1
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Ji Y, Xu J, Zhu L. Redox potential model for guiding moderate oxidation of polycyclic aromatic hydrocarbons in soils. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134443. [PMID: 38678701 DOI: 10.1016/j.jhazmat.2024.134443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/01/2024]
Abstract
In-situ chemical oxidation is an important approach to remediate soils contaminated with persistent organic pollutants, e.g., polycyclic aromatic hydrocarbons (PAHs). However, massive oxidants are added into soils without an explicit model for predicting the redox potential (Eh) during soil remediation, and overdosed oxidants would pose secondary damage by disturbing soil organic matter and acidity. Here, a soil redox potential (Eh) model was first established to quantify the relationship among oxidation parameters, crucial soil properties, and pollutant elimination. The impacts of oxidant types and doses, soil pH, and soil organic carbon contents on soil Eh were systematically clarified in four commonly used oxidation systems (i.e., KMnO4, H2O2, fenton, and persulfate). The relative error of preliminary Eh model was increased from 48-62% to 4-16% after being modified with the soil texture and dissolved organic carbon, and this high accuracy was verified by 12 actual PAHs contaminated soils. Combining the discovered critical oxidation potential (COP) of PAHs, the moderate oxidation process could be regulated by the guidance of the soil Eh model in different soil conditions. Moreover, the product analysis revealed that the hydroxylation of PAHs occurred most frequently when the soil Eh reached their COP, providing a foundation for further microorganism remediation. These results provide a feasible strategy for selecting oxidants and controlling their doses toward moderate oxidation of contaminated soils, which will reduce the consumption of soil organic matter and protect the main structure and function of soil for future utilization. ENVIRONMENTAL IMPLICATIONS: This study provides a novel insight into the moderate chemical oxidation by the Eh model and largely reduces the secondary risks of excessive oxidation and oxidant residual in ISCO. The moderate oxidation of PAHs could be a first step to decrease their toxicity and increase their bioaccessibility, favoring the microbial degradation of PAHs. Controlling the soil Eh with the established model here could be a promising approach to couple moderate oxidation of organic contaminants with microbial degradation. Such an effective and green soil remediation will largely preserve the soil's functional structure and favor the subsequent utilization of remediated soil.
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Affiliation(s)
- Yanping Ji
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Jiang Xu
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Lizhong Zhu
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China.
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2
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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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3
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Shi Z, Wang D, Li Y, Deng R, Lin J, Liu C, Li H, Wang R, Zhao M, Mao Z, Yuan Q, Liao X, Ma H. REME: an integrated platform for reaction enzyme mining and evaluation. Nucleic Acids Res 2024:gkae405. [PMID: 38769057 DOI: 10.1093/nar/gkae405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/16/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
A key challenge in pathway design is finding proper enzymes that can be engineered to catalyze a non-natural reaction. Although existing tools can identify potential enzymes based on similar reactions, these tools encounter several issues. Firstly, the calculated similar reactions may not even have the same reaction type. Secondly, the associated enzymes are often numerous and identifying the most promising candidate enzymes is difficult due to the lack of data for evaluation. Thirdly, existing web tools do not provide interactive functions that enable users to fine-tune results based on their expertise. Here, we present REME (https://reme.biodesign.ac.cn/), the first integrated web platform for reaction enzyme mining and evaluation. Combining atom-to-atom mapping, atom type change identification, and reaction similarity calculation enables quick ranking and visualization of reactions similar to an objective non-natural reaction. Additional functionality enables users to filter similar reactions by their specified functional groups and candidate enzymes can be further filtered (e.g. by organisms) or expanded by Enzyme Commission number (EC) or sequence homology. Afterward, enzyme attributes (such as kcat, Km, optimal temperature and pH) can be assessed with deep learning-based methods, facilitating the swift identification of potential enzymes that can catalyze the non-natural reaction.
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Affiliation(s)
- Zhenkun Shi
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Dehang Wang
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, PR China
| | - Yang Li
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
- University of Chinese Academy of Sciences, Beijing 101408, PR China
| | - Rui Deng
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, PR China
| | - Jiawei Lin
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, PR China
| | - Cui Liu
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Haoran Li
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Ruoyu Wang
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Muqiang Zhao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Zhitao Mao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR 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, PR China
| | - Xiaoping Liao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
- Haihe Laboratory of Synthetic Biology, Tianjin 300308, PR 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, PR China
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Qian Z, Yang H, Li J, Peng T, Huang T, Hu Z. The unique biodegradation pathway of benzo[a]pyrene in moderately halophilic Pontibacillus chungwhensis HN14. CHEMOSPHERE 2024; 354:141705. [PMID: 38494000 DOI: 10.1016/j.chemosphere.2024.141705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/17/2024] [Accepted: 03/11/2024] [Indexed: 03/19/2024]
Abstract
Benzo[a]pyrene (BaP), as the typical representative of polycyclic aromatic hydrocarbons (PAHs), is a serious hazard to human health and natural environments. Though the study of microbial degradation of PAHs has persisted for decades, the degradation pathway of BaP is still unclear. Previously, Pontibacillus chungwhensis HN14 was isolated from high salinity environment exhibiting a high BaP degradation ability. Here, based on the intermediates identified, BaP was found to be transformed to 4,5-epoxide-BaP, BaP-trans-4,5-dihydrodiol, 1,2-dihydroxy-phenanthrene, 2-carboxy-1-naphthol, and 4,5-dimethoxybenzo[a]pyrene by the strain HN14. Furthermore, functional genes involved in degradation of BaP were identified using genome and transcriptome data. Heterogeneous co-expression of monooxygenase CYP102(HN14) and epoxide hydrolase EH(HN14) suggested that CYP102(HN14) could transform BaP to 4,5-epoxide-BaP, which was further transformed to BaP-trans-4,5-dihydrodiol by EH(HN14). Moreover, gene cyp102(HN14) knockout was performed using CRISPR/Cas9 gene-editing system which confirmed that CYP102(HN14) play a key role in the initial conversion of BaP. Finally, a novel BaP degradation pathway was constructed in bacteria, which showed BaP could be converted into chrysene, phenanthrene, naphthalene pathways for the first time. These findings enhanced our understanding of microbial degradation process for BaP and suggested the potential of using P. chungwhensis HN14 for bioremediation in PAH-contaminated environments.
