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here, we first establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.
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12
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Ma P, Zhang S, Huang Q, Gu Y, Zhou Z, Hou W, Yi W, Xu H. Evolution of chemistry and selection technology for DNA-encoded library. Acta Pharm Sin B 2024; 14:492-516. [PMID: 38322331 PMCID: PMC10840438 DOI: 10.1016/j.apsb.2023.10.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/11/2023] [Accepted: 09/21/2023] [Indexed: 02/08/2024] Open
Abstract
DNA-encoded chemical library (DEL) links the power of amplifiable genetics and the non-self-replicating chemical phenotypes, generating a diverse chemical world. In analogy with the biological world, the DEL world can evolve by using a chemical central dogma, wherein DNA replicates using the PCR reactions to amplify the genetic codes, DNA sequencing transcripts the genetic information, and DNA-compatible synthesis translates into chemical phenotypes. Importantly, DNA-compatible synthesis is the key to expanding the DEL chemical space. Besides, the evolution-driven selection system pushes the chemicals to evolve under the selective pressure, i.e., desired selection strategies. In this perspective, we summarized recent advances in expanding DEL synthetic toolbox and panning strategies, which will shed light on the drug discovery harnessing in vitro evolution of chemicals via DEL.
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Affiliation(s)
- Peixiang Ma
- Shanghai Key Laboratory of Orthopedic Implants, Department of Orthopedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Shuning Zhang
- Shanghai Key Laboratory of Orthopedic Implants, Department of Orthopedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai 201210, China
| | - Qianping Huang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai 201210, China
| | - Yuang Gu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai 201210, China
| | - Zhi Zhou
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, The NMPA and State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou 511436, China
| | - Wei Hou
- College of Pharmaceutical Science and Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Hangzhou 310014, China
| | - Wei Yi
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, The NMPA and State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou 511436, China
| | - Hongtao Xu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai 201210, China
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13
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Jewel D, Pham Q, Chatterjee A. Virus-assisted directed evolution of biomolecules. Curr Opin Chem Biol 2023; 76:102375. [PMID: 37542745 PMCID: PMC10870257 DOI: 10.1016/j.cbpa.2023.102375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/01/2023] [Accepted: 07/02/2023] [Indexed: 08/07/2023]
Abstract
Directed evolution is a powerful technique that uses principles of natural evolution to enable the development of biomolecules with novel functions. However, the slow pace of natural evolution does not support the demand for rapidly generating new biomolecular functions in the laboratory. Viruses offer a unique path to design fast laboratory evolution experiments, owing to their innate ability to evolve much more rapidly than most living organisms, facilitated by a smaller genome size that tolerate a high frequency of mutations, as well as a fast rate of replication. These attributes offer a great opportunity to evolve various biomolecules by linking their activity to the replication of a suitable virus. This review highlights the recent advances in the application of virus-assisted directed evolution of designer biomolecules in both prokaryotic and eukaryotic cells.
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Affiliation(s)
- Delilah Jewel
- Department of Chemistry, Boston College, 2609 Beacon Street, Chestnut Hill, MA 02467, USA
| | - Quan Pham
- Department of Chemistry, Boston College, 2609 Beacon Street, Chestnut Hill, MA 02467, USA
| | - Abhishek Chatterjee
- Department of Chemistry, Boston College, 2609 Beacon Street, Chestnut Hill, MA 02467, USA.
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14
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Wierenga RP, Golas S, Ho W, Coley C, Esvelt KM. PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.547733. [PMID: 37502883 PMCID: PMC10369895 DOI: 10.1101/2023.07.10.547733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Liquid handling robots are often limited by proprietary programming interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides a universal set of commands and representations for deck layout and labware, enabling the control of diverse accessory devices. The interface is extensible and can work with any robot that manipulates liquids within a Cartesian coordinate system. We validated the system through unit tests and several application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation.
