1
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Hafner J, Lorsbach T, Schmidt S, Brydon L, Dost K, Zhang K, Fenner K, Wicker J. Advancements in biotransformation pathway prediction: enhancements, datasets, and novel functionalities in enviPath. J Cheminform 2024; 16:93. [PMID: 39107805 PMCID: PMC11304562 DOI: 10.1186/s13321-024-00881-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system. SCIENTIFIC CONTRIBUTION: The main scientific contributions are the development of a pathway prediction model applicable to diverse chemicals, a specialized evaluation method for holistic performance assessment, and a novel applicability domain algorithm for user-specific performance estimation. The introduction of two new data sets, and the creation of links to EC classes make enviPath a unique resource in microbial biotransformation research.
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Affiliation(s)
- Jasmin Hafner
- University of Zürich, Zürich, Switzerland
- Eawag, Dübendorf, Switzerland
| | | | | | - Liam Brydon
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Katharina Dost
- enviPath, Mainz, Germany
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Kunyang Zhang
- University of Zürich, Zürich, Switzerland
- Eawag, Dübendorf, Switzerland
- enviPath, Mainz, Germany
| | - Kathrin Fenner
- University of Zürich, Zürich, Switzerland
- Eawag, Dübendorf, Switzerland
| | - Jörg Wicker
- enviPath, Mainz, Germany.
- School of Computer Science, University of Auckland, Auckland, New Zealand.
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2
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Shiroma H, Darzi Y, Terajima E, Nakagawa Z, Tsuchikura H, Tsukuda N, Moriya Y, Okuda S, Goto S, Yamada T. Enteropathway: the metabolic pathway database for the human gut microbiota. Brief Bioinform 2024; 25:bbae419. [PMID: 39222063 PMCID: PMC11367760 DOI: 10.1093/bib/bbae419] [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: 04/10/2024] [Revised: 07/09/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024] Open
Abstract
The human gut microbiota produces diverse, extensive metabolites that have the potential to affect host physiology. Despite significant efforts to identify metabolic pathways for producing these microbial metabolites, a comprehensive metabolic pathway database for the human gut microbiota is still lacking. Here, we present Enteropathway, a metabolic pathway database that integrates 3269 compounds, 3677 reactions, and 876 modules that were obtained from 1012 manually curated scientific literature. Notably, 698 modules of these modules are new entries and cannot be found in any other databases. The database is accessible from a web application (https://enteropathway.org) that offers a metabolic diagram for graphical visualization of metabolic pathways, a customization interface, and an enrichment analysis feature for highlighting enriched modules on the metabolic diagram. Overall, Enteropathway is a comprehensive reference database that can complement widely used databases, and a tool for visual and statistical analysis in human gut microbiota studies and was designed to help researchers pinpoint new insights into the complex interplay between microbiota and host metabolism.
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Affiliation(s)
- Hirotsugu Shiroma
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 M6-3 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Youssef Darzi
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 M6-3 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
- Omixer solutions, 4-7-15, Zaimokuza, Kamakura-shi, Kanagawa 248-0013, Japan
| | - Etsuko Terajima
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 M6-3 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Zenichi Nakagawa
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 M6-3 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Hirotaka Tsuchikura
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 M6-3 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Naoki Tsukuda
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 M6-3 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Yuki Moriya
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 178-4-4 Wakashiba, Kashiwa-shi, Chiba 277-0871, Japan
| | - Shujiro Okuda
- Graduate School of Medical and Dental Sciences, Niigata University, 2-5274, Gakkocho-dori, Chuo-ku, Niigata City, Niigata 951-8514, Japan
| | - Susumu Goto
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 178-4-4 Wakashiba, Kashiwa-shi, Chiba 277-0871, Japan
| | - Takuji Yamada
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 M6-3 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
- Metagen, Inc., 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan
- Metagen Theurapeutics, Inc., 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan
- Digzyme, Inc., 2-2-1 Toranomon, Minato-ku, Tokyo 105-0001, Japan
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3
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Zeng T, Jin Z, Zheng S, Yu T, Wu R. Developing BioNavi for Hybrid Retrosynthesis Planning. JACS AU 2024; 4:2492-2502. [PMID: 39055138 PMCID: PMC11267531 DOI: 10.1021/jacsau.4c00228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/27/2024]
Abstract
Illuminating synthetic pathways is essential for producing valuable chemicals, such as bioactive molecules. Chemical and biological syntheses are crucial, and their integration often leads to more efficient and sustainable pathways. Despite the rapid development of retrosynthesis models, few of them consider both chemical and biological syntheses, hindering the pathway design for high-value chemicals. Here, we propose BioNavi by innovating multitask learning and reaction templates into the deep learning-driven model to design hybrid synthesis pathways in a more interpretable manner. BioNavi outperforms existing approaches on different data sets, achieving a 75% hit rate in replicating reported biosynthetic pathways and displaying superior ability in designing hybrid synthesis pathways. Additional case studies further illustrate the potential application of BioNavi in a de novo pathway design. The enhanced web server (http://biopathnavi.qmclab.com/bionavi/) simplifies input operations and implements step-by-step exploration according to user experience. We show that BioNavi is a handy navigator for designing synthetic pathways for various chemicals.
