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Balzerani F, Blasco T, Pérez-Burillo S, Valcarcel LV, Hassoun S, Planes FJ. Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota. NPJ Syst Biol Appl 2024; 10:56. [PMID: 38802371 PMCID: PMC11130242 DOI: 10.1038/s41540-024-00381-1] [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: 05/15/2023] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.
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Affiliation(s)
- Francesco Balzerani
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Telmo Blasco
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Sergio Pérez-Burillo
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Luis V Valcarcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA.
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, 02155, USA.
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain.
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2
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Barghout RA, Xu Z, Betala S, Mahadevan R. Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels. Curr Opin Biotechnol 2023; 84:103007. [PMID: 37931573 DOI: 10.1016/j.copbio.2023.103007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 11/08/2023]
Abstract
Biotechnology has revolutionized the development of sustainable energy sources by harnessing biomass as a feedstock for energy production. However, challenges such as recalcitrant feedstocks and inefficient metabolic pathways hinder the large-scale integration of renewable energy systems. Enzyme engineering has emerged as a powerful tool to address these challenges by enhancing enzyme activity, specificity, and stability. Generative machine learning (ML) models have shown great promise in accelerating protein design, allowing for the generation of novel protein sequences with desired properties by navigating vast spaces. This review paper aims to summarize the state of the art in generative models for protein design and how they can be applied to bioenergy applications, including the underlying architectures and training strategies. Additionally, it highlights the importance of high-quality datasets for training and evaluating generative models, organizes available datasets for generative protein design, and discusses the potential of applying generative models to strain design for bioenergy production.
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Affiliation(s)
- Rana A Barghout
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St, Toronto, ON, Canada.
| | - Zhiqing Xu
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St, Toronto, ON, Canada
| | - Siddharth Betala
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St, Toronto, ON, Canada
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3
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Liu Y, Chen L, Liu P, Yuan Q, Ma C, Wang W, Zhang C, Ma H, Zeng A. Design, Evaluation, and Implementation of Synthetic Isopentyldiol Pathways in Escherichia coli. ACS Synth Biol 2023; 12:3381-3392. [PMID: 37870756 DOI: 10.1021/acssynbio.3c00394] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
Isopentyldiol (IPDO) is an important raw material in the cosmetic industry. So far, IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a biobased process. We previously reported a biosynthetic route that produces IPDO via extending the leucine catabolism (route A), the efficiency of which, however, is not satisfactory. To address this issue, we computationally designed a novel non-natural IPDO synthesis pathway (route B) using RetroPath RL, the state-of-the-art tool for bioretrosynthesis based on artificial intelligence methods. We compared this new pathway with route A and two other intuitively designed routes for IPDO biosynthesis from various perspectives. Route B, which exhibits the highest thermodynamic driving force, least non-native reaction steps, and lowest energy requirements, appeared to hold the greatest potential for IPDO production. All three newly designed routes were then implemented in the Escherichia coli BL21(DE3) strain. Results show that the computationally designed route B can produce 2.2 mg/L IPDO from glucose but no IPDO production from routes C and D. These results highlight the importance and usefulness of in silico design and comprehensive evaluation of the potential efficiencies of candidate pathways in constructing novel non-natural pathways for the production of biochemicals.
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Affiliation(s)
- Yongfei Liu
- Center of Synthetic Biology and Integrated Bioengineering, School of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestr. 15, Hamburg 21073, Germany
| | - Lin Chen
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestr. 15, Hamburg 21073, Germany
| | - Pi Liu
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Qianqian Yuan
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Chengwei Ma
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestr. 15, Hamburg 21073, Germany
| | - Wei Wang
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestr. 15, Hamburg 21073, Germany
| | - Chijian Zhang
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestr. 15, Hamburg 21073, Germany
- Hua An Tang Biotech Group Co., Ltd, Guangzhou 511434, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - AnPing Zeng
- Center of Synthetic Biology and Integrated Bioengineering, School of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestr. 15, Hamburg 21073, Germany
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4
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Upadhyay V, Boorla VS, Maranas CD. Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network. Metab Eng 2023; 78:171-182. [PMID: 37301359 DOI: 10.1016/j.ymben.2023.06.001] [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: 12/15/2022] [Revised: 05/19/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023]
Abstract
Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent retro-biosynthetic algorithms increasingly use novel conversions that require altering the substrate or cofactor specificities of existing enzymes while connecting pathways leading to a target metabolite. However, identifying and re-engineering enzymes for desired novel conversions are currently the bottlenecks in implementing such designed pathways. Herein, we present EnzRank, a convolutional neural network (CNN) based approach, to rank-order existing enzymes in terms of their suitability to undergo successful protein engineering through directed evolution or de novo design towards a desired specific substrate activity. We train the CNN model on 11,800 known active enzyme-substrate pairs from the BRENDA database as positive samples and data generated by scrambling these pairs as negative samples using substrate dissimilarity between an enzyme's native substrate and all other molecules present in the dataset using Tanimoto similarity score. EnzRank achieves an average recovery rate of 80.72% and 73.08% for positive and negative pairs on test data after using a 10-fold holdout method for training and cross-validation. We further developed a web-based user interface (available at https://huggingface.co/spaces/vuu10/EnzRank) to predict enzyme-substrate activity using SMILES strings of substrates and enzyme sequence as input to allow convenient and easy-to-use access to EnzRank. In summary, this effort can aid de novo pathway design tools to prioritize starting enzyme re-engineering candidates for novel reactions as well as in predicting the potential secondary activity of enzymes in cell metabolism.
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Affiliation(s)
- Vikas Upadhyay
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
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Gurdo N, Volke DC, McCloskey D, Nikel PI. Automating the design-build-test-learn cycle towards next-generation bacterial cell factories. N Biotechnol 2023; 74:1-15. [PMID: 36736693 DOI: 10.1016/j.nbt.2023.01.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/15/2023] [Accepted: 01/22/2023] [Indexed: 02/04/2023]
Abstract
Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.
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Affiliation(s)
- Nicolás Gurdo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Daniel C Volke
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Douglas McCloskey
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Pablo Iván Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark.
