1
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Goss AL, Shudick RE, Johnson RJ. Shifting Mycobacterial Serine Hydrolase Activity Visualized Using Multi-Layer In-Gel Activity Assays. Molecules 2024; 29:3386. [PMID: 39064965 PMCID: PMC11279797 DOI: 10.3390/molecules29143386] [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: 06/20/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
The ability of Mycobacterium tuberculosis to derive lipids from the host, store them intracellularly, and then break them down into energy requires a battery of serine hydrolases. Serine hydrolases are a large, diverse enzyme family with functional roles in dormant, active, and reactivating mycobacterial cultures. To rapidly measure substrate-dependent shifts in mycobacterial serine hydrolase activity, we combined a robust mycobacterial growth system of nitrogen limitation and variable carbon availability with nimble in-gel fluorogenic enzyme measurements. Using this methodology, we rapidly analyzed a range of ester substrates, identified multiple hydrolases concurrently, observed functional enzyme shifts, and measured global substrate preferences. Within every growth condition, mycobacterial hydrolases displayed the full, dynamic range of upregulated, downregulated, and constitutively active hydrolases independent of the ester substrate. Increasing the alkyl chain length of the ester substrate also allowed visualization of distinct hydrolase activity likely corresponding with lipases most responsible for lipid breakdown. The most robust expression of hydrolase activity was observed under the highest stress growth conditions, reflecting the induction of multiple metabolic pathways scavenging for energy to survive under this high stress. The unique hydrolases present under these high-stress conditions could represent novel drug targets for combination treatment with current front-line therapeutics. Combining diverse fluorogenic esters with in-gel activity measurements provides a rapid, customizable, and sensitive detection method for mycobacterial serine hydrolase activity.
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Affiliation(s)
| | | | - R. Jeremy Johnson
- Department of Chemistry and Biochemistry, Butler University, 4600 Sunset Ave., Indianapolis, IN 46208, USA
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2
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Wardman JF, Withers SG. Carbohydrate-active enzyme (CAZyme) discovery and engineering via (Ultra)high-throughput screening. RSC Chem Biol 2024; 5:595-616. [PMID: 38966674 PMCID: PMC11221537 DOI: 10.1039/d4cb00024b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/16/2024] [Indexed: 07/06/2024] Open
Abstract
Carbohydrate-active enzymes (CAZymes) constitute a diverse set of enzymes that catalyze the assembly, degradation, and modification of carbohydrates. These enzymes have been fashioned into potent, selective catalysts by millennia of evolution, and yet are also highly adaptable and readily evolved in the laboratory. To identify and engineer CAZymes for different purposes, (ultra)high-throughput screening campaigns have been frequently utilized with great success. This review provides an overview of the different approaches taken in screening for CAZymes and how mechanistic understandings of CAZymes can enable new approaches to screening. Within, we also cover how cutting-edge techniques such as microfluidics, advances in computational approaches and synthetic biology, as well as novel assay designs are leading the field towards more informative and effective screening approaches.
