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Boob AG, Chen J, Zhao H. Enabling pathway design by multiplex experimentation and machine learning. Metab Eng 2024; 81:70-87. [PMID: 38040110 DOI: 10.1016/j.ymben.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/01/2023] [Accepted: 11/25/2023] [Indexed: 12/03/2023]
Abstract
The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.
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Affiliation(s)
- Aashutosh Girish Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Junyu Chen
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
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2
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Levin I, Liu M, Voigt CA, Coley CW. Merging enzymatic and synthetic chemistry with computational synthesis planning. Nat Commun 2022; 13:7747. [PMID: 36517480 DOI: 10.1038/s41467-022-35422-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/30/2022] [Indexed: 12/15/2022] Open
Abstract
Synthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability to leverage rare chemical transformations. This challenge is acute for enzymatic reactions, which are valuable due to their selectivity and sustainability but are few in number. We report a retrosynthetic search algorithm using two neural network models for retrosynthesis-one covering 7984 enzymatic transformations and one 163,723 synthetic transformations-that balances the exploration of enzymatic and synthetic reactions to identify hybrid synthesis plans. This approach extends the space of retrosynthetic moves by thousands of uniquely enzymatic one-step transformations, discovers routes to molecules for which synthetic or enzymatic searches find none, and designs shorter routes for others. Application to (-)-Δ9 tetrahydrocannabinol (THC) (dronabinol) and R,R-formoterol (arformoterol) illustrates how our strategy facilitates the replacement of metal catalysis, high step counts, or costly enantiomeric resolution with more elegant hybrid proposals.
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Cruz-Valdez JA, Patiño-Herrera R, González-Alatorre G, Louvier-Hernández JF, Martínez AA, Perez E. Decrease in CO
2
emissions in obtaining polymer grade propylene by extractive distillation process. Chem Eng Technol 2022. [DOI: 10.1002/ceat.202200110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jesús Alonso Cruz-Valdez
- Centro de Investigación y Estudios de Posgrado (CIEP), Facultad de Ciencias Químicas niversidad Autónoma de San Luis Potosí Av. Dr. Manuel Nava #6 – Zona Universitaria San Luis Potosí, S.L.P. 78210 México
| | - Rosalba Patiño-Herrera
- Departamento de Ingeniería Química, Instituto Tecnológico de Celaya Tecnológico Nacional de México Antonio García Cubas Pte #600 esq. Av. Tecnológico Celaya, Guanajuato 38010 México
| | - Guillermo González-Alatorre
- Departamento de Ingeniería Química, Instituto Tecnológico de Celaya Tecnológico Nacional de México Antonio García Cubas Pte #600 esq. Av. Tecnológico Celaya, Guanajuato 38010 México
| | - José Francisco Louvier-Hernández
- Departamento de Ingeniería Química, Instituto Tecnológico de Celaya Tecnológico Nacional de México Antonio García Cubas Pte #600 esq. Av. Tecnológico Celaya, Guanajuato 38010 México
| | - Adriana Avilés Martínez
- Universidad Michoacana de San Nicolás de Hidalgo Calle de Santiago Tapia 403, Centro Morelia, Mich 58000 México
| | - Elías Perez
- Instituto de Física, UASLP Álvaro Obregón #64 San Luis Potosí, S.L.P. 78000 México
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4
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Bourgade B, Humphreys CM, Millard J, Minton NP, Islam MA. Design, Analysis, and Implementation of a Novel Biochemical Pathway for Ethylene Glycol Production in Clostridium autoethanogenum. ACS Synth Biol 2022; 11:1790-1800. [PMID: 35543716 PMCID: PMC9127970 DOI: 10.1021/acssynbio.1c00624] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
![]()
The platform chemical
ethylene glycol (EG) is used to manufacture
various commodity chemicals of industrial importance, but largely
remains synthesized from fossil fuels. Although several novel metabolic
pathways have been reported for its bioproduction in model organisms,
none has been reported for gas-fermenting, non-model acetogenic chassis
organisms. Here, we describe a novel, synthetic biochemical pathway
to convert acetate into EG in the industrially important gas-fermenting
acetogen,Clostridium autoethanogenum. We not only developed a computational workflow to design and analyze
hundreds of novel biochemical pathways for EG production but also
demonstrated a successful pathway construction in the chosen host.
The EG production was achieved using a two-plasmid system to bypass
unfeasible expression levels and potential toxic enzymatic interactions.
Although only a yield of 0.029 g EG/g fructose was achieved and therefore
requiring further strain engineering efforts to optimize the designed
strain, this work demonstrates an important proof-of-concept approach
to computationally design and experimentally implement fully synthetic
metabolic pathways in a metabolically highly specific, non-model host
organism.