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Affiliation(s)
- Rick P. Wierenga
- Leiden University, Leiden, the Netherlands
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stefan Golas
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Wilson Ho
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Connor Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kevin M. Esvelt
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
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15
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Chen JP, Gong JS, Su C, Li H, Xu ZH, Shi JS. Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies. Metab Eng 2023; 78:99-114. [PMID: 37244368 DOI: 10.1016/j.ymben.2023.05.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023]
Abstract
Solubility and folding stability are key concerns for difficult-to-express proteins (DEPs) restricted by amino acid sequences and superarchitecture, resolved by the precise distribution of amino acids and molecular interactions as well as the assistance of the expression system. Therefore, an increasing number of tools are available to achieve efficient expression of DEPs, including directed evolution, solubilization partners, chaperones, and affluent expression hosts, among others. Furthermore, genome editing tools, such as transposons and CRISPR Cas9/dCas9, have been developed and expanded to construct engineered expression hosts capable of efficient expression ability of soluble proteins. Accounting for the accumulated knowledge of the pivotal factors in the solubility and folding stability of proteins, this review focuses on advanced technologies and tools of protein engineering, protein quality control systems, and the redesign of expression platforms in prokaryotic expression systems, as well as advances of the cell-free expression technologies for membrane proteins production.
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Affiliation(s)
- Jin-Ping Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Gong
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China.
| | - Chang Su
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Heng Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Zheng-Hong Xu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, School of Biotechnology, Jiangnan University, Wuxi, 214122, PR China; Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Shi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
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16
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English MA, Alcantar MA, Collins JJ. A self‐propagating, barcoded transposon system for the dynamic rewiring of genomic networks. Mol Syst Biol 2023:e11398. [DOI: 10.15252/msb.202211398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023] Open
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17
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Gong X, Zhang H, Shen Y, Fu X. Update of the Pyrrolysyl-tRNA Synthetase/tRNA Pyl Pair and Derivatives for Genetic Code Expansion. J Bacteriol 2023; 205:e0038522. [PMID: 36695595 PMCID: PMC9945579 DOI: 10.1128/jb.00385-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
The cotranslational incorporation of pyrrolysine (Pyl), the 22nd proteinogenic amino acid, into proteins in response to the UAG stop codon represents an outstanding example of natural genetic code expansion. Genetic encoding of Pyl is conducted by the pyrrolysyl-tRNA synthetase (PylRS) and its cognate tRNA, tRNAPyl. Owing to the high tolerance of PylRS toward diverse amino acid substrates and great orthogonality in various model organisms, the PylRS/tRNAPyl-derived pairs are ideal for genetic code expansion to insert noncanonical amino acids (ncAAs) into proteins of interest. Since the discovery of cellular components involved in the biosynthesis and genetic encoding of Pyl, synthetic biologists have been enthusiastic about engineering PylRS/tRNAPyl-derived pairs to rewrite the genetic code of living cells. Recently, considerable progress has been made in understanding the molecular phylogeny, biochemical properties, and structural features of the PylRS/tRNAPyl pair, guiding its further engineering and optimization. In this review, we cover the basic and updated knowledge of the PylRS/tRNAPyl pair's unique characteristics that make it an outstanding tool for reprogramming the genetic code. In addition, we summarize the recent efforts to create efficient and (mutually) orthogonal PylRS/tRNAPyl-derived pairs for incorporation of diverse ncAAs by genome mining, rational design, and advanced directed evolution methods.