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Affiliation(s)
- Tao Zeng
- School
of Pharmaceutical Sciences, Sun Yat-sen
University, Guangzhou 510006, P. R. China
| | - Zhehao Jin
- Center
for Synthetic Biochemistry, CAS Key Laboratory of Quantitative Engineering
Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
(CAS), Shenzhen 518055, P. R. China
| | - Shuangjia Zheng
- Global
Institute of Future Technology, Shanghai
Jiao Tong University, Shanghai 200240, P. R. China
| | - Tao Yu
- Center
for Synthetic Biochemistry, CAS Key Laboratory of Quantitative Engineering
Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
(CAS), Shenzhen 518055, P. R. China
| | - Ruibo Wu
- School
of Pharmaceutical Sciences, Sun Yat-sen
University, Guangzhou 510006, P. R. China
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4
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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; 52:W299-W305. [PMID: 38769057 PMCID: PMC11223788 DOI: 10.1093/nar/gkae405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [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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5
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Xing H, Cai P, Liu D, Han M, Liu J, Le Y, Zhang D, Hu QN. High-throughput prediction of enzyme promiscuity based on substrate-product pairs. Brief Bioinform 2024; 25:bbae089. [PMID: 38487850 PMCID: PMC10940840 DOI: 10.1093/bib/bbae089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/20/2024] [Accepted: 02/03/2024] [Indexed: 03/18/2024] Open
Abstract
The screening of enzymes for catalyzing specific substrate-product pairs is often constrained in the realms of metabolic engineering and synthetic biology. Existing tools based on substrate and reaction similarity predominantly rely on prior knowledge, demonstrating limited extrapolative capabilities and an inability to incorporate custom candidate-enzyme libraries. Addressing these limitations, we have developed the Substrate-product Pair-based Enzyme Promiscuity Prediction (SPEPP) model. This innovative approach utilizes transfer learning and transformer architecture to predict enzyme promiscuity, thereby elucidating the intricate interplay between enzymes and substrate-product pairs. SPEPP exhibited robust predictive ability, eliminating the need for prior knowledge of reactions and allowing users to define their own candidate-enzyme libraries. It can be seamlessly integrated into various applications, including metabolic engineering, de novo pathway design, and hazardous material degradation. To better assist metabolic engineers in designing and refining biochemical pathways, particularly those without programming skills, we also designed EnzyPick, an easy-to-use web server for enzyme screening based on SPEPP. EnzyPick is accessible at http://www.biosynther.com/enzypick/.