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Affiliation(s)
- Zhihui Qian
- Department of Biology, School of Science, Shantou University, Shantou, Guangdong, 515000, China.
| | - Haichen Yang
- Department of Biology, School of Science, Shantou University, Shantou, Guangdong, 515000, China.
| | - Jin Li
- Department of Biology, School of Science, Shantou University, Shantou, Guangdong, 515000, China; College of Life Sciences, China West Normal University, Nanchong, Sichuan, 637002, China
| | - Tao Peng
- Department of Biology, School of Science, Shantou University, Shantou, Guangdong, 515000, China
| | - Tongwang Huang
- Department of Biology, School of Science, Shantou University, Shantou, Guangdong, 515000, China.
| | - Zhong Hu
- Department of Biology, School of Science, Shantou University, Shantou, Guangdong, 515000, China; Guangdong Research Center of Offshore Environmental Pollution Control Engineering, Shantou University, Shantou, Guangdong, 515063, China.
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5
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Yang H, Qian Z, Liu Y, Yu F, Huang T, Zhang B, Peng T, Hu Z. Comparative genomics reveals evidence of polycyclic aromatic hydrocarbon degradation in the moderately halophilic genus Pontibacillus. JOURNAL OF HAZARDOUS MATERIALS 2024; 462:132724. [PMID: 37839372 DOI: 10.1016/j.jhazmat.2023.132724] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/22/2023] [Accepted: 10/04/2023] [Indexed: 10/17/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a common class of persistent organic pollutants (POPs) that are widely distributed in various environments and pose significant threats to both environmental and human health. The genus Pontibacillus, a type of moderately halophilic bacteria, has demonstrated potential for biodegrading aromatic compounds in high-salinity environments. However, no previous study has comprehensively investigated the PAH degradation mechanisms and environmental adaptability in the genus Pontibacillus. In this study, we sequenced the whole genome of the PAH-degrading strain Pontibacillus chungwhensis HN14 and conducted a comparative genomics analysis of genes associated with PAH degradation, as well as salt and arsenic tolerance using ten other Pontibacillus sp. strains. Here, we elucidated potential degradation pathways for benzo[a]pyrene and phenanthrene, which were initiated by cytochrome P450 monooxygenases, in most Pontibacillus strains. Moreover, four Pontibacillus strains were selected to investigate the biodegradation of benzo[a]pyrene and phenanthrene under high-salt (5% NaCl) stress, and all four strains exhibited exceptional degradation abilities. The results of comparative genomics and phenotypic analyses demonstrate that the genus Pontibacillus have the potential to degrade polycyclic aromatic hydrocarbons in high-salinity environments, thus providing valuable insights for biodegradation in extreme environments.
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Affiliation(s)
- Haichen Yang
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China
| | - Zhihui Qian
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China
| | - Yongjin Liu
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China
| | - Fei Yu
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China
| | - Tongwang Huang
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China
| | - Bing Zhang
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China
| | - Tao Peng
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China.
| | - Zhong Hu
- Department of Biology, Shantou University, Shantou, Guangdong 515063, PR China; Guangdong Research Center of Offshore Environmental Pollution Control Engineering, Shantou University, Shantou 515063, Guangdong, PR China.
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6
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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] [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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7
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Chainani Y, Bonnanzio G, Tyo KE, Broadbelt LJ. Coupling chemistry and biology for the synthesis of advanced bioproducts. Curr Opin Biotechnol 2023; 84:102992. [PMID: 37688985 DOI: 10.1016/j.copbio.2023.102992] [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: 06/15/2023] [Revised: 07/30/2023] [Accepted: 08/05/2023] [Indexed: 09/11/2023]
Abstract
Chemical and biological syntheses can both lead to a myriad of compounds. Biology enables us to harness the metabolism of microbial cell factories to produce key target molecules from renewable biomass-derived substrates. Although bio-based feedstocks are sustainably sourced and more benign than the rapidly depleting fossil fuels that chemical processes have historically relied on, limiting pathways solely to biological reactions may not equate to a greener process overall. In fact, bioreactors rely on substantial quantities of water and can be inefficient since organisms typically operate around ambient conditions and are sensitive to perturbations in their environment. Hybridizing biosynthetic pathways with green chemistry can instead be a more potent strategy to reduce our net manufacturing footprint. Emerging chemistries have demonstrated considerable success in performing complex transformations on biological feedstocks without significant solvent use. Many of these transformations would be too slow to perform enzymatically or infeasible altogether. Here, we put forth the concept that by carefully considering the merits and drawbacks of synthetic biology and chemistry as well as one's own use case, there exist many opportunities for coupling the two. Merging these syntheses can unlock a wider suite of functional group transformations, thereby enabling future manufacturing processes to sustainably access a larger space of valuable, platform chemicals.
