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Kissman EN, Sosa MB, Millar DC, Koleski EJ, Thevasundaram K, Chang MCY. Expanding chemistry through in vitro and in vivo biocatalysis. Nature 2024; 631:37-48. [PMID: 38961155 DOI: 10.1038/s41586-024-07506-w] [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: 04/22/2021] [Accepted: 05/01/2024] [Indexed: 07/05/2024]
Abstract
Living systems contain a vast network of metabolic reactions, providing a wealth of enzymes and cells as potential biocatalysts for chemical processes. The properties of protein and cell biocatalysts-high selectivity, the ability to control reaction sequence and operation in environmentally benign conditions-offer approaches to produce molecules at high efficiency while lowering the cost and environmental impact of industrial chemistry. Furthermore, biocatalysis offers the opportunity to generate chemical structures and functions that may be inaccessible to chemical synthesis. Here we consider developments in enzymes, biosynthetic pathways and cellular engineering that enable their use in catalysis for new chemistry and beyond.
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Affiliation(s)
- Elijah N Kissman
- Department of Chemistry, University of California Berkeley, Berkeley, CA, USA
| | - Max B Sosa
- Department of Chemistry, University of California Berkeley, Berkeley, CA, USA
| | - Douglas C Millar
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA
| | - Edward J Koleski
- Department of Chemistry, University of California Berkeley, Berkeley, CA, USA
| | | | - Michelle C Y Chang
- Department of Chemistry, University of California Berkeley, Berkeley, CA, USA.
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA.
- Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA, USA.
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
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Prešern U, Goličnik M. Enzyme Databases in the Era of Omics and Artificial Intelligence. Int J Mol Sci 2023; 24:16918. [PMID: 38069254 PMCID: PMC10707154 DOI: 10.3390/ijms242316918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.
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Affiliation(s)
| | - Marko Goličnik
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia;
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Zhang D, Tian Y, Tian Y, Xing H, Liu S, Zhang H, Ding S, Cai P, Sun D, Zhang T, Hong Y, Dai H, Tu W, Chen J, Wu A, Hu QN. A data-driven integrative platform for computational prediction of toxin biotransformation with a case study. JOURNAL OF HAZARDOUS MATERIALS 2021; 408:124810. [PMID: 33360695 DOI: 10.1016/j.jhazmat.2020.124810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/24/2020] [Accepted: 12/06/2020] [Indexed: 06/12/2023]
Abstract
Recently, biogenic toxins have received increasing attention owing to their high contamination levels in feed and food as well as in the environment. However, there is a lack of an integrative platform for seamless linking of data-driven computational methods with 'wet' experimental validations. To this end, we constructed a novel platform that integrates the technical aspects of toxin biotransformation methods. First, a biogenic toxin database termed ToxinDB (http://www.rxnfinder.org/toxindb/), containing multifaceted data on more than 4836 toxins, was built. Next, more than 8000 biotransformation reaction rules were extracted from over 300,000 biochemical reactions extracted from ~580,000 literature reports curated by more than 100 people over the past decade. Based on these reaction rules, a toxin biotransformation prediction model was constructed. Finally, the global chemical space of biogenic toxins was constructed, comprising ~550,000 toxins and putative toxin metabolites, of which 94.7% of the metabolites have not been previously reported. Additionally, we performed a case study to investigate citrinin metabolism in Trichoderma, and a novel metabolite was identified with the assistance of the biotransformation prediction tool of ToxinDB. This unique integrative platform will assist exploration of the 'dark matter' of a toxin's metabolome and promote the discovery of detoxification enzymes.
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Affiliation(s)
- Dachuan Zhang
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Ye Tian
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Yu Tian
- School of Biology and Pharmaceutical Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, PR China
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Sheng Liu
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Haoyang Zhang
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an 710119, PR China
| | - Shaozhen Ding
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Pengli Cai
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Dandan Sun
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Tong Zhang
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Yanhong Hong
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - Hongkun Dai
- Shandong Runda Testing Technology Co. Limited, Weifang 261000, PR China
| | - Weizhong Tu
- Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, PR China
| | - Junni Chen
- Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, PR China
| | - Aibo Wu
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.
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