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Affiliation(s)
- Xuemei Gong
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI Research-Shenzhen, BGI, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
| | - Haolin Zhang
- BGI Research-Shenzhen, BGI, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
| | - Yue Shen
- BGI Research-Shenzhen, BGI, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
- BGI Research-Changzhou, BGI, Changzhou, China
| | - Xian Fu
- BGI Research-Shenzhen, BGI, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
- BGI Research-Changzhou, BGI, Changzhou, China
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18
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Huang TP, Heins ZJ, Miller SM, Wong BG, Balivada PA, Wang T, Khalil AS, Liu DR. High-throughput continuous evolution of compact Cas9 variants targeting single-nucleotide-pyrimidine PAMs. Nat Biotechnol 2023; 41:96-107. [PMID: 36076084 PMCID: PMC9849140 DOI: 10.1038/s41587-022-01410-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/30/2022] [Indexed: 01/25/2023]
Abstract
Despite the availability of Cas9 variants with varied protospacer-adjacent motif (PAM) compatibilities, some genomic loci-especially those with pyrimidine-rich PAM sequences-remain inaccessible by high-activity Cas9 proteins. Moreover, broadening PAM sequence compatibility through engineering can increase off-target activity. With directed evolution, we generated four Cas9 variants that together enable targeting of most pyrimidine-rich PAM sequences in the human genome. Using phage-assisted noncontinuous evolution and eVOLVER-supported phage-assisted continuous evolution, we evolved Nme2Cas9, a compact Cas9 variant, into variants that recognize single-nucleotide pyrimidine-PAM sequences. We developed a general selection strategy that requires functional editing with fully specified target protospacers and PAMs. We applied this selection to evolve high-activity variants eNme2-T.1, eNme2-T.2, eNme2-C and eNme2-C.NR. Variants eNme2-T.1 and eNme2-T.2 offer access to N4TN PAM sequences with comparable editing efficiencies as existing variants, while eNme2-C and eNme2-C.NR offer less restrictive PAM requirements, comparable or higher activity in a variety of human cell types and lower off-target activity at N4CN PAM sequences.
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Affiliation(s)
- Tony P Huang
- Merkin Institute of Transformative Technologies in Healthcare, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Zachary J Heins
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Shannon M Miller
- Merkin Institute of Transformative Technologies in Healthcare, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Brandon G Wong
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Pallavi A Balivada
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Tina Wang
- Merkin Institute of Transformative Technologies in Healthcare, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ahmad S Khalil
- Biological Design Center, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
| | - David R Liu
- Merkin Institute of Transformative Technologies in Healthcare, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA.
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19
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Engineering Ag43 Signal Peptides with Bacterial Display and Selection. Methods Protoc 2022; 6:mps6010001. [PMID: 36648950 PMCID: PMC9844295 DOI: 10.3390/mps6010001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Protein display, secretion, and export in prokaryotes are essential for utilizing microbial systems as engineered living materials, medicines, biocatalysts, and protein factories. To select for improved signal peptides for Escherichia coli protein display, we utilized error-prone polymerase chain reaction (epPCR) coupled with single-cell sorting and microplate titer to generate, select, and detect improved Ag43 signal peptides. Through just three rounds of mutagenesis and selection using green fluorescence from the 56 kDa sfGFP-beta-lactamase, we isolated clones that modestly increased surface display from 1.4- to 3-fold as detected by the microplate plate-reader and native SDS-PAGE assays. To establish that the functional protein was displayed extracellularly, we trypsinized the bacterial cells to release the surface displayed proteins for analysis. This workflow demonstrated a fast and high-throughput method leveraging epPCR and single-cell sorting to augment bacterial surface display rapidly that could be applied to other bacterial proteins.
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20
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Characterization of the Binding Behavior of Specific Cobalt and Nickel Ion-Binding Peptides Identified by Phage Surface Display. SEPARATIONS 2022. [DOI: 10.3390/separations9110354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
In recent years, the application focus of phage surface display (PSD) technology has been extended to the identification of metal ion-selective peptides. In previous studies, two phage clones—a nickel-binding one with the peptide motif CNAKHHPRCGGG and a cobalt-binding one with the peptide motif CTQMLGQLCGGG—were isolated, and their binding ability to metal-loaded NTA agarose beads was investigated. Here, the free cyclic peptides are characterized by UV/VIS spectroscopy with respect to their binding capacity for the respective target ion and in crossover experiments for the other ion by isothermal titration calorimetry (ITC) in different buffer systems. This revealed differences in selectivity and affinity. The cobalt-specific peptide is very sensitive to different buffers; it has a 20-fold higher affinity for cobalt and nickel under suitable conditions. The nickel-specific peptide binds more moderately and robustly in different buffers but only selectively to nickel.