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Affiliation(s)
- Yash Chainani
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
| | - Geoffrey Bonnanzio
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
| | - Keith Ej Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, USA.
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8
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Hwang HG, Ye DY, Jung GY. Biosensor-guided discovery and engineering of metabolic enzymes. Biotechnol Adv 2023; 69:108251. [PMID: 37690614 DOI: 10.1016/j.biotechadv.2023.108251] [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: 05/08/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/12/2023]
Abstract
A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.
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Affiliation(s)
- Hyun Gyu Hwang
- Institute of Environmental and Energy Technology, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea
| | - Dae-Yeol Ye
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea
| | - Gyoo Yeol Jung
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea; School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea.
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9
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Probst D. An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification. J Cheminform 2023; 15:113. [PMID: 37996942 PMCID: PMC10668483 DOI: 10.1186/s13321-023-00784-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023] Open
Abstract
Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for organic reactions amendable to biocatalysis. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.
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Affiliation(s)
- Daniel Probst
- Signal Processing Laboratory 2, Institute of Electrical and Micro Engineering, School of Engineering, EPFL, Rte Cantonale, 1015, Lausanne, Vaud, Switzerland.
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10
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Ryu G, Kim GB, Yu T, Lee SY. Deep learning for metabolic pathway design. Metab Eng 2023; 80:130-141. [PMID: 37734652 DOI: 10.1016/j.ymben.2023.09.012] [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: 08/19/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023]
Abstract
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.
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Affiliation(s)
- Gahyeon Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Taeho Yu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon, 34141, Republic of Korea.
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11
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Lee GH, Kim DW, Jin YH, Kim SM, Lim ES, Cha MJ, Ko JK, Gong G, Lee SM, Um Y, Han SO, Ahn JH. Biotechnological Plastic Degradation and Valorization Using Systems Metabolic Engineering. Int J Mol Sci 2023; 24:15181. [PMID: 37894861 PMCID: PMC10607142 DOI: 10.3390/ijms242015181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Various kinds of plastics have been developed over the past century, vastly improving the quality of life. However, the indiscriminate production and irresponsible management of plastics have led to the accumulation of plastic waste, emerging as a pressing environmental concern. To establish a clean and sustainable plastic economy, plastic recycling becomes imperative to mitigate resource depletion and replace non-eco-friendly processes, such as incineration. Although chemical and mechanical recycling technologies exist, the prevalence of composite plastics in product manufacturing complicates recycling efforts. In recent years, the biodegradation of plastics using enzymes and microorganisms has been reported, opening a new possibility for biotechnological plastic degradation and bio-upcycling. This review provides an overview of microbial strains capable of degrading various plastics, highlighting key enzymes and their role. In addition, recent advances in plastic waste valorization technology based on systems metabolic engineering are explored in detail. Finally, future perspectives on systems metabolic engineering strategies to develop a circular plastic bioeconomy are discussed.
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Affiliation(s)
- Ga Hyun Lee
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Do-Wook Kim
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Yun Hui Jin
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Sang Min Kim
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Eui Seok Lim
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Min Ji Cha
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Ja Kyong Ko
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Division of Energy and Environment Technology, KIST School, University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Gyeongtaek Gong
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Division of Energy and Environment Technology, KIST School, University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Sun-Mi Lee
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Division of Energy and Environment Technology, KIST School, University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Youngsoon Um
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Division of Energy and Environment Technology, KIST School, University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Sung Ok Han
- Department of Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Jung Ho Ahn
- Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Division of Energy and Environment Technology, KIST School, University of Science and Technology (UST), Daejeon 34113, Republic of Korea
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12
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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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13
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Tan Z, Li J, Hou J, Gonzalez R. Designing artificial pathways for improving chemical production. Biotechnol Adv 2023; 64:108119. [PMID: 36764336 DOI: 10.1016/j.biotechadv.2023.108119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Abstract
Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.
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Affiliation(s)
- Zaigao Tan
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Department of Bioengineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jian Li
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Department of Bioengineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jin Hou
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, China
| | - Ramon Gonzalez
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA.
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14
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Recent progress in the synthesis of advanced biofuel and bioproducts. Curr Opin Biotechnol 2023; 80:102913. [PMID: 36854202 DOI: 10.1016/j.copbio.2023.102913] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/27/2023]
Abstract
Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. This opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass toward a broader consideration of how to reduce carbon dioxide to fuels and chemical products.
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15
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Shebek KM, Strutz J, Broadbelt LJ, Tyo KEJ. Pickaxe: a Python library for the prediction of novel metabolic reactions. BMC Bioinformatics 2023; 24:106. [PMID: 36949401 PMCID: PMC10031857 DOI: 10.1186/s12859-023-05149-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/13/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. RESULTS Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. CONCLUSION Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.
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Affiliation(s)
- Kevin M Shebek
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Jonathan Strutz
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA.
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA.