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6
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Zeng XX, Zeng JB. Systems Medicine as a Strategy to Deal with Alzheimer's Disease. J Alzheimers Dis 2023; 96:1411-1426. [PMID: 37980671 DOI: 10.3233/jad-230739] [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] [Indexed: 11/21/2023]
Abstract
The traits of Alzheimer's disease (AD) include amyloid plaques made of Aβ1-40 and Aβ1-42, and neurofibrillary tangles by the hyperphosphorylation of tau protein. AD is a complex disorder that is heterogenous in genetical, neuropathological, and clinical contexts. Current available therapeutics are unable to cure AD. Systems medicine is a strategy by viewing the body as a whole system, taking into account each individual's unique health profile, provide treatment and associated nursing care clinically for the patient, aiming for precision. Since the onset of AD can lead towards cognitive impairment, it is vital to intervene and diagnose early and prevent further progressive loss of neurons. Moreover, as the individual's brain functions are impaired due to neurodegeneration in AD, it is essential to reconstruct the neurons or brain cells to enable normal brain functions. Although there are different subtypes of AD due to varied pathological lesions, in the majority cases of AD, neurodegeneration and severe brain atrophy develop at the chronic stage. Novel approaches including RNA based gene therapy, stem cell based technology, bioprinting technology, synthetic biology for brain tissue reconstruction are researched in recent decades in the hope to decrease neuroinflammation and restore normal brain function in individuals of AD. Systems medicine include the prevention of disease, diagnosis and treatment by viewing the individual's body as a whole system, along with systems medicine based nursing as a strategy against AD that should be researched further.
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Affiliation(s)
- Xiao Xue Zeng
- Department of Health Management, Centre of General Practice, The Seventh Affiliated Hospital, Southern Medical University, Lishui Town, Nanhai District, Foshan City, Guangdong Province, P.R. China
| | - Jie Bangzhe Zeng
- Benjoe Institute of Systems Bio-Engineering, High Technology Park, Xinbei District, Changzhou City, Jiangsu Province, P.R. China
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7
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Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods. NPJ Syst Biol Appl 2022; 8:24. [PMID: 35831427 PMCID: PMC9279433 DOI: 10.1038/s41540-022-00234-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/20/2022] [Indexed: 11/08/2022] Open
Abstract
The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions.
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8
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Xu Z, Mahadevan R. Efficient Enumeration of Branched Novel Biochemical Pathways Using a Probabilistic Technique. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c02211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhiqing Xu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
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9
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Luo Y, James JS, Jones S, Martella A, Cai Y. EMMA-CAD: Design Automation for Synthetic Mammalian Constructs. ACS Synth Biol 2022; 11:579-586. [PMID: 35050610 DOI: 10.1021/acssynbio.1c00433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computational design tools are the cornerstone of synthetic biology and have underpinned its rapid development over the past two decades. As the field has matured, the scale of biological investigation has expanded dramatically, and researchers often must rely on computational tools to operate in the high-throughput investigational space. This is especially apparent in the modular design of DNA expression circuits, where complexity is accumulated rapidly. Alongside our automated pipeline for the high-throughput construction of Extensible Modular Mammalian Assembly (EMMA) expression vectors, we recognized the need for an integrated software solution for EMMA vector design. Here we present EMMA-CAD (https://emma.cailab.org), a powerful web-based computer-aided design tool for the rapid design of bespoke mammalian expression vectors. EMMA-CAD features a variety of functionalities, including a user-friendly design interface, automated connector selection underpinned by rigorous computer optimization algorithms, customization of part libraries, and personalized design spaces. Capable of translating vector assembly designs into human- and machine-readable protocols for vector construction, EMMA-CAD integrates seamlessly into our automated EMMA pipeline, hence completing an end-to-end design to production workflow.
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Affiliation(s)
- Yisha Luo
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - Joshua S. James
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore
| | - Sally Jones
- John Innes Centre, Norwich Research Park, Norwich, Norfolk NR4 7UH, U.K
| | - Andrea Martella
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Yizhi Cai
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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10
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Rinaldi MA, Ferraz CA, Scrutton NS. Alternative metabolic pathways and strategies to high-titre terpenoid production in Escherichia coli. Nat Prod Rep 2022; 39:90-118. [PMID: 34231643 PMCID: PMC8791446 DOI: 10.1039/d1np00025j] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Terpenoids are a diverse group of chemicals used in a wide range of industries. Microbial terpenoid production has the potential to displace traditional manufacturing of these compounds with renewable processes, but further titre improvements are needed to reach cost competitiveness. This review discusses strategies to increase terpenoid titres in Escherichia coli with a focus on alternative metabolic pathways. Alternative pathways can lead to improved titres by providing higher orthogonality to native metabolism that redirects carbon flux, by avoiding toxic intermediates, by bypassing highly-regulated or bottleneck steps, or by being shorter and thus more efficient and easier to manipulate. The canonical 2-C-methyl-D-erythritol 4-phosphate (MEP) and mevalonate (MVA) pathways are engineered to increase titres, sometimes using homologs from different species to address bottlenecks. Further, alternative terpenoid pathways, including additional entry points into the MEP and MVA pathways, archaeal MVA pathways, and new artificial pathways provide new tools to increase titres. Prenyl diphosphate synthases elongate terpenoid chains, and alternative homologs create orthogonal pathways and increase product diversity. Alternative sources of terpenoid synthases and modifying enzymes can also be better suited for E. coli expression. Mining the growing number of bacterial genomes for new bacterial terpenoid synthases and modifying enzymes identifies enzymes that outperform eukaryotic ones and expand microbial terpenoid production diversity. Terpenoid removal from cells is also crucial in production, and so terpenoid recovery and approaches to handle end-product toxicity increase titres. Combined, these strategies are contributing to current efforts to increase microbial terpenoid production towards commercial feasibility.
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Affiliation(s)
- Mauro A Rinaldi
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
| | - Clara A Ferraz
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
| | - Nigel S Scrutton
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
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11
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Breger JC, Ellis GA, Walper SA, Susumu K, Medintz IL. Implementing Multi-Enzyme Biocatalytic Systems Using Nanoparticle Scaffolds. Methods Mol Biol 2022; 2487:227-262. [PMID: 35687240 DOI: 10.1007/978-1-0716-2269-8_15] [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] [Indexed: 06/15/2023]
Abstract
Interest in multi-enzyme synthesis outside of cells (in vitro) is becoming far more prevalent as the field of cell-free synthetic biology grows exponentially. Such synthesis would allow for complex chemical transformations based on the exquisite specificity of enzymes in a "greener" manner as compared to organic chemical transformations. Here, we describe how nanoparticles, and in this specific case-semiconductor quantum dots, can be used to both stabilize enzymes and further allow them to self-assemble into nanocomplexes that facilitate high-efficiency channeling phenomena. Pertinent protocol information is provided on enzyme expression, choice of nanoparticulate material, confirmation of enzyme attachment to nanoparticles, assay format and tracking, data analysis, and optimization of assay formats to draw the best analytical information from the underlying processes.