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Affiliation(s)
- Barbara Bourgade
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, U.K
| | - Christopher M. Humphreys
- BBSRC/EPSRC Synthetic Biology Research Centre, Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, U.K
| | - James Millard
- BBSRC/EPSRC Synthetic Biology Research Centre, Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, U.K
| | - Nigel P. Minton
- BBSRC/EPSRC Synthetic Biology Research Centre, Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, U.K
| | - M. Ahsanul Islam
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, U.K
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5
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Sveshnikova A, Mohammadipeyhani H, Hatzimanikatis V. ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds. Metab Eng 2022. [DOI: 10.1016/j.ymben.2022.03.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/15/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022]
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6
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MohammadiPeyhani H, Hafner J, Sveshnikova A, Viterbo V, Hatzimanikatis V. Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx. Nat Commun 2022; 13:1560. [PMID: 35322036 DOI: 10.1038/s41467-022-29238-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/07/2022] [Indexed: 12/23/2022] Open
Abstract
Metabolic “dark matter” describes currently unknown metabolic processes, which form a blind spot in our general understanding of metabolism and slow down the development of biosynthetic cell factories and naturally derived pharmaceuticals. Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 489 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of biochemical pathways and evaluates the biochemical vicinity of molecule classes (https://lcsb-databases.epfl.ch/Atlas2). “Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. Here the authors present ATLASx, a repository of known and predicted enzymatic reaction, connecting millions of compounds to help synthetic biologists and metabolic engineers to design and explore metabolic pathways.”
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MohammadiPeyhani H, Chiappino-Pepe A, Haddadi K, Hafner J, Hadadi N, Hatzimanikatis V. NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism. eLife 2021; 10:e65543. [PMID: 34340747 PMCID: PMC8331181 DOI: 10.7554/elife.65543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 07/07/2021] [Indexed: 12/30/2022] Open
Abstract
The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.
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Affiliation(s)
- Homa MohammadiPeyhani
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Anush Chiappino-Pepe
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Kiandokht Haddadi
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Jasmin Hafner
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Noushin Hadadi
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
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8
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9
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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10
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Hafner J, Payne J, MohammadiPeyhani H, Hatzimanikatis V, Smolke C. A computational workflow for the expansion of heterologous biosynthetic pathways to natural product derivatives. Nat Commun 2021; 12:1760. [PMID: 33741955 PMCID: PMC7979880 DOI: 10.1038/s41467-021-22022-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 02/24/2021] [Indexed: 01/31/2023] Open
Abstract
Plant natural products (PNPs) and their derivatives are important but underexplored sources of pharmaceutical molecules. To access this untapped potential, the reconstitution of heterologous PNP biosynthesis pathways in engineered microbes provides a valuable starting point to explore and produce novel PNP derivatives. Here, we introduce a computational workflow to systematically screen the biochemical vicinity of a biosynthetic pathway for pharmaceutical compounds that could be produced by derivatizing pathway intermediates. We apply our workflow to the biosynthetic pathway of noscapine, a benzylisoquinoline alkaloid (BIA) with a long history of medicinal use. Our workflow identifies pathways and enzyme candidates for the production of (S)-tetrahydropalmatine, a known analgesic and anxiolytic, and three additional derivatives. We then construct pathways for these compounds in yeast, resulting in platforms for de novo biosynthesis of BIA derivatives and demonstrating the value of cheminformatic tools to predict reactions, pathways, and enzymes in synthetic biology and metabolic engineering.
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Affiliation(s)
- Jasmin Hafner
- Laboratory of Computational Systems Biotechnology, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - James Payne
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Homa MohammadiPeyhani
- Laboratory of Computational Systems Biotechnology, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
| | - Christina Smolke
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, USA.
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11
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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12
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Hafner J, MohammadiPeyhani H, Sveshnikova A, Scheidegger A, Hatzimanikatis V. Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power. ACS Synth Biol 2020; 9:1479-1482. [PMID: 32421310 PMCID: PMC7309321 DOI: 10.1021/acssynbio.0c00052] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The ATLAS of Biochemistry is a repository of both known and novel predicted biochemical reactions between biological compounds listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG). ATLAS was originally compiled based on KEGG 2015, though the number of KEGG reactions has increased by almost 20 percent since then. Here, we present an updated version of ATLAS created from KEGG 2018 using an increased set of generalized reaction rules. Furthermore, we improved the accuracy of the enzymes that are predicted for catalyzing novel reactions. ATLAS now contains ∼150 000 reactions, out of which 96% are novel. In this report, we present detailed statistics on the updated ATLAS and highlight the improvements with regard to the previous version. Most importantly, 107 reactions predicted in the original ATLAS are now known to KEGG, which validates the predictive power of our approach. The updated ATLAS is available at https://lcsb-databases.epfl.ch/atlas.