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16
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Yu T, Boob AG, Volk MJ, Liu X, Cui H, Zhao H. Machine learning-enabled retrobiosynthesis of molecules. Nat Catal 2023. [DOI: 10.1038/s41929-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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17
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Lim PK, Julca I, Mutwil M. Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data. Comput Struct Biotechnol J 2023; 21:1639-1650. [PMID: 36874159 PMCID: PMC9976193 DOI: 10.1016/j.csbj.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.
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Affiliation(s)
- Peng Ken Lim
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Irene Julca
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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18
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Sieow BFL, De Sotto R, Seet ZRD, Hwang IY, Chang MW. Synthetic Biology Meets Machine Learning. Methods Mol Biol 2023; 2553:21-39. [PMID: 36227537 DOI: 10.1007/978-1-0716-2617-7_2] [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: 06/16/2023]
Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
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Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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19
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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20
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He F, Li X, Huo C, Chu S, Cui Z, Li Y, Wan J, Liu R. Evaluation of fluorene-caused ecotoxicological responses and the mechanism underlying its toxicity in Eisenia fetida: Multi-level analysis of biological organization. JOURNAL OF HAZARDOUS MATERIALS 2022; 437:129342. [PMID: 35716570 DOI: 10.1016/j.jhazmat.2022.129342] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/04/2022] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
Fluorene is an important toxic chemical that exists ubiquitously in the environment, and it has also been suggested to exert potential deleterious effects on soil invertebrates. However, knowledge about the toxic effects of fluorene and its underlying mechanisms of the effects on key soil organism earthworms remains limited. From this view point, this study was undertaken to explore the potential effects of fluorene and its underlying mechanisms in Eisenia fetida at the level of experimental animals, tissue, cell, and molecule. It was concluded that fluorene exerted lethal activity to adult E. fetida on day 14 with the LC50 determined to be 88.61 mg/kg. Fluorene-induced ROS caused oxidative stress in E. fetida, resulting in DNA damage, protein carbonylation, and lipid peroxidation. Moreover, changed antioxidative enzymatic activities, non-enzymatic antioxidative activities, and total antioxidative capacity in E. fetida by fluorene stress are associated with antioxidative and protective effects. High-dose fluorene (> 2.5 mg/kg) exposure significantly caused histopathological lesions including the microstructure of body wall, intestine, and seminal vesicle of earthworms. Also, the reproductive system of E. fetida was clearly disrupted by fluorene stress, leading to poor reproduction ability (decreased cocoon and juvenile production) in earthworms. It is found that E. fetida growth was significantly inhibited when treated with high-dose fluorene, thereby causing normal growth disorders. Additionally, fluorene stress triggered the abnormal mRNA expression related to oxidative stress (e.g., metallothionein and heat shock protein 70), growth (translationally controlled tumour protein), reproduction (annetocin precursor) in E. fetida. Together, both high-dose and long-term exposure elicited more severe poisoning effects on earthworms using the Integrated Biological Response (IBR) index, and E. fetida coelomocyte DNA was the most negatively affected by fluorene stress. This study comprehensively evaluated fluorene-induced toxicity in E. fetida, and its underlying molecular mechanisms mediating the toxic responses have been elucidated. These findings provide valuable data for assessing potential ecological risks posed by fluorene-contaminated soil.
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Affiliation(s)
- Falin He
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China
| | - Xiangxiang Li
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China
| | - Chengqian Huo
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China
| | - Shanshan Chu
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China
| | - Zhihan Cui
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China
| | - Yuze Li
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China
| | - Jingqiang Wan
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China
| | - Rutao Liu
- School of Environmental Science and Engineering, Shandong University, China-America CRC for Environment & Health, Shandong Province, 72# Jimo Binhai Road, Qingdao, Shandong 266237, PR China.
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21
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The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering. Nat Commun 2022; 13:5082. [PMID: 36038542 PMCID: PMC9424320 DOI: 10.1038/s41467-022-32661-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/11/2022] [Indexed: 11/27/2022] Open
Abstract
Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.
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22
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Cho JS, Kim GB, Eun H, Moon CW, Lee SY. Designing Microbial Cell Factories for the Production of Chemicals. JACS AU 2022; 2:1781-1799. [PMID: 36032533 PMCID: PMC9400054 DOI: 10.1021/jacsau.2c00344] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/26/2022] [Accepted: 07/26/2022] [Indexed: 05/24/2023]
Abstract
The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.
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Affiliation(s)
- Jae Sung Cho
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
- BioProcess
Engineering Research Center and BioInformatics Research Center, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Hyunmin Eun
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Cheon Woo Moon
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
- BioProcess
Engineering Research Center and BioInformatics Research Center, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
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23
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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: 0] [Impact Index Per Article: 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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24
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ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds. Metab Eng 2022; 72:259-274. [DOI: 10.1016/j.ymben.2022.03.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [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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25
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Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx. Nat Commun 2022; 13:1560. [PMID: 35322036 PMCID: PMC8943196 DOI: 10.1038/s41467-022-29238-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [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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26
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Huang C, Wang C, Luo Y. Research progress of pathway and genome evolution in microbes. Synth Syst Biotechnol 2022; 7:648-656. [PMID: 35224232 PMCID: PMC8857405 DOI: 10.1016/j.synbio.2022.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/23/2021] [Accepted: 01/06/2022] [Indexed: 12/16/2022] Open
Abstract
Microbes can produce valuable natural products widely applied in medicine, food and other important fields. Nevertheless, it is usually challenging to achieve ideal industrial yields due to low production rate and poor toxicity tolerance. Evolution is a constant mutation and adaptation process used to improve strain performance. Generally speaking, the synthesis of natural products in microbes is often intricate, involving multiple enzymes or multiple pathways. Individual evolution of a certain enzyme often fails to achieve the desired results, and may lead to new rate-limiting nodes that affect the growth of microbes. Therefore, it is inevitable to evolve the biosynthetic pathways or the whole genome. Here, we reviewed the pathway-level evolution including multi-enzyme evolution, regulatory elements engineering, and computer-aided engineering, as well as the genome-level evolution based on several tools, such as genome shuffling and CRISPR/Cas systems. Finally, we also discussed the major challenges faced by in vivo evolution strategies and proposed some potential solutions.