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21
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Wei T, Lai W, Chen Q, Zhang Y, Sun C, He X, Zhao G, Fu X, Liu C. Exploiting spatial dimensions to enable parallelized continuous directed evolution. Mol Syst Biol 2022; 18:e10934. [PMID: 36129229 PMCID: PMC9491160 DOI: 10.15252/msb.202210934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 12/03/2022] Open
Abstract
Current strategies to improve the throughput of continuous directed evolution technologies often involve complex mechanical fluid-controlling system or robotic platforms, which limits their popularization and application in general laboratories. Inspired by our previous study on bacterial range expansion, in this study, we report a system termed SPACE for rapid and extensively parallelizable evolution of biomolecules by introducing spatial dimensions into the landmark phage-assisted continuous evolution system. Specifically, M13 phages and chemotactic Escherichia coli cells were closely inoculated onto a semisolid agar. The phages came into contact with the expanding front of the bacterial range, and then comigrated with the bacteria. This system leverages competition over space, wherein evolutionary progress is closely associated with the production of spatial patterns, allowing the emergence of improved or new protein functions. In a prototypical problem, SPACE remarkably simplified the process and evolved the promoter recognition of T7 RNA polymerase (RNAP) to a library of 96 random sequences in parallel. These results establish SPACE as a simple, easy to implement, and massively parallelizable platform for continuous directed evolution in general laboratories.
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Affiliation(s)
- Ting Wei
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
| | - Wangsheng Lai
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
| | - Qian Chen
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
- University of Chinese Academy of SciencesBeijingChina
| | - Yi Zhang
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
| | - Chenjian Sun
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xionglei He
- State Key Laboratory of Biocontrol, School of Life SciencesSun Yat‐Sen UniversityGuangzhouChina
| | - Guoping Zhao
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
- CAS Key Laboratory for Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Xiongfei Fu
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
- University of Chinese Academy of SciencesBeijingChina
| | - Chenli Liu
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
- University of Chinese Academy of SciencesBeijingChina
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22
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Allen GL, Grahn AK, Kourentzi K, Willson RC, Waldrop S, Guo J, Kay BK. Expanding the chemical diversity of M13 bacteriophage. Front Microbiol 2022; 13:961093. [PMID: 36003937 PMCID: PMC9393631 DOI: 10.3389/fmicb.2022.961093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/04/2022] [Indexed: 11/21/2022] Open
Abstract
Bacteriophage M13 virions are very stable nanoparticles that can be modified by chemical and genetic methods. The capsid proteins can be functionalized in a variety of chemical reactions without loss of particle integrity. In addition, Genetic Code Expansion (GCE) permits the introduction of non-canonical amino acids (ncAAs) into displayed peptides and proteins. The incorporation of ncAAs into phage libraries has led to the discovery of high-affinity binders with low nanomolar dissociation constant (K D) values that can potentially serve as inhibitors. This article reviews how bioconjugation and the incorporation of ncAAs during translation have expanded the chemistry of peptides and proteins displayed by M13 virions for a variety of purposes.
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Affiliation(s)
| | | | - Katerina Kourentzi
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, United States
| | - Richard C. Willson
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, United States
| | - Sean Waldrop
- Department of Chemistry, University of Nebraska at Lincoln, Lincoln, NE, United States
| | - Jiantao Guo
- Department of Chemistry, University of Nebraska at Lincoln, Lincoln, NE, United States
| | - Brian K. Kay
- Tango Biosciences, Inc., Chicago, IL, United States
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23
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Lalejini A, Dolson E, Vostinar AE, Zaman L. Artificial selection methods from evolutionary computing show promise for directed evolution of microbes. eLife 2022; 11:e79665. [PMID: 35916365 PMCID: PMC9444240 DOI: 10.7554/elife.79665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms-procedures for choosing promising progenitors-from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.
