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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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Ji J, Zhang D, Ye J, Zheng Y, Cui J, Sun X. MycotoxinDB: A Data-Driven Platform for Investigating Masked Forms of Mycotoxins. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37145977 DOI: 10.1021/acs.jafc.3c01403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Mycotoxins are likely to be converted into masked forms when subjected to plant metabolism or food processing. These masked forms of mycotoxins together with their prototypes may cause mixture toxicity effects, causing adverse effects on animal welfare and productivity. The structure elucidation of masked mycotoxins is the most challenging task in mycotoxin research due to the limitations of traditional analysis methods. To assist in the rapid identification of masked mycotoxins, we developed a data-driven online prediction tool, MycotoxinDB, based on reaction rules. Using MycotoxinDB, we identified seven masked DONs from wheat samples. Given its widespread applications, we expect that MycotoxinDB will become an indispensable tool in future mycotoxin research. MycotoxinDB is freely available at: http://www.mycotoxin-db.com/.
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Affiliation(s)
- Jian Ji
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, Synergetic Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, Jiangsu 214122, P. R. China
- College of Food Science and Pharmacy, Xinjiang Agricultural University, No. 311 Nongda Dong Road, Ürümqi, 830052 Xinjiang Uygur Autonomous Region, P. R. China
| | - Dachuan Zhang
- Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, Zurich 8093, Switzerland
| | - Jin Ye
- Academy of National Food and Strategic Reserves Administration, No. 11 Baiwanzhuang Str, Xicheng District, Beijing 100037, P. R. China
| | - Yi Zheng
- Jiangsu Agri-animal Husbandry Vocational College, Key Laboratory for High-Tech Research and Development of Veterinary Biopharmaceuticals, Taizhou, Jiangsu 225300, P. R. China
| | - Jing Cui
- Wuxi Food Safety Inspection and Test Center, No. 1 Building, Life Science Park, Xinwu District, Jiangsu, P. R. China
| | - Xiulan Sun
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, Synergetic Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, Jiangsu 214122, P. R. China
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Yin X, Ai F, Han L. Recent Development of MOF-Based Photothermal Agent for Tumor Ablation. Front Chem 2022; 10:841316. [PMID: 35372266 PMCID: PMC8966584 DOI: 10.3389/fchem.2022.841316] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/15/2022] [Indexed: 12/19/2022] Open
Abstract
Metal-organic frameworks (MOFs) are 3D-architecture compounds of metal ions and organic molecules with sufficient and permanent porosity, showing great potential as a versatile platform to load various functional moieties to endow the hybrid materials with specific applications. Currently, a variety of photothermal nanometals have been embedded into organic ligands for integrating the unique photothermal effects with the merits of MOFs to improve their performances for cancer therapy. In this review, we have summarized a series of novel MOF-based photothermal materials for this unique therapeutic modality against tumors from three main aspects according to their chemical compositions and structures, i) metal-doped MOF, ii) organic-doped MOF, and iii) polymer-coated MOF. In addition, we have summarized the latest developments and characteristics of MOF-based photothermal agents, such as good biocompatibility, low toxicity, and responsive photothermal conversion without destroying the structure of hybrid photothermal agent. At last, we addressed the future perspectives of MOF-based photothermal agent in the field of phototherapy.
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Affiliation(s)
- Xiuzhao Yin
- College of Applied Technology, Shenzhen University, Shenzhen, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Fujin Ai
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
- *Correspondence: Fujin Ai, ; Linbo Han,
| | - Linbo Han
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
- *Correspondence: Fujin Ai, ; Linbo Han,
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