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Affiliation(s)
- Chaoqun Huang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China
| | - Chang Wang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China
| | - Yunzi Luo
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China
- Georgia Tech Shenzhen Institute, Tianjin University, Tangxing Road 133, Nanshan District, Shenzhen, 518071, China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, 300072, China
- Corresponding author. Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
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27
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Bansal P, Morgat A, Axelsen KB, Muthukrishnan V, Coudert E, Aimo L, Hyka-Nouspikel N, Gasteiger E, Kerhornou A, Neto TB, Pozzato M, Blatter MC, Ignatchenko A, Redaschi N, Bridge A. Rhea, the reaction knowledgebase in 2022. Nucleic Acids Res 2022; 50:D693-D700. [PMID: 34755880 PMCID: PMC8728268 DOI: 10.1093/nar/gkab1016] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/08/2021] [Accepted: 11/09/2021] [Indexed: 12/15/2022] Open
Abstract
Rhea (https://www.rhea-db.org) is an expert-curated knowledgebase of biochemical reactions based on the chemical ontology ChEBI (Chemical Entities of Biological Interest) (https://www.ebi.ac.uk/chebi). In this paper, we describe a number of key developments in Rhea since our last report in the database issue of Nucleic Acids Research in 2019. These include improved reaction coverage in Rhea, the adoption of Rhea as the reference vocabulary for enzyme annotation in the UniProt knowledgebase UniProtKB (https://www.uniprot.org), the development of a new Rhea website, and the designation of Rhea as an ELIXIR Core Data Resource. We hope that these and other developments will enhance the utility of Rhea as a reference resource to study and engineer enzymes and the metabolic systems in which they function.
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Affiliation(s)
- Parit Bansal
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Anne Morgat
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Kristian B Axelsen
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Venkatesh Muthukrishnan
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Elisabeth Coudert
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Lucila Aimo
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Nevila Hyka-Nouspikel
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Elisabeth Gasteiger
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Arnaud Kerhornou
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Teresa Batista Neto
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Monica Pozzato
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Marie-Claude Blatter
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Alex Ignatchenko
- EMBL-EBI European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicole Redaschi
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Alan Bridge
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
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28
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Heid E, Goldman S, Sankaranarayanan K, Coley CW, Flamm C, Green WH. EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates. J Chem Inf Model 2021; 61:4949-4961. [PMID: 34587449 PMCID: PMC8549070 DOI: 10.1021/acs.jcim.1c00921] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Indexed: 11/29/2022]
Abstract
Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply, and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.
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Affiliation(s)
- Esther Heid
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Samuel Goldman
- Computational
and Systems Biology, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Karthik Sankaranarayanan
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Connor W. Coley
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Christoph Flamm
- Department
of Theoretical Chemistry, University of
Vienna, 1090 Vienna, Austria
| | - William H. Green
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
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29
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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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30
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Jang WD, Kim GB, Kim Y, Lee SY. Applications of artificial intelligence to enzyme and pathway design for metabolic engineering. Curr Opin Biotechnol 2021; 73:101-107. [PMID: 34358728 DOI: 10.1016/j.copbio.2021.07.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/16/2021] [Accepted: 07/17/2021] [Indexed: 01/07/2023]
Abstract
Metabolic engineering for developing industrial strains capable of overproducing bioproducts requires good understanding of cellular metabolism, including metabolic reactions and enzymes. However, metabolic pathways and enzymes involved are still unknown for many products of interest, which presents a key challenge in their biological production. This challenge can be partly overcome by constructing novel biosynthetic pathways through enzyme and pathway design approaches. With the increase in bio-big data, data-driven approaches using artificial intelligence (AI) techniques are allowing more advanced protein and pathway design. In this paper, we review recent studies on AI-aided protein engineering and design, focusing on directed evolution that uses AI approaches to efficiently construct mutant libraries. Also, recent works of AI-aided pathway design strategies, including template-based and template-free approaches, are discussed.
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Affiliation(s)
- Woo Dae Jang
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury, KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Yeji Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury, KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
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32
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Cui S, Lv X, Xu X, Chen T, Zhang H, Liu Y, Li J, Du G, Ledesma-Amaro R, Liu L. Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways. ACS Synth Biol 2021; 10:1587-1597. [PMID: 34213900 DOI: 10.1021/acssynbio.1c00073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.