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Affiliation(s)
- Joyce C Breger
- Center for Bio/Molecular Science and Engineering, Code 6900, Washington, DC, USA
| | - Gregory A Ellis
- Center for Bio/Molecular Science and Engineering, Code 6900, Washington, DC, USA
| | - Scott A Walper
- Center for Bio/Molecular Science and Engineering, Code 6900, Washington, DC, USA
| | - Kimihiro Susumu
- Optical Sciences Division, Code 5611, U.S. Naval Research Laboratory, Washington, DC, USA
- Jacobs Corporation, Hanover, MD, USA
| | - Igor L Medintz
- Center for Bio/Molecular Science and Engineering, Code 6900, Washington, DC, USA.
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12
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Wang L, Upadhyay V, Maranas CD. dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. PLoS Comput Biol 2021; 17:e1009448. [PMID: 34570771 PMCID: PMC8496854 DOI: 10.1371/journal.pcbi.1009448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/07/2021] [Accepted: 09/13/2021] [Indexed: 11/19/2022] Open
Abstract
Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG′o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor). The standard Gibbs energy change is commonly used to check for the feasibility of enzyme-catalyzed reactions as thermodynamics plays a crucial role in pathway design for biochemical synthesis. The group contribution methods using expert-defined functional groups have been extensively used for estimating standard Gibbs energy change. Here, we introduce a molecular fingerprint-based thermodynamic tool, dGPredictor, that enables distinguishing between (stereo)isomers in metabolic reactions leading to improved reaction coverage and comparable prediction accuracy as GC methods. dGPredictor can also be used alongside de novo pathway design tools to ensure the correct directionality of chosen reaction steps. We applied and tested dGPredictor on reactions from the KEGG database and applied it to screen an isobutanol synthesis pathway design. An open-source, user-friendly web interface is provided to facilitate easy access for standard Gibbs energy change of reactions at different pH values. (https://github.com/maranasgroup/dGPredictor).
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Vikas Upadhyay
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
- * E-mail:
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13
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Diéguez-Santana K, Casañola-Martin GM, Green JR, Rasulev B, González-Díaz H. Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models. Curr Top Med Chem 2021; 21:819-827. [PMID: 33797370 DOI: 10.2174/1568026621666210331161144] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. OBJECTIVE In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions). RESULTS The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. METHODS In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis' group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. CONCLUSION Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, and Basque Center for Biophysics CSIC-UPV/EHU, Leioa 48940, Great Bilbao, Biscay, Basque Country, Spain
| | | | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S 5B6, Ottawa, ON, Canada
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, United States
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, and Basque Center for Biophysics CSIC-UPV/EHU, Leioa 48940, Great Bilbao, Biscay, Basque Country, Spain
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14
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Vila-Santa A, Islam MA, Ferreira FC, Prather KLJ, Mira NP. Prospecting Biochemical Pathways to Implement Microbe-Based Production of the New-to-Nature Platform Chemical Levulinic Acid. ACS Synth Biol 2021; 10:724-736. [PMID: 33764057 DOI: 10.1021/acssynbio.0c00518] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Levulinic acid is a versatile platform molecule with potential to be used as an intermediate in the synthesis of many value-added products used across different industries, from cosmetics to fuels. Thus far, microbial biosynthetic pathways having levulinic acid as a product or an intermediate are not known, which restrains the development and optimization of a microbe-based process envisaging the sustainable bioproduction of this chemical. One of the doors opened by synthetic biology in the design of microbial systems is the implementation of new-to-nature pathways, that is, the assembly of combinations of enzymes not observed in vivo, where the enzymes can use not only their native substrates but also non-native ones, creating synthetic steps that enable the production of novel compounds. Resorting to a combined approach involving complementary computational tools and extensive manual curation, in this work, we provide a thorough prospect of candidate biosynthetic pathways that can be assembled for the production of levulinic acid in Escherichia coli or Saccharomyces cerevisiae. Out of the hundreds of combinations screened, five pathways were selected as best candidates on the basis of the availability of substrates and of candidate enzymes to catalyze the synthetic steps (that is, those steps that involve conversions not previously described). Genome-scale metabolic modeling was used to assess the performance of these pathways in the two selected hosts and to anticipate possible bottlenecks. Not only does the herein described approach offer a platform for the future implementation of the microbial production of levulinic acid but also it provides an organized research strategy that can be used as a framework for the implementation of other new-to-nature biosynthetic pathways for the production of value-added chemicals, thus fostering the emerging field of synthetic industrial microbiotechnology.
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Affiliation(s)
- Ana Vila-Santa
- Department of Bioengineering and Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
| | - M. Ahsanul Islam
- Department of Chemical Engineering, Loughborough University, Leicestershire, LE11 3TU Loughborough, United Kingdom
| | - Frederico C. Ferreira
- Department of Bioengineering and Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
| | - Kristala L. J. Prather
- Department of Chemical Engineering and Center for Integrative Synthetic Biology (CISB), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Nuno P. Mira
- Department of Bioengineering and Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
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15
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Asemoloye MD, Marchisio MA, Gupta VK, Pecoraro L. Genome-based engineering of ligninolytic enzymes in fungi. Microb Cell Fact 2021; 20:20. [PMID: 33478513 PMCID: PMC7819241 DOI: 10.1186/s12934-021-01510-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/07/2021] [Indexed: 12/23/2022] Open
Abstract
Background Many fungi grow as saprobic organisms and obtain nutrients from a wide range of dead organic materials. Among saprobes, fungal species that grow on wood or in polluted environments have evolved prolific mechanisms for the production of degrading compounds, such as ligninolytic enzymes. These enzymes include arrays of intense redox-potential oxidoreductase, such as laccase, catalase, and peroxidases. The ability to produce ligninolytic enzymes makes a variety of fungal species suitable for application in many industries, including the production of biofuels and antibiotics, bioremediation, and biomedical application as biosensors. However, fungal ligninolytic enzymes are produced naturally in small quantities that may not meet the industrial or market demands. Over the last decade, combined synthetic biology and computational designs have yielded significant results in enhancing the synthesis of natural compounds in fungi. Main body of the abstract In this review, we gave insights into different protein engineering methods, including rational, semi-rational, and directed evolution approaches that have been employed to enhance the production of some important ligninolytic enzymes in fungi. We described the role of metabolic pathway engineering to optimize the synthesis of chemical compounds of interest in various fields. We highlighted synthetic biology novel techniques for biosynthetic gene cluster (BGC) activation in fungo and heterologous reconstruction of BGC in microbial cells. We also discussed in detail some recombinant ligninolytic enzymes that have been successfully enhanced and expressed in different heterologous hosts. Finally, we described recent advance in CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas (CRISPR associated) protein systems as the most promising biotechnology for large-scale production of ligninolytic enzymes. Short conclusion Aggregation, expression, and regulation of ligninolytic enzymes in fungi require very complex procedures with many interfering factors. Synthetic and computational biology strategies, as explained in this review, are powerful tools that can be combined to solve these puzzles. These integrated strategies can lead to the production of enzymes with special abilities, such as wide substrate specifications, thermo-stability, tolerance to long time storage, and stability in different substrate conditions, such as pH and nutrients.