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Affiliation(s)
- Jacob F Wardman
- Department of Biochemistry and Molecular Biology, University of British Columbia Vancouver BC V6T 1Z3 Canada
- Michael Smith Laboratories, University of British Columbia Vancouver BC V6T 1Z4 Canada
| | - Stephen G Withers
- Department of Biochemistry and Molecular Biology, University of British Columbia Vancouver BC V6T 1Z3 Canada
- Michael Smith Laboratories, University of British Columbia Vancouver BC V6T 1Z4 Canada
- Department of Chemistry, University of British Columbia Vancouver BC V6T 1Z1 Canada
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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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Jansen S, Mayer C. A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues. JACS AU 2024; 4:1583-1590. [PMID: 38665651 PMCID: PMC11040555 DOI: 10.1021/jacsau.4c00070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 04/28/2024]
Abstract
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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Affiliation(s)
- Suzanne
C. Jansen
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747
AG Groningen, The
Netherlands
| | - Clemens Mayer
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747
AG Groningen, The
Netherlands
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5
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Park SY, Qiu J, Wei S, Peterson FC, Beltrán J, Medina-Cucurella AV, Vaidya AS, Xing Z, Volkman BF, Nusinow DA, Whitehead TA, Wheeldon I, Cutler SR. An orthogonalized PYR1-based CID module with reprogrammable ligand-binding specificity. Nat Chem Biol 2024; 20:103-110. [PMID: 37872402 PMCID: PMC10746540 DOI: 10.1038/s41589-023-01447-7] [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: 10/27/2022] [Accepted: 09/13/2023] [Indexed: 10/25/2023]
Abstract
Plants sense abscisic acid (ABA) using chemical-induced dimerization (CID) modules, including the receptor PYR1 and HAB1, a phosphatase inhibited by ligand-activated PYR1. This system is unique because of the relative ease with which ligand recognition can be reprogrammed. To expand the PYR1 system, we designed an orthogonal '*' module, which harbors a dimer interface salt bridge; X-ray crystallographic, biochemical and in vivo analyses confirm its orthogonality. We used this module to create PYR1*MANDI/HAB1* and PYR1*AZIN/HAB1*, which possess nanomolar sensitivities to their activating ligands mandipropamid and azinphos-ethyl. Experiments in Arabidopsis thaliana and Saccharomyces cerevisiae demonstrate the sensitive detection of banned organophosphate contaminants using living biosensors and the construction of multi-input/output genetic circuits. Our new modules enable ligand-programmable multi-channel CID systems for plant and eukaryotic synthetic biology that can empower new plant-based and microbe-based sensing modalities.
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Affiliation(s)
- Sang-Youl Park
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Jingde Qiu
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Shuang Wei
- Department of Biochemistry, University of California, Riverside, Riverside, CA, USA
| | - Francis C Peterson
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jesús Beltrán
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
- Department of Plant and Soil Sciences, Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA
| | | | - Aditya S Vaidya
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Zenan Xing
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Brian F Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA
| | - Ian Wheeldon
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, USA.
- Center for Industrial Biotechnology, University of California, Riverside, Riverside, CA, USA.
| | - Sean R Cutler
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA.
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
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6
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Li Z, Deng Y, Yang GY. Growth-coupled high throughput selection for directed enzyme evolution. Biotechnol Adv 2023; 68:108238. [PMID: 37619825 DOI: 10.1016/j.biotechadv.2023.108238] [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: 06/05/2023] [Revised: 08/03/2023] [Accepted: 08/20/2023] [Indexed: 08/26/2023]
Abstract
Directed enzyme evolution has revolutionized the rapid development of enzymes with desired properties. However, the lack of a high-throughput method to identify the most suitable variants from a large pool of genetic diversity poses a major bottleneck. To overcome this challenge, growth-coupled in vivo high-throughput selection approaches (GCHTS) have emerged as a novel selection system for enzyme evolution. GCHTS links the survival of the host cell with the properties of the target protein, resulting in a screening system that is easily measurable and has a high throughput-scale limited only by transformation efficiency. This allows for the rapid identification of desired variants from a pool of >109 variants in each experiment. In recent years, GCHTS approaches have been extensively utilized in the directed evolution of multiple enzymes, demonstrating success in catalyzing non-native substrates, enhancing catalytic activity, and acquiring novel functions. This review introduces three main strategies employed to achieve GCHTS: the elimination of toxic compounds via desired variants, enabling host cells to thrive in hazardous conditions; the complementation of an auxotroph with desired variants, where essential genes for cell growth have been eliminated; and the control of the transcription or expression of a reporter gene related to host cell growth, regulated by the desired variants. Additionally, we highlighted the recent developments in the in vivo continuous evolution of enzyme technology, including phage-assisted continuous evolution (PACE) and orthogonal DNA Replication (OrthoRep). Furthermore, this review discusses the challenges and future prospects in the field of growth-coupled selection for protein engineering.
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Affiliation(s)
- Zhengqun Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuting Deng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Guang-Yu Yang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
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7
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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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8
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Shao Q, Jiang Y, Yang ZJ. EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation. J Chem Inf Model 2023; 63:5650-5659. [PMID: 37611241 PMCID: PMC11211066 DOI: 10.1021/acs.jcim.3c00618] [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] [Indexed: 08/25/2023]
Abstract
Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.
