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Wang Y, Han S, Wang Y, Liang Q, Luo W. Artificial Intelligence Technology Assists Enzyme Prediction and Rational Design. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2025; 73:7065-7073. [PMID: 40066931 DOI: 10.1021/acs.jafc.4c13201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2025]
Abstract
Since the structure of enzymes determines their function, elucidating the structure of enzymes lays a solid foundation for deciphering their catalytic mechanism and enabling rational design. The development of artificial intelligence (AI) has sparked a technological revolution, infusing new vitality into theoretical studies of enzymology and the advancement of enzyme engineering techniques. This Review outlines the development process and main methods of AI applied in the structural elucidation and functional prediction of enzymes. Furthermore, it emphasizes AI-based rational design of enzymes and provides a detailed exposition of representative AI algorithms and case studies. With the support of AI technology, the comprehension of enzyme structure and function and their relationship will become deeper and more efficient, thereby promoting the widespread application of enzyme engineering in various fields.
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Affiliation(s)
- Yuhang Wang
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214126, China
| | - Shuangxin Han
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214126, China
| | - Yi Wang
- Department of Biological and Agricultural Engineering, University of California, Davis, 1 Shields Ave, Davis, California 95616, United States
| | - Quanfeng Liang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, P. R. China
| | - Wei Luo
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214126, China
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2
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Yang D, Liang H, Li X, Zhang C, Lu Z, Ma X. Unleashing the potential of microbial biosynthesis of monoterpenes via enzyme and metabolic engineering. Biotechnol Adv 2025; 79:108525. [PMID: 39921018 DOI: 10.1016/j.biotechadv.2025.108525] [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: 07/12/2024] [Revised: 12/20/2024] [Accepted: 01/27/2025] [Indexed: 02/10/2025]
Abstract
Monoterpenes (MTPs) are valuable isoprenoids widely used in cosmetics, food flavorings, pharmaceuticals, etc. Compared to plant extraction and chemical synthesis, microbial biosynthesis offers superior sustainability and efficiency in producing natural MTPs, overcoming the limitations of raw material dependency, environmental impact, and racemic mixtures inherent in these methods. This review comprehensively discusses the development of natural or non-natural biosynthetic pathways for producing regular and irregular MTPs, emphasizing the importance of enzyme and metabolic engineering to optimize monoterpene synthases (MTPSs) in various engineered microbial cell factories (MCFs). The advances in functional expression of MTPS to enhance enzyme activity, substrate channeling of MTPS with critical biosynthesis enzymes, protein engineering of MTPS, targeted localization of MTPS in the subcellular organelle, and other favorable engineering strategies are discussed in detail. Leveraging these technologies, the engineered microbes will achieve the production of the defined product profile with higher titer/yield/productivity and improved industrial adaptability. Furthermore, we highlight the important development direction for optimizing MTPS performance and biosynthetic pathways, ensuring the microbial production of natural MTPs in a more efficient and application-specific manner.
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Affiliation(s)
- Dianqi Yang
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Hong Liang
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Xuxu Li
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Chenyu Zhang
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zewei Lu
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoqiang Ma
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
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Pimtawong T, Ren J, Lee J, Lee HM, Na D. A review on computational models for predicting protein solubility. J Microbiol 2025; 63:e.2408001. [PMID: 39895070 DOI: 10.71150/jm.2408001] [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: 08/27/2024] [Accepted: 10/29/2024] [Indexed: 02/04/2025]
Abstract
Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.
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Affiliation(s)
- Teerapat Pimtawong
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Jun Ren
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Jingyu Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Hyang-Mi Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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Taguchi H, Niwa T. Reconstituted Cell-free Translation Systems for Exploring Protein Folding and Aggregation. J Mol Biol 2024; 436:168726. [PMID: 39074633 DOI: 10.1016/j.jmb.2024.168726] [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: 03/25/2024] [Revised: 07/18/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
Protein folding is crucial for achieving functional three-dimensional structures. However, the process is often hampered by aggregate formation, necessitating the presence of chaperones and quality control systems within the cell to maintain protein homeostasis. Despite a long history of folding studies involving the denaturation and subsequent refolding of translation-completed purified proteins, numerous facets of cotranslational folding, wherein nascent polypeptides are synthesized by ribosomes and folded during translation, remain unexplored. Cell-free protein synthesis (CFPS) systems are invaluable tools for studying cotranslational folding, offering a platform not only for elucidating mechanisms but also for large-scale analyses to identify aggregation-prone proteins. This review provides an overview of the extensive use of CFPS in folding studies to date. In particular, we discuss a comprehensive aggregation formation assay of thousands of Escherichia coli proteins conducted under chaperone-free conditions using a reconstituted translation system, along with its derived studies.