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Affiliation(s)
- Michael Dare Asemoloye
- School of Pharmaceutical Science and Technology, Tianjin University, Nankai District, 92 Weijin Road, Tianjin, 300072, China
| | - Mario Andrea Marchisio
- School of Pharmaceutical Science and Technology, Tianjin University, Nankai District, 92 Weijin Road, Tianjin, 300072, China.
| | - Vijai Kumar Gupta
- Biorefining and Advanced Materials Research Center, Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK
| | - Lorenzo Pecoraro
- School of Pharmaceutical Science and Technology, Tianjin University, Nankai District, 92 Weijin Road, Tianjin, 300072, China.
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16
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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17
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Boada Y, Vignoni A, Picó J, Carbonell P. Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories. iScience 2020; 23:101305. [PMID: 32629420 PMCID: PMC7334618 DOI: 10.1016/j.isci.2020.101305] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/05/2020] [Accepted: 06/18/2020] [Indexed: 12/17/2022] Open
Abstract
Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.
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Affiliation(s)
- Yadira Boada
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain; Centro Universitario EDEM, Escuela de Empresarios, Muelle de la Aduana s/n, La Marina de València, 46024 Valencia, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Pablo Carbonell
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.
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18
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Hwang S, Lee N, Cho S, Palsson B, Cho BK. Repurposing Modular Polyketide Synthases and Non-ribosomal Peptide Synthetases for Novel Chemical Biosynthesis. Front Mol Biosci 2020; 7:87. [PMID: 32500080 PMCID: PMC7242659 DOI: 10.3389/fmolb.2020.00087] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/16/2020] [Indexed: 12/16/2022] Open
Abstract
In nature, various enzymes govern diverse biochemical reactions through their specific three-dimensional structures, which have been harnessed to produce many useful bioactive compounds including clinical agents and commodity chemicals. Polyketide synthases (PKSs) and non-ribosomal peptide synthetases (NRPSs) are particularly unique multifunctional enzymes that display modular organization. Individual modules incorporate their own specific substrates and collaborate to assemble complex polyketides or non-ribosomal polypeptides in a linear fashion. Due to the modular properties of PKSs and NRPSs, they have been attractive rational engineering targets for novel chemical production through the predictable modification of each moiety of the complex chemical through engineering of the cognate module. Thus, individual reactions of each module could be separated as a retro-biosynthetic biopart and repurposed to new biosynthetic pathways for the production of biofuels or commodity chemicals. Despite these potentials, repurposing attempts have often failed owing to impaired catalytic activity or the production of unintended products due to incompatible protein–protein interactions between the modules and structural perturbation of the enzyme. Recent advances in the structural, computational, and synthetic tools provide more opportunities for successful repurposing. In this review, we focused on the representative strategies and examples for the repurposing of modular PKSs and NRPSs, along with their advantages and current limitations. Thereafter, synthetic biology tools and perspectives were suggested for potential further advancement, including the rational and large-scale high-throughput approaches. Ultimately, the potential diverse reactions from modular PKSs and NRPSs would be leveraged to expand the reservoir of useful chemicals.
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Affiliation(s)
- Soonkyu Hwang
- Systems and Synthetic Biology Laboratory, Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Namil Lee
- Systems and Synthetic Biology Laboratory, Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Suhyung Cho
- Systems and Synthetic Biology Laboratory, Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Bernhard Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States.,Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States.,The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Byung-Kwan Cho
- Systems and Synthetic Biology Laboratory, Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.,Intelligent Synthetic Biology Center, Daejeon, South Korea
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19
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Wiltschi B, Cernava T, Dennig A, Galindo Casas M, Geier M, Gruber S, Haberbauer M, Heidinger P, Herrero Acero E, Kratzer R, Luley-Goedl C, Müller CA, Pitzer J, Ribitsch D, Sauer M, Schmölzer K, Schnitzhofer W, Sensen CW, Soh J, Steiner K, Winkler CK, Winkler M, Wriessnegger T. Enzymes revolutionize the bioproduction of value-added compounds: From enzyme discovery to special applications. Biotechnol Adv 2020; 40:107520. [DOI: 10.1016/j.biotechadv.2020.107520] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 10/18/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
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20
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Burgener S, Luo S, McLean R, Miller TE, Erb TJ. A roadmap towards integrated catalytic systems of the future. Nat Catal 2020. [DOI: 10.1038/s41929-020-0429-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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21
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Systems biology based metabolic engineering for non-natural chemicals. Biotechnol Adv 2019; 37:107379. [DOI: 10.1016/j.biotechadv.2019.04.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 02/23/2019] [Accepted: 04/01/2019] [Indexed: 12/17/2022]
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22
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Gilbert J, Pearcy N, Norman R, Millat T, Winzer K, King J, Hodgman C, Minton N, Twycross J. Gsmodutils: a python based framework for test-driven genome scale metabolic model development. Bioinformatics 2019; 35:3397-3403. [PMID: 30759197 PMCID: PMC6748746 DOI: 10.1093/bioinformatics/btz088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/29/2019] [Accepted: 02/12/2019] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle. RESULTS As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions. AVAILABILITY AND IMPLEMENTATION The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- James Gilbert
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Nicole Pearcy
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Rupert Norman
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
- School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, UK
| | - Thomas Millat
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Klaus Winzer
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - John King
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Charlie Hodgman
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
- School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, UK
| | - Nigel Minton
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Jamie Twycross
- School of Computer Science, University of Nottingham, Nottingham, UK
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23
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Sinatti VVC, Gonçalves CAX, Romão-Dumaresq AS. Identification of metabolites identical and similar to drugs as candidates for metabolic engineering. J Biotechnol 2019; 302:67-76. [PMID: 31254549 DOI: 10.1016/j.jbiotec.2019.06.303] [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: 01/10/2019] [Revised: 04/20/2019] [Accepted: 06/25/2019] [Indexed: 11/18/2022]
Abstract
Natural compounds and derivatives play an essential role in the pharmaceutical industry, however, the difficulty in resynthesizing natural products or isolate them from the native host, often limit their availability, elevate costs and slow down the pharmaceutical manufacturing process. In this context, application of synthetic biology could enable the efficient production of large amounts of drugs or drug precursors in heterologous microorganisms aiming to accelerate the entire manufacturing process. Considering this perspective, here we developed a pipeline to automatically search for metabolites available in the metabolic space that are structurally similar to worldwide approved drugs. This pipeline involved the in silico screening of metabolites from a metabolic pathway meta-database using both Tanimoto coefficients based on Daylight like fingerprints and Maximum Common Substructure algorithm. The method was successfully applied to identify metabolites sharing essential scaffolds with one or more drugs as potential candidates for metabolic engineering. Three of these metabolites (Festuclavine, Scopolamine, and Baccatin III) were identified as similar to many drugs like Cabergoline, Oxitropium, Paclitaxel and had their metabolic pathways computationally mapped for their production in Saccharomyces cerevisiae with our proprietary pathway design software. These compounds are examples of new opportunities for the application of synthetic biology in pharmaceutical production.