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Affiliation(s)
- Jasmin Hafner
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Homa MohammadiPeyhani
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Anastasia Sveshnikova
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Alan Scheidegger
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
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13
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Duigou T, du Lac M, Carbonell P, Faulon JL. RetroRules: a database of reaction rules for engineering biology. Nucleic Acids Res 2020; 47:D1229-D1235. [PMID: 30321422 PMCID: PMC6323975 DOI: 10.1093/nar/gky940] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 10/09/2018] [Indexed: 01/03/2023] Open
Abstract
RetroRules is a database of reaction rules for metabolic engineering (https://retrorules.org). Reaction rules are generic descriptions of chemical reactions that can be used in retrosynthesis workflows in order to enumerate all possible biosynthetic routes connecting a target molecule to its precursors. The use of such rules is becoming increasingly important in the context of synthetic biology applied to de novo pathway discovery and in systems biology to discover underground metabolism due to enzyme promiscuity. Here, we provide for the first time a complete set containing >400 000 stereochemistry-aware reaction rules extracted from public databases and expressed in the community-standard SMARTS (SMIRKS) format, augmented by a rule representation at different levels of specificity (the atomic environment around the reaction center). Such numerous representations of reactions expand natural chemical diversity by predicting de novo reactions of promiscuous enzymes.
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Affiliation(s)
- Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Melchior du Lac
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Pablo Carbonell
- SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France.,SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.,CNRS-UMR8030/Laboratoire iSSB, Université Paris-Saclay, Évry 91000, France
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14
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Tokic M, Hatzimanikatis V, Miskovic L. Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies. Biotechnol Biofuels 2020; 13:33. [PMID: 32140178 PMCID: PMC7048048 DOI: 10.1186/s13068-020-1665-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 01/22/2020] [Indexed: 05/15/2023]
Abstract
BACKGROUND Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. RESULTS In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. CONCLUSIONS The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.
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Affiliation(s)
- Milenko Tokic
- Laboratory of Computational Systems Biotechnology (LCSB), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biotechnology (LCSB), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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15
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Abstract
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.
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Affiliation(s)
- Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
- iSSB Laboratory, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057 Evry, France
- SYNBIOCHEM Center, School of Chemistry, University of Manchester, Manchester M13 9PL, U.K
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16
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Chen Y, Banerjee D, Mukhopadhyay A, Petzold CJ. Systems and synthetic biology tools for advanced bioproduction hosts. Curr Opin Biotechnol 2020; 64:101-109. [PMID: 31927061 DOI: 10.1016/j.copbio.2019.12.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 11/27/2019] [Accepted: 12/08/2019] [Indexed: 02/07/2023]
Abstract
The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.
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Affiliation(s)
- Yan Chen
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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17
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Blank LM, Narancic T, Mampel J, Tiso T, O'Connor K. Biotechnological upcycling of plastic waste and other non-conventional feedstocks in a circular economy. Curr Opin Biotechnol 2019; 62:212-219. [PMID: 31881445 DOI: 10.1016/j.copbio.2019.11.011] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 10/01/2019] [Accepted: 11/13/2019] [Indexed: 02/07/2023]
Abstract
The envisaged circular economy requires absolute carbon efficiency and in the long run abstinence from fossil feedstocks, and integration of industrial production with end-of-life waste management. Non-conventional feedstocks arising from industrial production and societal consumption such as CO2 and plastic waste may soon enable manufacture of multiple products from simple bulk chemicals to pharmaceuticals using biotechnology. The change to these feedstocks could be faster than expected by many, especially if the true cost, including the carbon footprint of products, is considered. The efficiency of biotechnological processes can be improved through metabolic engineering, which can help fulfill the promises of the Paris agreement.
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Affiliation(s)
- Lars Mathias Blank
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany.
| | - Tanja Narancic
- BEACON SFI Bioeconomy Research Centre and School of Biomolecular and Biomedical Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Jörg Mampel
- BRAIN AG, Darmstädter Str. 34-36, 64673 Zwingenberg, Germany
| | - Till Tiso
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany
| | - Kevin O'Connor
- BEACON SFI Bioeconomy Research Centre and School of Biomolecular and Biomedical Science, University College Dublin, Belfield, Dublin 4, Ireland
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