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Affiliation(s)
- Huadong Xing
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Pengli Cai
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongliang Liu
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengying Han
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Yingying Le
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dachuan Zhang
- Institute of Environmental Engineering, ETH Zurich, Laura-Hezner-Weg 7, 8093 Zurich, Switzerland
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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6
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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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7
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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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8
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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: 1] [Impact Index Per Article: 1.0] [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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9
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Yoshida Y, Shimizu I, Shimada A, Nakahara K, Yanagisawa S, Kubo M, Fukuda S, Ishii C, Yamamoto H, Ishikawa T, Kano K, Aoki J, Katsuumi G, Suda M, Ozaki K, Yoshida Y, Okuda S, Ohta S, Okamoto S, Minokoshi Y, Oda K, Sasaoka T, Abe M, Sakimura K, Kubota Y, Yoshimura N, Kajimura S, Zuriaga M, Walsh K, Soga T, Minamino T. Brown adipose tissue dysfunction promotes heart failure via a trimethylamine N-oxide-dependent mechanism. Sci Rep 2022; 12:14883. [PMID: 36050466 PMCID: PMC9436957 DOI: 10.1038/s41598-022-19245-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/26/2022] [Indexed: 11/14/2022] Open
Abstract
Low body temperature predicts a poor outcome in patients with heart failure, but the underlying pathological mechanisms and implications are largely unknown. Brown adipose tissue (BAT) was initially characterised as a thermogenic organ, and recent studies have suggested it plays a crucial role in maintaining systemic metabolic health. While these reports suggest a potential link between BAT and heart failure, the potential role of BAT dysfunction in heart failure has not been investigated. Here, we demonstrate that alteration of BAT function contributes to development of heart failure through disorientation in choline metabolism. Thoracic aortic constriction (TAC) or myocardial infarction (MI) reduced the thermogenic capacity of BAT in mice, leading to significant reduction of body temperature with cold exposure. BAT became hypoxic with TAC or MI, and hypoxic stress induced apoptosis of brown adipocytes. Enhancement of BAT function improved thermogenesis and cardiac function in TAC mice. Conversely, systolic function was impaired in a mouse model of genetic BAT dysfunction, in association with a low survival rate after TAC. Metabolomic analysis showed that reduced BAT thermogenesis was associated with elevation of plasma trimethylamine N-oxide (TMAO) levels. Administration of TMAO to mice led to significant reduction of phosphocreatine and ATP levels in cardiac tissue via suppression of mitochondrial complex IV activity. Genetic or pharmacological inhibition of flavin-containing monooxygenase reduced the plasma TMAO level in mice, and improved cardiac dysfunction in animals with left ventricular pressure overload. In patients with dilated cardiomyopathy, body temperature was low along with elevation of plasma choline and TMAO levels. These results suggest that maintenance of BAT homeostasis and reducing TMAO production could be potential next-generation therapies for heart failure.
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Affiliation(s)
- Yohko Yoshida
- grid.258269.20000 0004 1762 2738Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, 113-8431 Japan ,grid.258269.20000 0004 1762 2738Department of Advanced Senotherapeutics, Juntendo University Graduate School of Medicine, Tokyo, 113-8431 Japan
| | - Ippei Shimizu
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, 113-8431, Japan. .,Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
| | - Atsuhiro Shimada
- grid.256342.40000 0004 0370 4927Department of Applied Life Science, Faculty of Applied Biological Sciences, Gifu University, Gifu, 501-1193 Japan
| | - Keita Nakahara
- grid.256342.40000 0004 0370 4927Department of Applied Life Science, Faculty of Applied Biological Sciences, Gifu University, Gifu, 501-1193 Japan
| | - Sachiko Yanagisawa
- grid.266453.00000 0001 0724 9317Graduate School of Science, University of Hyogo, Hyogo, 678-1297 Japan
| | - Minoru Kubo
- grid.266453.00000 0001 0724 9317Graduate School of Science, University of Hyogo, Hyogo, 678-1297 Japan
| | - Shinji Fukuda
- grid.26091.3c0000 0004 1936 9959Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052 Japan ,grid.26999.3d0000 0001 2151 536XIntestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kanagawa, 210-0821 Japan ,grid.20515.330000 0001 2369 4728Transborder Medical Research Center, University of Tsukuba, Ibaraki, 305-8575 Japan