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Affiliation(s)
- Vanessa V C Sinatti
- SENAI Innovation Institute for Biosynthetics, Technology Center for Chemical and Textile Industry, Rio de Janeiro, Brazil.
| | - Carlos Alberto X Gonçalves
- SENAI Innovation Institute for Biosynthetics, Technology Center for Chemical and Textile Industry, Rio de Janeiro, Brazil
| | - Aline S Romão-Dumaresq
- SENAI Innovation Institute for Biosynthetics, Technology Center for Chemical and Textile Industry, Rio de Janeiro, Brazil
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24
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Alanjary M, Cano-Prieto C, Gross H, Medema MH. Computer-aided re-engineering of nonribosomal peptide and polyketide biosynthetic assembly lines. Nat Prod Rep 2019; 36:1249-1261. [PMID: 31259995 DOI: 10.1039/c9np00021f] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Covering: 2014 to 2019Nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) have been the subject of engineering efforts for multiple decades. Their modular assembly line architecture potentially allows unlocking vast chemical space for biosynthesis. However, attempts thus far are often met with mixed success, due to limited molecular compatibility of the parts used for engineering. Now, new engineering strategies, increases in genomic data, and improved computational tools provide more opportunities for major progress. In this review we highlight some of the challenges and progressive strategies for the re-design of NRPSs & type I PKSs and survey useful computational tools and approaches to attain the ultimate goal of semi-automated and design-based engineering of novel peptide and polyketide products.
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Affiliation(s)
- Mohammad Alanjary
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
| | - Carolina Cano-Prieto
- Department of Pharmaceutical Biology, Pharmaceutical Institute, Eberhard Karls Universität Tübingen, Tübingen, Germany.
| | - Harald Gross
- Department of Pharmaceutical Biology, Pharmaceutical Institute, Eberhard Karls Universität Tübingen, Tübingen, Germany.
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
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25
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Lin GM, Warden-Rothman R, Voigt CA. Retrosynthetic design of metabolic pathways to chemicals not found in nature. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.04.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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26
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Erb TJ. Back to the future: Why we need enzymology to build a synthetic metabolism of the future. Beilstein J Org Chem 2019; 15:551-557. [PMID: 30873239 PMCID: PMC6404388 DOI: 10.3762/bjoc.15.49] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 01/29/2019] [Indexed: 12/26/2022] Open
Abstract
Biology is turning from an analytical into a synthetic discipline. This is especially apparent in the field of metabolic engineering, where the concept of synthetic metabolism has been recently developed. Compared to classical metabolic engineering efforts, synthetic metabolism aims at creating novel metabolic networks in a rational fashion from bottom-up. However, while the theoretical design of synthetic metabolic networks has made tremendous progress, the actual realization of such synthetic pathways is still lacking behind. This is mostly because of our limitations in enzyme discovery and engineering to provide the parts required to build synthetic metabolism. Here I discuss the current challenges and limitations in synthetic metabolic engineering and elucidate how modern day enzymology can help to build a synthetic metabolism of the future.
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Affiliation(s)
- Tobias J Erb
- Max-Planck-Institute for Terrestrial Microbiology, Department of Biochemistry & Synthetic Metabolism, Karl-von-Frisch-Str. 10, D-35043 Marburg, Germany.,LOEWE Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
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27
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Garcia-Ruiz E, HamediRad M, Zhao H. Pathway Design, Engineering, and Optimization. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2018; 162:77-116. [PMID: 27629378 DOI: 10.1007/10_2016_12] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.