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Affiliation(s)
- Alexander Lalejini
- Department of Ecology and Evolutionary Biology, University of MichiganAnn ArborUnited States
- Center for the Study of Complex Systems, University of MichiganAnn ArborUnited States
| | - Emily Dolson
- Department of Computer Science and Engineering, Michigan State UniversityEast LansingUnited States
- Program in Ecology, Evolution, and Behavior, Michigan State UniversityEast LansingUnited States
| | - Anya E Vostinar
- Computer Science Department, Carleton CollegeNorthfieldUnited States
| | - Luis Zaman
- Department of Ecology and Evolutionary Biology, University of MichiganAnn ArborUnited States
- Center for the Study of Complex Systems, University of MichiganAnn ArborUnited States
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24
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Jagdish T, Nguyen Ba AN. Microbial experimental evolution in a massively multiplexed and high-throughput era. Curr Opin Genet Dev 2022; 75:101943. [PMID: 35752001 DOI: 10.1016/j.gde.2022.101943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022]
Abstract
Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.
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Affiliation(s)
- Tanush Jagdish
- Department of Molecular and Cellular Biology and The Program for Systems Synthetic and Quantitative Biology, Harvard University, Cambridge, United States.
| | - Alex N Nguyen Ba
- Department of Biology, University of Toronto at Mississauga, Mississauga, Canada; Department of Cell and Systems Biology, University of Toronto, Toronto, Canada.
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Yao Z, Wang Q, Dai Z. Recent Advances in Directed Yeast Genome Evolution. J Fungi (Basel) 2022; 8:635. [PMID: 35736118 PMCID: PMC9225242 DOI: 10.3390/jof8060635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/09/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022] Open
Abstract
Saccharomyces cerevisiae, as a Generally Recognized as Safe (GRAS) fungus, has become one of the most widely used chassis cells for industrial applications and basic research. However, owing to its complex genetic background and intertwined metabolic networks, there are still many obstacles that need to be overcome in order to improve desired traits and to successfully link genotypes to phenotypes. In this context, genome editing and evolutionary technology have rapidly progressed over the last few decades to facilitate the rapid generation of tailor-made properties as well as for the precise determination of relevant gene targets that regulate physiological functions, including stress resistance, metabolic-pathway optimization and organismal adaptation. Directed genome evolution has emerged as a versatile tool to enable researchers to access desired traits and to study increasingly complicated phenomena. Here, the development of directed genome evolutions in S. cerevisiae is reviewed, with a focus on different techniques driving evolutionary engineering.
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Affiliation(s)
- Zhen Yao
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China;
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Qinhong Wang
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China;
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Zongjie Dai
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China;
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
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Fu X, Huang Y, Shen Y. Improving the Efficiency and Orthogonality of Genetic Code Expansion. BIODESIGN RESEARCH 2022; 2022:9896125. [PMID: 37850140 PMCID: PMC10521639 DOI: 10.34133/2022/9896125] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/20/2022] [Indexed: 10/19/2023] Open
Abstract
The site-specific incorporation of the noncanonical amino acid (ncAA) into proteins via genetic code expansion (GCE) has enabled the development of new and powerful ways to learn, regulate, and evolve biological functions in vivo. However, cellular biosynthesis of ncAA-containing proteins with high efficiency and fidelity is a formidable challenge. In this review, we summarize up-to-date progress towards improving the efficiency and orthogonality of GCE and enhancing intracellular compatibility of introduced translation machinery in the living cells by creation and optimization of orthogonal translation components, constructing genomically recoded organism (GRO), utilization of unnatural base pairs (UBP) and quadruplet codons (four-base codons), and spatial separation of orthogonal translation.
