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Swamidatta SH, Lichman BR. Beyond co-expression: pathway discovery for plant pharmaceuticals. Curr Opin Biotechnol 2024; 88:103147. [PMID: 38833915 DOI: 10.1016/j.copbio.2024.103147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Plant natural products have been an important source of medicinal molecules since ancient times. To gain access to the whole diversity of these molecules for pharmaceutical applications, it is important to understand their biosynthetic origins. Whilst co-expression is a reliable tool for identifying gene candidates, a variety of complementary methods can aid in screening or refining candidate selection. Here, we review recently employed plant biosynthetic pathway discovery approaches, and highlight future directions in the field.
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Affiliation(s)
- Sandesh H Swamidatta
- Centre for Novel Agricultural Products, Department of Biology, University of York, York YO10 5DD, UK
| | - Benjamin R Lichman
- Centre for Novel Agricultural Products, Department of Biology, University of York, York YO10 5DD, UK.
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2
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Durand M, Besseau S, Papon N, Courdavault V. Unlocking plant bioactive pathways: omics data harnessing and machine learning assisting. Curr Opin Biotechnol 2024; 87:103135. [PMID: 38728826 DOI: 10.1016/j.copbio.2024.103135] [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: 01/11/2024] [Revised: 04/02/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024]
Abstract
Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in metabolomics and transcriptomics. While harnessing data, whether integrated or standalone, has proven successful in unveiling plant natural product (PNP) biosynthetic pathways, the democratization of machine learning in biology opens exciting new opportunities for enhancing the exploration of these pathways. This review highlights the recent breakthroughs in disrupting plant-specialized biosynthetic pathways through the utilization of omics data harnessing and machine learning techniques.
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Affiliation(s)
- Mickael Durand
- Biomolécules et Biotechnologies Végétales, EA2106, Université de Tours, 37200 Tours, France
| | - Sébastien Besseau
- Biomolécules et Biotechnologies Végétales, EA2106, Université de Tours, 37200 Tours, France
| | - Nicolas Papon
- Univ Angers, Univ Brest, IRF, SFR ICAT, F-49000 Angers, France
| | - Vincent Courdavault
- Biomolécules et Biotechnologies Végétales, EA2106, Université de Tours, 37200 Tours, France.
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3
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Kim GB, Kim JY, Lee JA, Norsigian CJ, Palsson BO, Lee SY. Functional annotation of enzyme-encoding genes using deep learning with transformer layers. Nat Commun 2023; 14:7370. [PMID: 37963869 PMCID: PMC10645960 DOI: 10.1038/s41467-023-43216-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/03/2023] [Indexed: 11/16/2023] Open
Abstract
Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Enzymes constitute the most prevalent functional gene class in microbial genomes and can be described by their specific catalytic functions using the Enzyme Commission (EC) number. Consequently, the ability to predict EC numbers could substantially reduce the number of un-annotated genes. Here we present a deep learning model, DeepECtransformer, which utilizes transformer layers as a neural network architecture to predict EC numbers. Using the extensively studied Escherichia coli K-12 MG1655 genome, DeepECtransformer predicted EC numbers for 464 un-annotated genes. We experimentally validated the enzymatic activities predicted for three proteins (YgfF, YciO, and YjdM). Further examination of the neural network's reasoning process revealed that the trained neural network relies on functional motifs of enzymes to predict EC numbers. Thus, DeepECtransformer is a method that facilitates the functional annotation of uncharacterized genes.
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Affiliation(s)
- 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), Daejeon, 34141, Republic of Korea
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST, Daejeon, 34141, Republic of Korea
- KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon, 34141, Republic of Korea
| | - Ji Yeon Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST, Daejeon, 34141, Republic of Korea
- KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon, 34141, Republic of Korea
| | - Jong An Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST, Daejeon, 34141, Republic of Korea
- KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon, 34141, Republic of Korea
| | - Charles J Norsigian
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, 2800, Kongens Lyngby, Denmark
| | - 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), Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), KAIST, Daejeon, 34141, Republic of Korea.
- KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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4
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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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5
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Bui VH, Rodríguez-López CE, Dang TTT. Integration of discovery and engineering in plant alkaloid research: Recent developments in elucidation, reconstruction, and repurposing biosynthetic pathways. CURRENT OPINION IN PLANT BIOLOGY 2023; 74:102379. [PMID: 37182414 DOI: 10.1016/j.pbi.2023.102379] [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: 01/24/2023] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 05/16/2023]
Abstract
Plants synthesize tens of thousands of bioactive nitrogen-containing compounds called alkaloids, including some clinically important drugs in modern medicine. The discovery of new alkaloid structures and their metabolism in plants have provided ways to access these rich sources of bioactivities including new-to-nature compounds relevant to therapeutic and industrial applications. This review discusses recent advances in alkaloid biosynthesis discovery, including complete pathway elucidations. Additionally, the latest developments in the production of new and established plant alkaloids based on either biosynthesis or semisynthesis are discussed.
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Affiliation(s)
- Van-Hung Bui
- Department of Chemistry, Irving K. Barber Faculty of Science, University of British Columbia, 3247 University Way, Kelowna, BC V1V 1V7, Canada
| | - Carlos Eduardo Rodríguez-López
- Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico.
| | - Thu-Thuy T Dang
- Department of Chemistry, Irving K. Barber Faculty of Science, University of British Columbia, 3247 University Way, Kelowna, BC V1V 1V7, Canada.
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What is your next invention? — A framework of mining technological development rules and assisting in designing new technologies based on BERT as well as patent citations. COMPUT IND 2023. [DOI: 10.1016/j.compind.2022.103829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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7
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Affiliation(s)
- David Love
- United States Drug Enforcement Administration, Special Testing and Research Laboratory, USA
| | - Nicole S. Jones
- RTI International, Applied Justice Research Division, Center for Forensic Sciences, 3040 E. Cornwallis Road, Research Triangle Park, NC, 22709-2194, USA,70113th Street, N.W., Suite 750, Washington, DC, 20005-3967, USA,Corresponding author. RTI International, Applied Justice Research Division, Center for Forensic Sciences, 3040 E. Cornwallis Road, Research Triangle Park, NC, 22709-2194, USA.
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8
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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9
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Yang D, Eun H, Prabowo CPS, Cho S, Lee SY. Metabolic and cellular engineering for the production of natural products. Curr Opin Biotechnol 2022; 77:102760. [PMID: 35908315 DOI: 10.1016/j.copbio.2022.102760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/14/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022]
Abstract
Increased awareness of the environmental and health concerns of consuming chemically synthesized products has led to a rising demand for natural products that are greener and more sustainable. Despite their importance, however, industrial-scale production of natural products has been challenging due to the low yield and high cost of the bioprocesses. To cope with this problem, systems metabolic engineering has been employed to efficiently produce natural products from renewable biomass. Here, we review the recent systems metabolic engineering strategies employed for enhanced production of value-added natural products, together with accompanying examples. Particular focus is set on systems-level engineering and cell physiology engineering strategies. Future perspectives are also discussed.
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Affiliation(s)
- Dongsoo Yang
- 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, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, 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, KAIST, Daejeon 34141, Republic of Korea
| | - Cindy Pricilia Surya Prabowo
- 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, KAIST, Daejeon 34141, Republic of Korea
| | - Sumin 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, 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, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea.
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