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Affiliation(s)
- Eva Garcia-Ruiz
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Mohammad HamediRad
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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28
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Swainston N, Dunstan M, Jervis AJ, Robinson CJ, Carbonell P, Williams AR, Faulon JL, Scrutton NS, Kell DB. PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts. Bioinformatics 2018; 34:2327-2329. [DOI: 10.1093/bioinformatics/bty105] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 02/22/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Mark Dunstan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Adrian J Jervis
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Alan R Williams
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Jean-Loup Faulon
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- MICALIS, INRA-AgroParisTech, Domaine de Vilvert, Jouy en Josas Cedex, France
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Douglas B Kell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- School of Chemistry, The University of Manchester, Manchester, UK
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29
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Hossain GS, Nadarajan SP, Zhang L, Ng TK, Foo JL, Ling H, Choi WJ, Chang MW. Rewriting the Metabolic Blueprint: Advances in Pathway Diversification in Microorganisms. Front Microbiol 2018; 9:155. [PMID: 29483901 PMCID: PMC5816047 DOI: 10.3389/fmicb.2018.00155] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 01/23/2018] [Indexed: 11/13/2022] Open
Abstract
Living organisms have evolved over millions of years to fine tune their metabolism to create efficient pathways for producing metabolites necessary for their survival. Advancement in the field of synthetic biology has enabled the exploitation of these metabolic pathways for the production of desired compounds by creating microbial cell factories through metabolic engineering, thus providing sustainable routes to obtain value-added chemicals. Following the past success in metabolic engineering, there is increasing interest in diversifying natural metabolic pathways to construct non-natural biosynthesis routes, thereby creating possibilities for producing novel valuable compounds that are non-natural or without elucidated biosynthesis pathways. Thus, the range of chemicals that can be produced by biological systems can be expanded to meet the demands of industries for compounds such as plastic precursors and new antibiotics, most of which can only be obtained through chemical synthesis currently. Herein, we review and discuss novel strategies that have been developed to rewrite natural metabolic blueprints in a bid to broaden the chemical repertoire achievable in microorganisms. This review aims to provide insights on recent approaches taken to open new avenues for achieving biochemical production that are beyond currently available inventions.
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Affiliation(s)
- Gazi Sakir Hossain
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Saravanan Prabhu Nadarajan
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Lei Zhang
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Tee-Kheang Ng
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Jee Loon Foo
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Hua Ling
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Won Jae Choi
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
- Agency for Science, Technology and Research (ASTAR), Institute of Chemical and Engineering Sciences, Singapore, Singapore
| | - Matthew Wook Chang
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
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Robinson CJ, Dunstan MS, Swainston N, Titchmarsh J, Takano E, Scrutton NS, Jervis AJ. Multifragment DNA Assembly of Biochemical Pathways via Automated Ligase Cycling Reaction. Methods Enzymol 2018; 608:369-392. [DOI: 10.1016/bs.mie.2018.04.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Carbonell P, Koch M, Duigou T, Faulon JL. Enzyme Discovery: Enzyme Selection and Pathway Design. Methods Enzymol 2018; 608:3-27. [PMID: 30173766 DOI: 10.1016/bs.mie.2018.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
In this protocol, we describe in silico design methods that can assist in the engineering of production pathways that are based on enzymatic transformations. The described protocols are the basis for automated processes to be integrated into an iterative Design-Build-Test-Learn cycle in synthetic biology for chemical production. Selecting the right enzyme sequence for a desired biocatalytic activity from the extensive catalogue of sequences available in databases is challenging and can dramatically influence the success of bioproducing chemical compounds. A method for enzyme selection is presented that helps identifying candidate enzyme sequences through a scoring approach that considers not only sequence homology but also reaction similarity. Selecting a viable biochemical pathway for compound production requires screening large sets of reactions in a process involving combinatorial complexity. A method for pathway design using retrosynthesis is presented. The protocol allows the discovery of alternative chemical pathways leading to the final product by using reaction rules of selectable degree of specificity. The protocols can be reversed through clustering discovery and product identification processes. The integration of these protocols into a general pipeline provides a toolbox for enhanced automated synthetic biology design and metabolic engineering.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom; Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France; School of Chemistry, The University of Manchester, Manchester, United Kingdom.
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Delépine B, Duigou T, Carbonell P, Faulon JL. RetroPath2.0: A retrosynthesis workflow for metabolic engineers. Metab Eng 2018; 45:158-170. [DOI: 10.1016/j.ymben.2017.12.002] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 11/03/2017] [Accepted: 12/05/2017] [Indexed: 12/01/2022]
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Ramzi AB. Metabolic Engineering and Synthetic Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1102:81-95. [DOI: 10.1007/978-3-319-98758-3_6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Asplund-Samuelsson J, Janasch M, Hudson EP. Thermodynamic analysis of computed pathways integrated into the metabolic networks of E. coli and Synechocystis reveals contrasting expansion potential. Metab Eng 2017; 45:223-236. [PMID: 29278749 DOI: 10.1016/j.ymben.2017.12.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 12/04/2017] [Accepted: 12/20/2017] [Indexed: 01/09/2023]
Abstract
Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified.
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Affiliation(s)
- Johannes Asplund-Samuelsson
- Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology, P-Box 1031, 171 21 Solna, Sweden.
| | - Markus Janasch
- Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology, P-Box 1031, 171 21 Solna, Sweden.
| | - Elton P Hudson
- Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology, P-Box 1031, 171 21 Solna, Sweden.
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Abstract
Determining the fraction of the chemical space that can be processed in vivo by using natural and synthetic biology devices is crucial for the development of advanced synthetic biology applications. The extended metabolic space is a coding system based on molecular signatures that enables the derivation of reaction rules for metabolic reactions and the enumeration of all possible substrates and products corresponding to the rules. The extended metabolic space expands capabilities for controlling the production, processing, sensing, and the release of specific molecules in chassis organisms.
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Dvořák P, Nikel PI, Damborský J, de Lorenzo V. Bioremediation 3 . 0 : Engineering pollutant-removing bacteria in the times of systemic biology. Biotechnol Adv 2017; 35:845-866. [DOI: 10.1016/j.biotechadv.2017.08.001] [Citation(s) in RCA: 126] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 08/01/2017] [Accepted: 08/04/2017] [Indexed: 01/07/2023]
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Kim SM, Peña MI, Moll M, Bennett GN, Kavraki LE. A review of parameters and heuristics for guiding metabolic pathfinding. J Cheminform 2017; 9:51. [PMID: 29086092 PMCID: PMC5602787 DOI: 10.1186/s13321-017-0239-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 09/07/2017] [Indexed: 12/04/2022] Open
Abstract
Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.
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Affiliation(s)
- Sarah M Kim
- Department of Computer Science, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Matthew I Peña
- Department of BioSciences, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Mark Moll
- Department of Computer Science, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - George N Bennett
- Department of BioSciences, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, 6100 Main St., Houston, TX, 77005, USA.