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Affiliation(s)
- Qianzhen Shao
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zhongyue J. Yang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
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9
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Neugebauer ME, Hsu A, Arbab M, Krasnow NA, McElroy AN, Pandey S, Doman JL, Huang TP, Raguram A, Banskota S, Newby GA, Tolar J, Osborn MJ, Liu DR. Evolution of an adenine base editor into a small, efficient cytosine base editor with low off-target activity. Nat Biotechnol 2023; 41:673-685. [PMID: 36357719 PMCID: PMC10188366 DOI: 10.1038/s41587-022-01533-6] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/28/2022] [Indexed: 11/12/2022]
Abstract
Cytosine base editors (CBEs) are larger and can suffer from higher off-target activity or lower on-target editing efficiency than current adenine base editors (ABEs). To develop a CBE that retains the small size, low off-target activity and high on-target activity of current ABEs, we evolved the highly active deoxyadenosine deaminase TadA-8e to perform cytidine deamination using phage-assisted continuous evolution. Evolved TadA cytidine deaminases contain mutations at DNA-binding residues that alter enzyme selectivity to strongly favor deoxycytidine over deoxyadenosine deamination. Compared to commonly used CBEs, TadA-derived cytosine base editors (TadCBEs) offer similar or higher on-target activity, smaller size and substantially lower Cas-independent DNA and RNA off-target editing activity. We also identified a TadA dual base editor (TadDE) that performs equally efficient cytosine and adenine base editing. TadCBEs support single or multiplexed base editing at therapeutically relevant genomic loci in primary human T cells and primary human hematopoietic stem and progenitor cells. TadCBEs expand the utility of CBEs for precision gene editing.
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Affiliation(s)
- Monica E Neugebauer
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Alvin Hsu
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Mandana Arbab
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Nicholas A Krasnow
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Amber N McElroy
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Smriti Pandey
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Jordan L Doman
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Tony P Huang
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Aditya Raguram
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Samagya Banskota
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Gregory A Newby
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Jakub Tolar
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Mark J Osborn
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - David R Liu
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, 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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10
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Steiner PJ, Swift SD, Bedewitz M, Wheeldon I, Cutler SR, Nusinow DA, Whitehead TA. A Closed Form Model for Molecular Ratchet-Type Chemically Induced Dimerization Modules. Biochemistry 2023; 62:281-291. [PMID: 35675717 DOI: 10.1021/acs.biochem.2c00172] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Chemical-induced dimerization (CID) modules enable users to implement ligand-controlled cellular and biochemical functions for a number of problems in basic and applied biology. A special class of CID modules occur naturally in plants and involve a hormone receptor that binds a hormone, triggering a conformational change in the receptor that enables recognition by a second binding protein. Two recent reports show that such hormone receptors can be engineered to sense dozens of structurally diverse compounds. As a closed form model for molecular ratchets would be of immense utility in forward engineering of biological systems, here we have developed a closed form model for these distinct CID modules. These modules, which we call molecular ratchets, are distinct from more common CID modules called molecular glues in that they engage in saturable binding kinetics and are characterized well by a Hill equation. A defining characteristic of molecular ratchets is that the sensitivity of the response can be tuned by increasing the molar ratio of the hormone receptor to the binding protein. Thus, the same molecular ratchet can have a pico- or micromolar EC50 depending on the concentration of the different receptor and binding proteins. Closed form models are derived for a base elementary reaction rate model, for ligand-independent complexation of the receptor and binding protein, and for homodimerization of the hormone receptor. Useful governing equations for a variety of in vitro and in vivo applications are derived, including enzyme-linked immunosorbent assay-like microplate assays, transcriptional activation in prokaryotes and eukaryotes, and ligand-induced split protein complementation.