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Affiliation(s)
- Shixiu Cui
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xianhao Xu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Taichi Chen
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Hongzhi Zhang
- Shandong Runde Biotechnology Co., Ltd., Tai’an 271000, China
| | - Yanfeng Liu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, U.K
| | - Long Liu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
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33
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Mao Y, Yuan Q, Yang X, Liu P, Cheng Y, Luo J, Liu H, Yao Y, Sun H, Cai T, Ma H. Non-natural Aldol Reactions Enable the Design and Construction of Novel One-Carbon Assimilation Pathways in vitro. Front Microbiol 2021; 12:677596. [PMID: 34149668 PMCID: PMC8208507 DOI: 10.3389/fmicb.2021.677596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/04/2021] [Indexed: 12/02/2022] Open
Abstract
Methylotrophs utilizes cheap, abundant one-carbon compounds, offering a promising green, sustainable and economical alternative to current sugar-based biomanufacturing. However, natural one-carbon assimilation pathways come with many disadvantages, such as complicated reaction steps, the need for additional energy and/or reducing power, or loss of CO2, resulting in unsatisfactory biomanufacturing performance. Here, we predicted eight simple, novel and carbon-conserving formaldehyde (FALD) assimilation pathways based on the extended metabolic network with non-natural aldol reactions using the comb-flux balance analysis (FBA) algorithm. Three of these pathways were found to be independent of energy/reducing equivalents, and thus chosen for further experimental verification. Then, two novel aldol reactions, condensing D-erythrose 4-phosphate and glycolaldehyde (GALD) into 2R,3R-stereo allose 6-phosphate by DeoC or 2S,3R-stereo altrose 6-phosphate by TalBF178Y/Fsa, were identified for the first time. Finally, a novel FALD assimilation pathway proceeding via allose 6-phosphate, named as the glycolaldehyde-allose 6-phosphate assimilation (GAPA) pathway, was constructed in vitro with a high carbon yield of 94%. This work provides an elegant paradigm for systematic design of one-carbon assimilation pathways based on artificial aldolase (ALS) reactions, which could also be feasibly adapted for the mining of other metabolic pathways.
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Affiliation(s)
- Yufeng Mao
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Qianqian Yuan
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Xue Yang
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Pi Liu
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Ying Cheng
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science and Technology, Tianjin, China
| | - Jiahao Luo
- Key Laboratory of Systems Bioengineering (Ministry of Education), SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Huanhuan Liu
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science and Technology, Tianjin, China
| | - Yonghong Yao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Hongbing Sun
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Tao Cai
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
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34
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Wang L, Maranas CD. Computationally Prospecting Potential Pathways from Lignin Monomers and Dimers toward Aromatic Compounds. ACS Synth Biol 2021; 10:1064-1076. [PMID: 33877818 DOI: 10.1021/acssynbio.0c00598] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The heterogeneity of the aromatic products originating from lignin catalytic depolymerization remains one of the major challenges associated with lignin valorization. Microbes have evolved catabolic pathways that can funnel heterogeneous intermediates to a few central aromatic products. These aromatic compounds can subsequently undergo intra- or extradiol ring opening to produce value-added chemicals. However, such funneling pathways are only partially characterized for a few organisms such as Sphingobium sp. SYK-6 and Pseudomonas putida KT2440. Herein, we apply the de novo pathway design tool (novoStoic) to computationally prospect possible ways of funneling lignin-derived mono- and biaryls. novoStoic employs reaction rules between molecular moieties to hypothesize de novo conversions by flagging known enzymes that carry out the same biotransformation on the most similar substrate. Both reaction rules and known reactions are then deployed by novoStoic to identify a mass-balanced biochemical network that converts a source to a target metabolite while minimizing the number of de novo steps. We demonstrate the application of novoStoic for (i) designing alternative pathways of funneling S, G, and H lignin monomers, and (ii) exploring cleavage pathways of β-1 and β-β dimers. By exploring the uncharted chemical space afforded by enzyme promiscuity, novoStoic can help predict previously unknown native pathways leveraging enzyme promiscuity and propose new carbon/energy efficient lignin funneling pathways with few heterologous enzymes.
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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35
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Hafner J, Hatzimanikatis V. Finding metabolic pathways in large networks through atom-conserving substrate-product pairs. Bioinformatics 2021; 37:3560-3568. [PMID: 34003971 PMCID: PMC8545321 DOI: 10.1093/bioinformatics/btab368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/22/2021] [Accepted: 05/17/2021] [Indexed: 11/29/2022] Open
Abstract
Motivation Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge. Results Here, we propose the construction of searchable graph representations of metabolic networks. Each reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant–product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks. Availability and implementation https://github.com/EPFL-LCSB/nicepath. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jasmin Hafner
- Laboratory of Computational Systems Biotechnology (LCSB), Institute of Chemical Sciences and Engineering (ISIC), School of Basic Sciences (SB), Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), Institute of Chemical Sciences and Engineering (ISIC), School of Basic Sciences (SB), Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland
- To whom correspondence should be addressed.