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Affiliation(s)
- Xian Fu
- BGI-Shenzhen, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120China
| | - Yijian Huang
- BGI-Shenzhen, Shenzhen 518083, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Shen
- BGI-Shenzhen, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Abril AG, Carrera M, Notario V, Sánchez-Pérez Á, Villa TG. The Use of Bacteriophages in Biotechnology and Recent Insights into Proteomics. Antibiotics (Basel) 2022; 11:653. [PMID: 35625297 PMCID: PMC9137636 DOI: 10.3390/antibiotics11050653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 12/10/2022] Open
Abstract
Phages have certain features, such as their ability to form protein-protein interactions, that make them good candidates for use in a variety of beneficial applications, such as in human or animal health, industry, food science, food safety, and agriculture. It is essential to identify and characterize the proteins produced by particular phages in order to use these viruses in a variety of functional processes, such as bacterial detection, as vehicles for drug delivery, in vaccine development, and to combat multidrug resistant bacterial infections. Furthermore, phages can also play a major role in the design of a variety of cheap and stable sensors as well as in diagnostic assays that can either specifically identify specific compounds or detect bacteria. This article reviews recently developed phage-based techniques, such as the use of recombinant tempered phages, phage display and phage amplification-based detection. It also encompasses the application of phages as capture elements, biosensors and bioreceptors, with a special emphasis on novel bacteriophage-based mass spectrometry (MS) applications.
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Affiliation(s)
- Ana G. Abril
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15898 Santiago de Compostela, Spain;
- Department of Food Technology, Spanish National Research Council (CSIC), Marine Research Institute (IIM), 36208 Vigo, Spain;
| | - Mónica Carrera
- Department of Food Technology, Spanish National Research Council (CSIC), Marine Research Institute (IIM), 36208 Vigo, Spain;
| | - Vicente Notario
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA;
| | - Ángeles Sánchez-Pérez
- Sydney School of Veterinary Science, Faculty of Science, University of Sydney, Sydney, NSW 2006, Australia;
| | - Tomás G. Villa
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15898 Santiago de Compostela, Spain;
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DeBenedictis EA, Söll D, Esvelt KM. Measuring the tolerance of the genetic code to altered codon size. eLife 2022; 11:76941. [PMID: 35293861 PMCID: PMC9094753 DOI: 10.7554/elife.76941] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Translation using four-base codons occurs in both natural and synthetic systems. What constraints contributed to the universal adoption of a triplet codon, rather than quadruplet codon, genetic code? Here, we investigate the tolerance of the Escherichia coli genetic code to tRNA mutations that increase codon size. We found that tRNAs from all 20 canonical isoacceptor classes can be converted to functional quadruplet tRNAs (qtRNAs). Many of these selectively incorporate a single amino acid in response to a specified four-base codon, as confirmed with mass spectrometry. However, efficient quadruplet codon translation often requires multiple tRNA mutations. Moreover, while tRNAs were largely amenable to quadruplet conversion, only nine of the twenty aminoacyl tRNA synthetases tolerate quadruplet anticodons. These may constitute a functional and mutually orthogonal set, but one that sharply limits the chemical alphabet available to a nascent all-quadruplet code. Our results suggest that the triplet codon code was selected because it is simpler and sufficient, not because a quadruplet codon code is unachievable. These data provide a blueprint for synthetic biologists to deliberately engineer an all-quadruplet expanded genetic code.
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Affiliation(s)
- Erika Alden DeBenedictis
- Department of Biological Engineering, Massachusetts Institue of Technology, Cambridge, United States
| | - Dieter Söll
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, United States
| | - Kevin M Esvelt
- Department of Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, United States
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McLure RJ, Radford SE, Brockwell DJ. High-throughput directed evolution: a golden era for protein science. TRENDS IN CHEMISTRY 2022. [DOI: 10.1016/j.trechm.2022.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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