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38
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Re A. Synthetic Gene Expression Circuits for Designing Precision Tools in Oncology. Front Cell Dev Biol 2017; 5:77. [PMID: 28894736 PMCID: PMC5581392 DOI: 10.3389/fcell.2017.00077] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Accepted: 08/16/2017] [Indexed: 01/21/2023] Open
Abstract
Precision medicine in oncology needs to enhance its capabilities to match diagnostic and therapeutic technologies to individual patients. Synthetic biology streamlines the design and construction of functionalized devices through standardization and rational engineering of basic biological elements decoupled from their natural context. Remarkable improvements have opened the prospects for the availability of synthetic devices of enhanced mechanism clarity, robustness, sensitivity, as well as scalability and portability, which might bring new capabilities in precision cancer medicine implementations. In this review, we begin by presenting a brief overview of some of the major advances in the engineering of synthetic genetic circuits aimed to the control of gene expression and operating at the transcriptional, post-transcriptional/translational, and post-translational levels. We then focus on engineering synthetic circuits as an enabling methodology for the successful establishment of precision technologies in oncology. We describe significant advancements in our capabilities to tailor synthetic genetic circuits to specific applications in tumor diagnosis, tumor cell- and gene-based therapy, and drug delivery.
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Affiliation(s)
- Angela Re
- Centre for Sustainable Future Technologies, Istituto Italiano di TecnologiaTorino, Italy
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39
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Sankar A, Ranu S, Raman K. Predicting novel metabolic pathways through subgraph mining. Bioinformatics 2017; 33:3955-3963. [DOI: 10.1093/bioinformatics/btx481] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/26/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aravind Sankar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Sayan Ranu
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Karthik Raman
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
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40
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Chao R, Mishra S, Si T, Zhao H. Engineering biological systems using automated biofoundries. Metab Eng 2017; 42:98-108. [PMID: 28602523 PMCID: PMC5544601 DOI: 10.1016/j.ymben.2017.06.003] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 05/22/2017] [Accepted: 06/05/2017] [Indexed: 11/19/2022]
Abstract
Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.
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Affiliation(s)
- Ran Chao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Shekhar Mishra
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Tong Si
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Departments of Chemistry, Biochemistry, Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
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41
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O'Hagan S, Kell DB. Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures. J Cheminform 2017; 9:18. [PMID: 28316656 PMCID: PMC5344883 DOI: 10.1186/s13321-017-0198-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 02/09/2017] [Indexed: 12/21/2022] Open
Abstract
In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites.
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Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| | - Douglas B Kell
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Centre for the Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
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42
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Synthetic metabolism: metabolic engineering meets enzyme design. Curr Opin Chem Biol 2017; 37:56-62. [PMID: 28152442 DOI: 10.1016/j.cbpa.2016.12.023] [Citation(s) in RCA: 149] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 12/15/2016] [Accepted: 12/16/2016] [Indexed: 01/29/2023]
Abstract
Metabolic engineering aims at modifying the endogenous metabolic network of an organism to harness it for a useful biotechnological task, for example, production of a value-added compound. Several levels of metabolic engineering can be defined and are the topic of this review. Basic 'copy, paste and fine-tuning' approaches are limited to the structure of naturally existing pathways. 'Mix and match' approaches freely recombine the repertoire of existing enzymes to create synthetic metabolic networks that are able to outcompete naturally evolved pathways or redirect flux toward non-natural products. The space of possible metabolic solution can be further increased through approaches including 'new enzyme reactions', which are engineered on the basis of known enzyme mechanisms. Finally, by considering completely 'novel enzyme chemistries' with de novo enzyme design, the limits of nature can be breached to derive the most advanced form of synthetic pathways. We discuss the challenges and promises associated with these different metabolic engineering approaches and illuminate how enzyme engineering is expected to take a prime role in synthetic metabolic engineering for biotechnology, chemical industry and agriculture of the future.
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Zhang Y, Liu D, Chen Z. Production of C2-C4 diols from renewable bioresources: new metabolic pathways and metabolic engineering strategies. BIOTECHNOLOGY FOR BIOFUELS 2017; 10:299. [PMID: 29255482 PMCID: PMC5727944 DOI: 10.1186/s13068-017-0992-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 12/05/2017] [Indexed: 05/17/2023]
Abstract
C2-C4 diols classically derived from fossil resource are very important bulk chemicals which have been used in a wide range of areas, including solvents, fuels, polymers, cosmetics, and pharmaceuticals. Production of C2-C4 diols from renewable resources has received significant interest in consideration of the reducing fossil resource and the increasing environmental issues. While bioproduction of certain diols like 1,3-propanediol has been commercialized in recent years, biosynthesis of many other important C2-C4 diol isomers is highly challenging due to the lack of natural synthesis pathways. Recent advances in synthetic biology have enabled the de novo design of completely new pathways to non-natural molecules from renewable feedstocks. In this study, we review recent advances in bioproduction of C2-C4 diols, focusing on new metabolic pathways and metabolic engineering strategies being developed. We also discuss the challenges and future trends toward the development of economically competitive processes for bio-based diol production.
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Affiliation(s)
- Ye Zhang
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084 China
- Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084 China
- Tsinghua Innovation Center in Dongguan, Dongguan, 523808 China
| | - Dehua Liu
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084 China
- Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084 China
- Tsinghua Innovation Center in Dongguan, Dongguan, 523808 China
- Center of Synthetic and Systems Biology, Tsinghua University, Beijing, 100084 China
| | - Zhen Chen
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084 China
- Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084 China
- Tsinghua Innovation Center in Dongguan, Dongguan, 523808 China
- Center of Synthetic and Systems Biology, Tsinghua University, Beijing, 100084 China
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Carbonell P, Currin A, Jervis AJ, Rattray NJW, Swainston N, Yan C, Takano E, Breitling R. Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle. Nat Prod Rep 2016; 33:925-32. [PMID: 27185383 PMCID: PMC5063057 DOI: 10.1039/c6np00018e] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 12/11/2022]
Abstract
Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Andrew Currin
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Adrian J. Jervis
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Nicholas J. W. Rattray
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Neil Swainston
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Cunyu Yan
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Eriko Takano
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Rainer Breitling
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
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SYNBIOCHEM Synthetic Biology Research Centre, Manchester - A UK foundry for fine and speciality chemicals production. Synth Syst Biotechnol 2016; 1:271-275. [PMID: 29062953 PMCID: PMC5625740 DOI: 10.1016/j.synbio.2016.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 07/08/2016] [Accepted: 07/11/2016] [Indexed: 11/21/2022] Open
Abstract
The UK Synthetic Biology Research Centre, SYNBIOCHEM, hosted by the Manchester Institute of Biotechnology at the University of Manchester is delivering innovative technology platforms to facilitate the predictable engineering of microbial bio-factories for fine and speciality chemicals production. We provide an overview of our foundry activities that are being applied to grand challenge projects to deliver innovation in bio-based chemicals production for industrial biotechnology.
