AI-assisted food enzymes design and engineering: a critical review

被引:8
作者
Wang, Xinglong [1 ,2 ]
Yang, Penghui [1 ,2 ]
Zhao, Beichen [1 ,2 ]
Liu, Song [1 ,2 ]
机构
[1] Jiangnan Univ, Sci Ctr Future Foods, 1800 Lihu Rd, Wuxi 214122, Jiangsu, Peoples R China
[2] Jiangnan Univ, Sch Biotechnol, 1800 Lihu Rd, Wuxi 214122, Jiangsu, Peoples R China
来源
SYSTEMS MICROBIOLOGY AND BIOMANUFACTURING | 2023年 / 3卷 / 01期
基金
中国国家自然科学基金;
关键词
Artificial intelligence; Deep learning; Protein rational design; Protein thermostability; Protein activity; PROTEIN-STRUCTURE PREDICTION; DRUG DESIGN; STABILITY; DOCKING; MODELS; TRANSGLUTAMINASE; SPECTROSCOPY; ENHANCEMENT; ALGORITHMS; MOLECULES;
D O I
10.1007/s43393-022-00138-z
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Food enzymes are basic components used for food processing. Through catalysis, food enzymes can function as removing allergy, enriching absorbable nutrients, improving food texture, and adjusting flavors. Food enzymes work in various conditions, which brought out the need for engineering these enzymes with harsh environment tolerance and higher catalytic efficiency. Artificial intelligence (AI) has recently provided solutions for structural modeling, finding modification hot spots, and guiding mutations toward specific needs, which greatly benefit enzyme engineering. AI-based tools showed great advantages in cutting down the computational time, enabling higher prediction accuracy, and providing trainable models suited for wide uses. In this review, we describe the functions and uses of food enzymes, as well as their utility limitations. The necessity and advantages of using AI-based tools in enzyme engineering, and the differences between using traditional and AI-based tools are mainly discussed. Few AI-based tools for enzyme engineering were introduced and described their function. The perspective of using AI tools and the future challenges were discussed.
引用
收藏
页码:75 / 87
页数:13
相关论文
共 112 条
[31]   High-resolution design of a protein loop [J].
Hu, Xiaozhen ;
Wang, Huanchen ;
Ke, Hengming ;
Kuhlman, Brian .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2007, 104 (45) :17668-17673
[32]   RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design [J].
Huang, Po-Ssu ;
Ban, Yih-En Andrew ;
Richter, Florian ;
Andre, Ingemar ;
Vernon, Robert ;
Schief, William R. ;
Baker, David .
PLOS ONE, 2011, 6 (08)
[33]   Cryogel disks for lactase immobilization and lactose-free milk production [J].
Inanan, T. .
LWT-FOOD SCIENCE AND TECHNOLOGY, 2022, 154
[34]   Application of enzymes in food processing [J].
James, J ;
Simpson, BK .
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION, 1996, 36 (05) :437-463
[35]   Engineering the hydrogen transfer pathway of an alcohol dehydrogenase to increase activity by rational enzyme design [J].
Jiang, Yingying ;
Li, Xu ;
Liu, Beibei ;
Tong, Feifei ;
Qu, Ge ;
Sun, Zhoutong .
MOLECULAR CATALYSIS, 2022, 530
[36]   Highly accurate protein structure prediction with AlphaFold [J].
Jumper, John ;
Evans, Richard ;
Pritzel, Alexander ;
Green, Tim ;
Figurnov, Michael ;
Ronneberger, Olaf ;
Tunyasuvunakool, Kathryn ;
Bates, Russ ;
Zidek, Augustin ;
Potapenko, Anna ;
Bridgland, Alex ;
Meyer, Clemens ;
Kohl, Simon A. A. ;
Ballard, Andrew J. ;
Cowie, Andrew ;
Romera-Paredes, Bernardino ;
Nikolov, Stanislav ;
Jain, Rishub ;
Adler, Jonas ;
Back, Trevor ;
Petersen, Stig ;
Reiman, David ;
Clancy, Ellen ;
Zielinski, Michal ;
Steinegger, Martin ;
Pacholska, Michalina ;
Berghammer, Tamas ;
Bodenstein, Sebastian ;
Silver, David ;
Vinyals, Oriol ;
Senior, Andrew W. ;
Kavukcuoglu, Koray ;
Kohli, Pushmeet ;
Hassabis, Demis .
NATURE, 2021, 596 (7873) :583-+
[37]   PUResNet: prediction of protein-ligand binding sites using deep residual neural network [J].
Kandel, Jeevan ;
Tayara, Hilal ;
Chong, Kil To .
JOURNAL OF CHEMINFORMATICS, 2021, 13 (01)
[38]   De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks [J].
Karimi, Mostafa ;
Zhu, Shaowen ;
Cao, Yue ;
Shen, Yang .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2020, 60 (12) :5667-5681
[39]   Role of conformational sampling in computing mutation-induced changes in protein structure and stability [J].
Kellogg, Elizabeth H. ;
Leaver-Fay, Andrew ;
Baker, David .
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2011, 79 (03) :830-838
[40]   Replacement of buried cysteine from zebrafish Cu/Zn superoxide dismutase and enhancement of its stability via site-directed mutagenesis [J].
Ken, Chuian-Fu ;
Lin, Chi-Tsai ;
Wen, Yu-Der ;
Wu, Jen-Leih .
MARINE BIOTECHNOLOGY, 2007, 9 (03) :335-342