| | - Chiharu Ishii
- grid.26091.3c0000 0004 1936 9959Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052 Japan
| | - Hiromitsu Yamamoto
- grid.26091.3c0000 0004 1936 9959Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052 Japan
| | - Takamasa Ishikawa
- grid.26091.3c0000 0004 1936 9959Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052 Japan
| | - Kuniyuki Kano
- grid.26999.3d0000 0001 2151 536XDepartment of Health Chemistry, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Junken Aoki
- grid.26999.3d0000 0001 2151 536XDepartment of Health Chemistry, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Goro Katsuumi
- grid.258269.20000 0004 1762 2738Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, 113-8431 Japan
| | - Masayoshi Suda
- grid.258269.20000 0004 1762 2738Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, 113-8431 Japan
| | - Kazuyuki Ozaki
- grid.260975.f0000 0001 0671 5144Department of Cardiovascular Biology and Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, 951-8510 Japan
| | - Yutaka Yoshida
- grid.260975.f0000 0001 0671 5144Department of Structural Pathology, Kidney Research Center, Niigata University Graduate School of Medical and Dental Sciences, Niigata, 951-8510 Japan
| | - Shujiro Okuda
- grid.260975.f0000 0001 0671 5144Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, 951-8510 Japan
| | - Shigeo Ohta
- grid.258269.20000 0004 1762 2738Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, 113-8421 Japan
| | - Shiki Okamoto
- grid.267625.20000 0001 0685 5104Second Department of Internal Medicine (Endocrinology, Diabetes and Metabolism, Hematology, Rheumatology), Graduate School of Medicine, University of the Ryukyus, Okinawa, 903-0215 Japan
| | - Yasuhiko Minokoshi
- grid.467811.d0000 0001 2272 1771Department of Homeostatic Regulation, Division of Endocrinology and Metabolism, National Institutes of Natural Sciences, National Institute for Physiological Sciences, Aichi, 444-8585 Japan
| | - Kanako Oda
- grid.260975.f0000 0001 0671 5144Department of Comparative and Experimental Medicine, Brain Research Institute, Niigata University, Niigata, 951-8585 Japan
| | - Toshikuni Sasaoka
- grid.260975.f0000 0001 0671 5144Department of Comparative and Experimental Medicine, Brain Research Institute, Niigata University, Niigata, 951-8585 Japan
| | - Manabu Abe
- grid.260975.f0000 0001 0671 5144Department of Cellular Neurobiology, Brain Research Institute, Niigata University, Niigata, 951-8585 Japan ,grid.260975.f0000 0001 0671 5144Department of Animal Model Development, Brain Research Institute, Niigata University, Niigata, 951-8585 Japan
| | - Kenji Sakimura
- grid.260975.f0000 0001 0671 5144Department of Cellular Neurobiology, Brain Research Institute, Niigata University, Niigata, 951-8585 Japan ,grid.260975.f0000 0001 0671 5144Department of Animal Model Development, Brain Research Institute, Niigata University, Niigata, 951-8585 Japan
| | - Yoshiaki Kubota
- grid.26091.3c0000 0004 1936 9959Department of Anatomy, Keio University School of Medicine, Tokyo, 160-8582 Japan
| | - Norihiko Yoshimura
- grid.260975.f0000 0001 0671 5144Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, 951-8510 Japan ,grid.416205.40000 0004 1764 833XDepartment of Radiology, Niigata City General Hospital, Niigata, 950-1197 Japan
| | - Shingo Kajimura
- grid.239395.70000 0000 9011 8547Division of Endocrinology, Diabetes & Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Maria Zuriaga
- grid.467824.b0000 0001 0125 7682Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Kenneth Walsh
- grid.27755.320000 0000 9136 933XDivision of Cardiovascular Medicine, Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA 22908 USA
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
| | - Tohru Minamino
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, 113-8431, Japan. .,Japan Agency for Medical Research and Development-Core Research for Evolutionary Medical Science and Technology (AMED-CREST), Japan Agency for Medical Research and Development, Tokyo, 100-0004, Japan. .,Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
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10
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Zheng S, Zeng T, Li C, Chen B, Coley CW, Yang Y, Wu R. Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP. Nat Commun 2022; 13:3342. [PMID: 35688826 PMCID: PMC9187661 DOI: 10.1038/s41467-022-30970-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 05/27/2022] [Indexed: 12/30/2022] Open
Abstract
The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP can identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. The model is further shown to identify biologically plausible pathways for complex NPs collected from the recent literature. The toolkit as well as the curated datasets and learned models are freely available to facilitate the elucidation and reconstruction of the biosynthetic pathways for NPs. The complete biosynthetic pathway from most natural products (NPs) are unknown. Here, the authors report BioNavi-NP, a computational toolkit for bio-retrosynthetic pathway elucidation or reconstruction for both NPs and NP-like compounds.