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36
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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] [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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37
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Fuji T, Nakazawa S, Ito K. Feasible-metabolic-pathway-exploration technique using chemical latent space. Bioinformatics 2021; 36:i770-i778. [PMID: 33381845 PMCID: PMC8454040 DOI: 10.1093/bioinformatics/btaa809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Motivation Exploring metabolic pathways is one of the key techniques for developing highly
productive microbes for the bioproduction of chemical compounds. To explore feasible
pathways, not only examining a combination of well-known enzymatic reactions but also
finding potential enzymatic reactions that can catalyze the desired structural changes
are necessary. To achieve this, most conventional techniques use manually
predefined-reaction rules, however, they cannot sufficiently find potential reactions
because the conventional rules cannot comprehensively express structural changes before
and after enzymatic reactions. Evaluating the feasibility of the explored pathways is
another challenge because there is no way to validate the reaction possibility of
unknown enzymatic reactions by these rules. Therefore, a technique for comprehensively
capturing the structural changes in enzymatic reactions and a technique for evaluating
the pathway feasibility are still necessary to explore feasible metabolic pathways. Results We developed a feasible-pathway-exploration technique using chemical latent space
obtained from a deep generative model for compound structures. With this technique, an
enzymatic reaction is regarded as a difference vector between the main substrate and the
main product in chemical latent space acquired from the generative model. Features of
the enzymatic reaction are embedded into the fixed-dimensional vector, and it is
possible to express structural changes of enzymatic reactions comprehensively. The
technique also involves differential-evolution-based reaction selection to design
feasible candidate pathways and pathway scoring using neural-network-based
reaction-possibility prediction. The proposed technique was applied to the
non-registered pathways relevant to the production of 2-butanone, and successfully
explored feasible pathways that include such reactions.
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Affiliation(s)
- Taiki Fuji
- Center for Exploratory Research, Research and Development Group , Hitachi, Ltd., Kokubunji-shi, Tokyo 185-8601, Japan
| | - Shiori Nakazawa
- Center for Exploratory Research, Research and Development Group , Hitachi, Ltd., Kokubunji-shi, Tokyo 185-8601, Japan
| | - Kiyoto Ito
- Center for Exploratory Research, Research and Development Group , Hitachi, Ltd., Kokubunji-shi, Tokyo 185-8601, Japan
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38
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Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design. Metab Eng 2021; 65:79-87. [PMID: 33662575 DOI: 10.1016/j.ymben.2021.02.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/05/2021] [Accepted: 02/23/2021] [Indexed: 01/29/2023]
Abstract
Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. We developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.
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39
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Kim Y, Ryu JY, Kim HU, Jang WD, Lee SY. A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis. Biotechnol J 2021; 16:e2000605. [PMID: 33386776 DOI: 10.1002/biot.202000605] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 12/30/2020] [Indexed: 12/29/2022]
Abstract
Retrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective method that can reduce a large number of the initially predicted enzymatic reactions has been needed. Here, we present Deep learning-based Reaction Feasibility Checker (DeepRFC) to classify the feasibility of a given enzymatic reaction with high performance and speed. DeepRFC is designed to receive Simplified Molecular-Input Line-Entry System (SMILES) strings of a reactant pair, which is defined as a substrate and a product of a reaction, as an input, and evaluates whether the input reaction is feasible. A deep neural network is selected for DeepRFC as it leads to better classification performance than five other representative machine learning methods examined. For validation, the performance of DeepRFC is compared with another in-house reaction feasibility checker that uses the concept of reaction similarity. Finally, the use of DeepRFC is demonstrated for the retrobiosynthesis-based design of novel one-carbon assimilation pathways. DeepRFC will allow retrobiosynthesis to be more practical for metabolic engineering applications by efficiently screening a large number of retrobiosynthesis-derived enzymatic reactions. DeepRFC is freely available at https://bitbucket.org/kaistsystemsbiology/deeprfc.
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Affiliation(s)
- Yeji Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
| | - Jae Yong Ryu
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Hyun Uk Kim
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea.,Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, KAIST, Daejeon, Republic of Korea
| | - Woo Dae Jang
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
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40
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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] [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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41
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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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42
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Lawson CE, Martí JM, Radivojevic T, Jonnalagadda SVR, Gentz R, Hillson NJ, Peisert S, Kim J, Simmons BA, Petzold CJ, Singer SW, Mukhopadhyay A, Tanjore D, Dunn JG, Garcia Martin H. Machine learning for metabolic engineering: A review. Metab Eng 2020; 63:34-60. [PMID: 33221420 DOI: 10.1016/j.ymben.2020.10.005] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/22/2020] [Accepted: 10/31/2020] [Indexed: 12/14/2022]
Abstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
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Affiliation(s)
- Christopher E Lawson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Jose Manuel Martí
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Tijana Radivojevic
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sai Vamshi R Jonnalagadda
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Reinhard Gentz
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Nathan J Hillson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sean Peisert
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; University of California Davis, Davis, CA, 95616, USA
| | - Joonhoon Kim
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Pacific Northwest National Laboratory, Richland, 99354, WA, USA
| | - Blake A Simmons
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Christopher J Petzold
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Steven W Singer
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA
| | - Deepti Tanjore
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Emeryville, CA, 94608, USA
| | | | - Hector Garcia Martin
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA; Basque Center for Applied Mathematics, 48009, Bilbao, Spain; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA.
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43
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Engineered biosynthetic pathways and biocatalytic cascades for sustainable synthesis. Curr Opin Chem Biol 2020; 58:146-154. [PMID: 33152607 DOI: 10.1016/j.cbpa.2020.08.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/13/2020] [Accepted: 08/14/2020] [Indexed: 10/23/2022]
Abstract
Nature exploits biosynthetic cascades to construct numerous molecules from a limited set of starting materials. A deeper understanding of biosynthesis and extraordinary developments in gene technology has allowed the manipulation of natural pathways and construction of artificial cascades for the preparation of a range of molecules, which would be challenging to access using traditional synthetic chemical approaches. Alongside these metabolic engineering strategies, there has been continued interest in developing in vivo and in vitro biocatalytic cascades. Advancements in both metabolic engineering and biocatalysis are complementary, and this article aims to highlight some of the most exciting developments in these two areas with a particular focus on exploring those that have the potential to advance both pathway engineering and more traditional biocatalytic cascade development.