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Affiliation(s)
- Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Samuel D Swift
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Matthew Bedewitz
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Ian Wheeldon
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, California 92521, United States.,Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, California 92521, United States
| | - Sean R Cutler
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, California 92521, United States.,Department of Botany and Plant Sciences, University of California Riverside, Riverside, California 92521, United States.,Center for Plant Cell Biology, University of California Riverside, Riverside, California 92521, United States
| | - Dmitri A Nusinow
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132, United States
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
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11
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Sellés Vidal L, Isalan M, Heap JT, Ledesma-Amaro R. A primer to directed evolution: current methodologies and future directions. RSC Chem Biol 2023; 4:271-291. [PMID: 37034405 PMCID: PMC10074555 DOI: 10.1039/d2cb00231k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/18/2023] [Indexed: 01/30/2023] Open
Abstract
This review summarises the methods available for directed evolution, including mutagenesis and variant selection techniques. The advantages and disadvantages of each technique are presented, and future challenges in the field are discussed.
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Affiliation(s)
- Lara Sellés Vidal
- Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Mark Isalan
- Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - John T. Heap
- Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
- School of Life Sciences, The University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Rodrigo Ledesma-Amaro
- Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
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12
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Andon JS, Lee B, Wang T. Enzyme directed evolution using genetically encodable biosensors. Org Biomol Chem 2022; 20:5891-5906. [PMID: 35437559 DOI: 10.1039/d2ob00443g] [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] [Indexed: 11/21/2022]
Abstract
Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.
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Affiliation(s)
- James S Andon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - ByungUk Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Tina Wang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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Hong KQ, Zhang J, Jin B, Chen T, Wang ZW. Development and characterization of a glycine biosensor system for fine-tuned metabolic regulation in Escherichia coli. Microb Cell Fact 2022; 21:56. [PMID: 35392910 PMCID: PMC8991567 DOI: 10.1186/s12934-022-01779-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/23/2022] [Indexed: 11/10/2022] Open
Abstract
Background In vivo biosensors have a wide range of applications, ranging from the detection of metabolites to the regulation of metabolic networks, providing versatile tools for synthetic biology and metabolic engineering. However, in view of the vast array of metabolite molecules, the existing number and performance of biosensors is far from sufficient, limiting their potential applications in metabolic engineering. Therefore, we developed the synthetic glycine-ON and -OFF riboswitches for metabolic regulation and directed evolution of enzyme in Escherichia coli. Results The results showed that a synthetic glycine-OFF riboswitch (glyOFF6) and an increased-detection-range synthetic glycine-ON riboswitch (glyON14) were successfully screened from a library based on the Bacillus subtilis glycine riboswitch using fluorescence-activated cell sorting (FACS) and tetA-based dual genetic selection. The two synthetic glycine riboswitches were successfully used in tunable regulation of lactate synthesis, dynamic regulation of serine synthesis and directed evolution of alanine-glyoxylate aminotransferase in Escherichia coli, respectively. Mutants AGXT22 and AGXT26 of alanine-glyoxylate aminotransferase with an increase of 58% and 73% enzyme activity were obtained by using a high-throughput screening platform based on the synthetic glycine-OFF riboswitch, and successfully used to increase the 5-aminolevulinic acid yield of engineered Escherichia coli. Conclusions A synthetic glycine-OFF riboswitch and an increased-detection-range synthetic glycine-ON riboswitch were successfully designed and screened. The developed riboswitches showed broad application in tunable regulation, dynamic regulation and directed evolution of enzyme in E. coli. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-022-01779-4.
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Affiliation(s)
- Kun-Qiang Hong
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China.,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, 300072, China.,SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, 300072, China.,Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin, 300072, China
| | - Jing Zhang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China.,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, 300072, China.,SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, 300072, China.,Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin, 300072, China
| | - Biao Jin
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China.,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, 300072, China.,SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, 300072, China.,Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin, 300072, China
| | - Tao Chen
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China.,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, 300072, China.,SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, 300072, China.,Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin, 300072, China
| | - Zhi-Wen Wang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China. .,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, 300072, China. .,SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, 300072, China. .,Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin, 300072, China.
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Jiang Y, Yan B, Chen Y, Juarez RJ, Yang ZJ. Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates. J Phys Chem B 2022; 126:2486-2495. [PMID: 35324218 DOI: 10.1021/acs.jpcb.2c00142] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme-substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.
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Affiliation(s)
- Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Bailu Yan
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yu Chen
- School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Reecan J Juarez
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
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