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Affiliation(s)
- Shuangjia Zheng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.,School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,Galixir, Beijing, China.,School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Tao Zeng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
| | | | - Binghong Chen
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Ruibo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
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11
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Chen F, Yuan L, Ding S, Tian Y, Hu QN. Data-driven rational biosynthesis design: from molecules to cell factories. Brief Bioinform 2020; 21:1238-1248. [PMID: 31243440 DOI: 10.1093/bib/bbz065] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 04/28/2019] [Accepted: 05/08/2019] [Indexed: 11/12/2022] Open
Abstract
A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.
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Affiliation(s)
- Fu Chen
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, People's Republic of China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.,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, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Le Yuan
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.,University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - 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, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Yu Tian
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.,University of Chinese Academy of Sciences, Beijing, People's Republic of 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, Chinese Academy of Sciences, Shanghai, People's Republic of China
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12
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Nakamura Y, Hirose S, Taniguchi Y, Moriya Y, Yamada T. Targeted enzyme gene re-positioning: A computational approach for discovering alternative bacterial enzymes for the synthesis of plant-specific secondary metabolites. Metab Eng Commun 2019; 9:e00102. [PMID: 31720217 PMCID: PMC6838473 DOI: 10.1016/j.mec.2019.e00102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 08/19/2019] [Accepted: 09/08/2019] [Indexed: 12/27/2022] Open
Abstract
Plant-biosynthesised secondary metabolites are unique sources of pharmaceuticals, food additives, and flavourings, among other industrial uses. However, industrial production of these metabolites is difficult because of their structural complexity, dangerousness and unfriendliness to natural environment, so the development of new methods to synthesise them is required. In this study, we developed a novel approach to identifying alternative bacterial enzyme to produce plant-biosynthesised secondary metabolites. Based on the similarity of enzymatic reactions, we searched for candidate bacterial genes encoding enzymes that could potentially replace the enzymes in plant-specific secondary metabolism reactions that are contained in the KEGG database (enzyme re-positioning). As a result, we discovered candidate bacterial alternative enzyme genes for 447 plant-specific secondary metabolic reaction. To validate our approach, we focused on the ability of an enzyme from Streptomyces coelicolor strain A3(2) strain to convert valencene to the grapefruit metabolite nootkatone, and confirmed its enzymatic activity by gas chromatography-mass spectrometry. This enzyme re-positioning approach may offer an entirely new way of screening enzymes that cannot be achieved by most of other conventional methods, and it is applicable to various other metabolites and may enable microbial production of compounds that are currently difficult to produce industrially.
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Affiliation(s)
- Yuya Nakamura
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo, 152-8550, Japan
| | - Shuichi Hirose
- NAGASE R&D Center, Nagase & Co., Ltd, Kobe High Tech Park 2-2-3 Murotani, Nishi- ku, Kobe, Hyogo, 651-2241, Japan
| | - Yuko Taniguchi
- NAGASE R&D Center, Nagase & Co., Ltd, Kobe High Tech Park 2-2-3 Murotani, Nishi- ku, Kobe, Hyogo, 651-2241, Japan
| | - Yuki Moriya
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa, 277-0871, Japan
| | - Takuji Yamada
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo, 152-8550, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho Kawaguchi, Saitama, 332-0012, Japan
- Metabologenomics Inc, 246-2 Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan
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13
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Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc Natl Acad Sci U S A 2019; 116:7298-7307. [PMID: 30910961 PMCID: PMC6462048 DOI: 10.1073/pnas.1818877116] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them “orphan.” A large number of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, the computational method BridgIT identified potential enzymes of orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. The BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze nonnatural transformations. Thousands of biochemical reactions with characterized activities are “orphan,” meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.
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14
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Sato H, Wang C, Yamazaki M, Saito K, Uchiyama M. Computational study on a puzzle in the biosynthetic pathway of anthocyanin: Why is an enzymatic oxidation/ reduction process required for a simple tautomerization? PLoS One 2018; 13:e0198944. [PMID: 29897974 PMCID: PMC5999093 DOI: 10.1371/journal.pone.0198944] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 05/29/2018] [Indexed: 01/20/2023] Open
Abstract
In the late stage of anthocyanin biosynthesis, dihydroflavonol reductase (DFR) and anthocyanidin synthase (ANS) mediate a formal tautomerization. However, such oxidation/reduction process requires high energy and appears to be unnecessary, as the oxidation state does not change during the transformation. Thus, a non-enzymatic pathway of tautomerization has also been proposed. To resolve the long-standing issue of whether this non-enzymatic pathway is the main contributor for the biosynthesis, we carried out density functional theory (DFT) calculations to examine this non-enzymatic pathway from dihydroflavonol to anthocyanidin. We show here that the activation barriers for the proposed non-enzymatic tautomerization are too high to enable the reaction to proceed under normal aqueous conditions in plants. The calculations also explain the experimentally observed requirement for acidic conditions during the final step of conversion of 2-flaven-3,4-diol to anthocyanidin; a thermodynamically and kinetically favorable concerted pathway can operate under these conditions.