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44
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Liu Y, Su A, Li J, Ledesma-Amaro R, Xu P, Du G, Liu L. Towards next-generation model microorganism chassis for biomanufacturing. Appl Microbiol Biotechnol 2020; 104:9095-9108. [DOI: 10.1007/s00253-020-10902-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/03/2020] [Accepted: 09/10/2020] [Indexed: 11/29/2022]
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45
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Zhou K, Ng W, Cortés-Peña Y, Wang X. Increasing metabolic pathway flux by using machine learning models. Curr Opin Biotechnol 2020; 66:179-185. [PMID: 32896771 DOI: 10.1016/j.copbio.2020.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 08/03/2020] [Accepted: 08/11/2020] [Indexed: 01/19/2023]
Abstract
Machine learning is transforming many industries through self-improving models that are fueled by big data and high computing power. The field of metabolic engineering, which uses cellular biochemical network to manufacture useful small molecules, has also witnessed the first wave of machine learning applications in the past five years, covering reaction route design, enzyme selection, pathway engineering and process optimization. This review focuses on pathway engineering, and uses a few recent studies to illustrate (1) how machine learning models can be useful in overcoming an evident rate-limiting step, and (2) how the models may be used to exhaustively search - or guide optimization algorithms to search - a large design space when the cellular regulation of the reaction network is more convoluted.
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Affiliation(s)
- Kang Zhou
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore.
| | - Wenfa Ng
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Yoel Cortés-Peña
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Xiaonan Wang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
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46
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Zargar A, Valencia L, Wang J, Lal R, Chang S, Werts M, Wong AR, Hernández AC, Benites V, Baidoo EE, Katz L, Keasling JD. A bimodular PKS platform that expands the biological design space. Metab Eng 2020; 61:389-396. [DOI: 10.1016/j.ymben.2020.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 01/21/2023]
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47
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Ding S, Tian Y, Cai P, Zhang D, Cheng X, Sun D, Yuan L, Chen J, Tu W, Wei DQ, Hu QN. novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model. Nucleic Acids Res 2020; 48:W477-W487. [PMID: 32313937 PMCID: PMC7319456 DOI: 10.1093/nar/gkaa230] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/16/2020] [Accepted: 03/28/2020] [Indexed: 12/14/2022] Open
Abstract
To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/.
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Affiliation(s)
- Shaozhen Ding
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Yu Tian
- School of Biology and Pharmaceutical Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China
| | - Pengli Cai
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People's Republic of China
| | - Dachuan Zhang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Xingxiang Cheng
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Dandan Sun
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Le Yuan
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden
| | - Junni Chen
- Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, People's Republic of China
| | - Weizhong Tu
- Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, People's Republic of China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism (Shanghai Jiao Tong University), Shanghai 200240, China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
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48
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Microbial Chassis Development for Natural Product Biosynthesis. Trends Biotechnol 2020; 38:779-796. [DOI: 10.1016/j.tibtech.2020.01.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/18/2019] [Accepted: 01/03/2020] [Indexed: 02/07/2023]
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49
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Whitehead TA, Banta S, Bentley WE, Betenbaugh MJ, Chan C, Clark DS, Hoesli CA, Jewett MC, Junker B, Koffas M, Kshirsagar R, Lewis A, Li CT, Maranas C, Terry Papoutsakis E, Prather KLJ, Schaffer S, Segatori L, Wheeldon I. The importance and future of biochemical engineering. Biotechnol Bioeng 2020; 117:2305-2318. [PMID: 32343367 DOI: 10.1002/bit.27364] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/24/2020] [Accepted: 04/26/2020] [Indexed: 02/06/2023]
Abstract
Today's Biochemical Engineer may contribute to advances in a wide range of technical areas. The recent Biochemical and Molecular Engineering XXI conference focused on "The Next Generation of Biochemical and Molecular Engineering: The role of emerging technologies in tomorrow's products and processes". On the basis of topical discussions at this conference, this perspective synthesizes one vision on where investment in research areas is needed for biotechnology to continue contributing to some of the world's grand challenges.
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Affiliation(s)
- Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado
| | - Scott Banta
- Department of Chemical Engineering, Columbia University, New York, New York
| | - William E Bentley
- Fischell Department of Bioengineering, University of Maryland, College Park, Maryland
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Christina Chan
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan
| | - Douglas S Clark
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California
| | - Corinne A Hoesli
- Department of Chemical Engineering & Department of Biological and Biomedical Engineering, McGill University, Montreal, Québec, Canada
| | - Michael C Jewett
- Department of Chemical and Biological Engineering and Center for Synthetic Biology, Northwestern University, Evanston, Illinois
| | - Beth Junker
- BioProcess Advantage LLC, Middesex, New Jersey
| | - Mattheos Koffas
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | | | | | - Chien-Ting Li
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Costas Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania
| | - E Terry Papoutsakis
- Department of Chemical & Biomolecular Engineering & the Delaware Biotechnology Institute, University of Delaware, Newark, Delaware
| | - Kristala L J Prather
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | | | - Laura Segatori
- Department of Bioengineering, Rice University, Houston, Texas
| | - Ian Wheeldon
- Department of Chemical and Environmental Engineering, University of California, Riverside, California
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50
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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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