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46
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Carbonell P, Gök A, Shapira P, Faulon JL. Mapping the patent landscape of synthetic biology for fine chemical production pathways. Microb Biotechnol 2016; 9:687-95. [PMID: 27489206 PMCID: PMC4993189 DOI: 10.1111/1751-7915.12401] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 07/13/2016] [Indexed: 12/01/2022] Open
Abstract
A goal of synthetic biology bio‐foundries is to innovate through an iterative design/build/test/learn pipeline. In assessing the value of new chemical production routes, the intellectual property (IP) novelty of the pathway is important. Exploratory studies can be carried using knowledge of the patent/IP landscape for synthetic biology and metabolic engineering. In this paper, we perform an assessment of pathways as potential targets for chemical production across the full catalogue of reachable chemicals in the extended metabolic space of chassis organisms, as computed by the retrosynthesis‐based algorithm RetroPath. Our database for reactions processed by sequences in heterologous pathways was screened against the PatSeq database, a comprehensive collection of more than 150M sequences present in patent grants and applications. We also examine related patent families using Derwent Innovations. This large‐scale computational study provides useful insights into the IP landscape of synthetic biology for fine and specialty chemicals production.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Abdullah Gök
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.,Manchester Institute of Innovation Research, Alliance Manchester Business School, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Philip Shapira
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.,Manchester Institute of Innovation Research, Alliance Manchester Business School, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.,School of Public Policy, Georgia Institute of Technology, 685 Cherry Street, Atlanta, GA, 30332-0345, USA
| | - Jean-Loup Faulon
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.,MICALIS Institute, INRA, Domaine de Vilvert, 78352, Jouy en Josas Cedex, France
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47
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Delépine B, Libis V, Carbonell P, Faulon JL. SensiPath: computer-aided design of sensing-enabling metabolic pathways. Nucleic Acids Res 2016; 44:W226-31. [PMID: 27106061 PMCID: PMC5741204 DOI: 10.1093/nar/gkw305] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 04/04/2016] [Accepted: 04/12/2016] [Indexed: 12/17/2022] Open
Abstract
Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.
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Affiliation(s)
- Baudoin Delépine
- iSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France
| | - Vincent Libis
- iSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France
| | - Pablo Carbonell
- SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, M1 7DN Manchester, UK
| | - Jean-Loup Faulon
- iSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, M1 7DN Manchester, UK
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48
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Mellor J, Grigoras I, Carbonell P, Faulon JL. Semisupervised Gaussian Process for Automated Enzyme Search. ACS Synth Biol 2016; 5:518-28. [PMID: 27007080 DOI: 10.1021/acssynbio.5b00294] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Synthetic biology is today harnessing the design of novel and greener biosynthesis routes for the production of added-value chemicals and natural products. The design of novel pathways often requires a detailed selection of enzyme sequences to import into the chassis at each of the reaction steps. To address such design requirements in an automated way, we present here a tool for exploring the space of enzymatic reactions. Given a reaction and an enzyme the tool provides a probability estimate that the enzyme catalyzes the reaction. Our tool first considers the similarity of a reaction to known biochemical reactions with respect to signatures around their reaction centers. Signatures are defined based on chemical transformation rules by using extended connectivity fingerprint descriptors. A semisupervised Gaussian process model associated with the similar known reactions then provides the probability estimate. The Gaussian process model uses information about both the reaction and the enzyme in providing the estimate. These estimates were validated experimentally by the application of the Gaussian process model to a newly identified metabolite in Escherichia coli in order to search for the enzymes catalyzing its associated reactions. Furthermore, we show with several pathway design examples how such ability to assign probability estimates to enzymatic reactions provides the potential to assist in bioengineering applications, providing experimental validation to our proposed approach. To the best of our knowledge, the proposed approach is the first application of Gaussian processes dealing with biological sequences and chemicals, the use of a semisupervised Gaussian process framework is also novel in the context of machine learning applied to bioinformatics. However, the ability of an enzyme to catalyze a reaction depends on the affinity between the substrates of the reaction and the enzyme. This affinity is generally quantified by the Michaelis constant KM. Therefore, we also demonstrate using Gaussian process regression to predict KM given a substrate-enzyme pair.
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Affiliation(s)
- Joseph Mellor
- School
of Chemistry, University of Manchester, Manchester M13 9PL, U.K
- Manchester
Institute of Biotechnology, University of Manchester, Manchester M13 9PL, U.K
| | - Ioana Grigoras
- iSSB,
Institute of Systems and Synthetic Biology, CNRS, University of Évry-Val-d’Essonne, 91000 Évry, France
| | - Pablo Carbonell
- SYNBIOCHEM
Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M13 9PL, U.K
| | - Jean-Loup Faulon
- School
of Chemistry, University of Manchester, Manchester M13 9PL, U.K
- iSSB,
Institute of Systems and Synthetic Biology, CNRS, University of Évry-Val-d’Essonne, 91000 Évry, France
- SYNBIOCHEM
Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M13 9PL, U.K
- MICALIS Institute, INRA, 78352 Jouy en Jossas, France
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49
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Chubukov V, Mukhopadhyay A, Petzold CJ, Keasling JD, Martín HG. Synthetic and systems biology for microbial production of commodity chemicals. NPJ Syst Biol Appl 2016; 2:16009. [PMID: 28725470 PMCID: PMC5516863 DOI: 10.1038/npjsba.2016.9] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 02/01/2016] [Accepted: 02/05/2016] [Indexed: 01/08/2023] Open
Abstract
The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.
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Affiliation(s)
- Victor Chubukov
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jay D Keasling
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Chemical & Biomolecular Engineering, University of California, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Héctor García Martín
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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50
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Petzold CJ, Chan LJG, Nhan M, Adams PD. Analytics for Metabolic Engineering. Front Bioeng Biotechnol 2015; 3:135. [PMID: 26442249 PMCID: PMC4561385 DOI: 10.3389/fbioe.2015.00135] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 08/24/2015] [Indexed: 12/20/2022] Open
Abstract
Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.
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Affiliation(s)
- Christopher J Petzold
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA
| | - Leanne Jade G Chan
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA
| | - Melissa Nhan
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA
| | - Paul D Adams
- Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA ; Department of Bioengineering, University of California Berkeley , Berkeley, CA , USA
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