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Affiliation(s)
- Hajime Sato
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
- Elements Chemistry Laboratory, RIKEN, and RIKEN Center for Sustainable Resource Science (Wako campus) 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Chao Wang
- Elements Chemistry Laboratory, RIKEN, and RIKEN Center for Sustainable Resource Science (Wako campus) 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Mami Yamazaki
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Kazuki Saito
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
- RIKEN Center for Sustainable Resource Science (Yokohama campus) 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Masanobu Uchiyama
- Elements Chemistry Laboratory, RIKEN, and RIKEN Center for Sustainable Resource Science (Wako campus) 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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15
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Muto-Fujita A, Takemoto K, Kanaya S, Nakazato T, Tokimatsu T, Matsumoto N, Kono M, Chubachi Y, Ozaki K, Kotera M. Data integration aids understanding of butterfly-host plant networks. Sci Rep 2017; 7:43368. [PMID: 28262809 PMCID: PMC5338290 DOI: 10.1038/srep43368] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 01/23/2017] [Indexed: 12/05/2022] Open
Abstract
Although host-plant selection is a central topic in ecology, its general underpinnings are poorly understood. Here, we performed a case study focusing on the publicly available data on Japanese butterflies. A combined statistical analysis of plant-herbivore relationships and taxonomy revealed that some butterfly subfamilies in different families feed on the same plant families, and the occurrence of this phenomenon more than just by chance, thus indicating the independent acquisition of adaptive phenotypes to the same hosts. We consequently integrated plant-herbivore and plant-compound relationship data and conducted a statistical analysis to identify compounds unique to host plants of specific butterfly families. Some of the identified plant compounds are known to attract certain butterfly groups while repelling others. The additional incorporation of insect-compound relationship data revealed potential metabolic processes that are related to host plant selection. Our results demonstrate that data integration enables the computational detection of compounds putatively involved in particular interspecies interactions and that further data enrichment and integration of genomic and transcriptomic data facilitates the unveiling of the molecular mechanisms involved in host plant selection.
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Affiliation(s)
- Ai Muto-Fujita
- Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Kazuhiro Takemoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, Fukuoka 820-8502, Japan
| | - Shigehiko Kanaya
- Graduate School of Information Sciences, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Takeru Nakazato
- Database Center for Life Science (DBCLS), Research Organization of Information and Systems, Yata 1111, Mishima, Shizuoka 411-8540, Japan
| | - Toshiaki Tokimatsu
- DDBJ Center, National Institute of Genetics, Research Organization of Information and Systems, Yata 1111, Mishima, Shizuoka 411-8540, Japan
| | | | - Mayo Kono
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Yuko Chubachi
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Katsuhisa Ozaki
- JT Biohistory Research Hall, 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
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16
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Kotera M, Goto S. Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis. Biophys Physicobiol 2016; 13:195-205. [PMID: 27924274 PMCID: PMC5042172 DOI: 10.2142/biophysico.13.0_195] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 06/20/2016] [Indexed: 12/22/2022] Open
Abstract
Metabolic pathway reconstruction presents a challenge for understanding metabolic pathways in organisms of interest. Different strategies, i.e., reference-based vs. de novo, must be used for pathway reconstruction depending on the availability of well-characterized enzymatic reactions. If at least one enzyme is already known to catalyze a reaction, its amino acid sequence can be used as a reference for identifying homologous enzymes in the genome of an organism of interest. Where there is no known enzyme able to catalyze a corresponding reaction, however, the reaction and the corresponding enzyme must be predicted de novo from chemical transformations of the putative substrate-product pair. This review summarizes studies involving reference-based and de novo metabolic pathway reconstruction and discusses the importance of the classification and structure-function relationships of enzymes.
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Affiliation(s)
- Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
| | - Susumu Goto
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
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