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Affiliation(s)
- Hideki Taguchi
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, S2-19, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan.
| | - Tatsuya Niwa
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, S2-19, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
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Wei H, Lunin VV, Alahuhta M, Himmel ME, Huang S, Bomble YJ, Zhang M. Streamlining heterologous expression of top carbonic anhydrases in Escherichia coli: bioinformatic and experimental approaches. Microb Cell Fact 2024; 23:190. [PMID: 38956607 PMCID: PMC11218372 DOI: 10.1186/s12934-024-02463-5] [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: 04/08/2024] [Accepted: 06/18/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Carbonic anhydrase (CA) enzymes facilitate the reversible hydration of CO2 to bicarbonate ions and protons. Identifying efficient and robust CAs and expressing them in model host cells, such as Escherichia coli, enables more efficient engineering of these enzymes for industrial CO2 capture. However, expression of CAs in E. coli is challenging due to the possible formation of insoluble protein aggregates, or inclusion bodies. This makes the production of soluble and active CA protein a prerequisite for downstream applications. RESULTS In this study, we streamlined the process of CA expression by selecting seven top CA candidates and used two bioinformatic tools to predict their solubility for expression in E. coli. The prediction results place these enzymes in two categories: low and high solubility. Our expression of high solubility score CAs (namely CA5-SspCA, CA6-SazCAtrunc, CA7-PabCA and CA8-PhoCA) led to significantly higher protein yields (5 to 75 mg purified protein per liter) in flask cultures, indicating a strong correlation between the solubility prediction score and protein expression yields. Furthermore, phylogenetic tree analysis demonstrated CA class-specific clustering patterns for protein solubility and production yields. Unexpectedly, we also found that the unique N-terminal, 11-amino acid segment found after the signal sequence (not present in its homologs), was essential for CA6-SazCA activity. CONCLUSIONS Overall, this work demonstrated that protein solubility prediction, phylogenetic tree analysis, and experimental validation are potent tools for identifying top CA candidates and then producing soluble, active forms of these enzymes in E. coli. The comprehensive approaches we report here should be extendable to the expression of other heterogeneous proteins in E. coli.
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Affiliation(s)
- Hui Wei
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA.
| | - Vladimir V Lunin
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | - Markus Alahuhta
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | - Michael E Himmel
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
- National Bioenergy Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | - Shu Huang
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
| | - Yannick J Bomble
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | - Min Zhang
- Biosciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA.
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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Lee HM, Thai TD, Lim W, Ren J, Na D. Functional small peptides for enhanced protein delivery, solubility, and secretion in microbial biotechnology. J Biotechnol 2023; 375:40-48. [PMID: 37652168 DOI: 10.1016/j.jbiotec.2023.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 08/14/2023] [Accepted: 08/27/2023] [Indexed: 09/02/2023]
Abstract
In microbial biotechnology, there is a constant demand for functional peptides to give new functionality to engineered proteins to address problems such as direct delivery of functional proteins into bacterial cells, enhanced protein solubility during the expression of recombinant proteins, and efficient protein secretion from bacteria. To tackle these critical issues, we selected three types of functional small peptides: cell-penetrating peptides (CPPs) enable the delivery of diverse cargoes into bacterial cytoplasm for a variety of purposes, protein-solubilizing peptide tags demonstrate remarkable efficiency in solubilizing recombinant proteins without folding interference, and signal peptides play a key role in enabling the secretion of recombinant proteins from bacterial cells. In this review, we introduced these three functional small peptides that offer effective solutions to address emerging problems in microbial biotechnology. Additionally, we summarized various engineering efforts aimed at enhancing the activity and performance of these peptides, thereby providing valuable insights into their potential for further applications.
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Affiliation(s)
- Hyang-Mi Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, the Republic of Korea
| | - Thi Duc Thai
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, the Republic of Korea
| | - Wonseop Lim
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, the Republic of Korea
| | - Jun Ren
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, the Republic of Korea.
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, the Republic of Korea.
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Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
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Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
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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: 26] [Impact Index Per Article: 13.0] [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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Suleman MT, Alkhalifah T, Alturise F, Khan YD. DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers. PeerJ 2022; 10:e14104. [PMID: 36320563 PMCID: PMC9618264 DOI: 10.7717/peerj.14104] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/01/2022] [Indexed: 01/21/2023] Open
Abstract
Background Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans. Objective For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites. Methodology The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches. Results The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors. Availability and Implementation A user-friendly web server for the proposed model was also developed and is freely available for the researchers.
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Affiliation(s)
- Muhammad Taseer Suleman
- Department of Computer Science, School of Systems and Technology, University of Management & Technology, Lahore, Pakistan
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass Qassim University, Ar Rass, Qassim, Saudi Arabia
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass Qassim University, Ar Rass, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management & Technology, Lahore, Pakistan
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Pang C, Zhang G, Liu S, Zhou J, Li J, Du G. Engineering sigma factors and chaperones for enhanced heterologous lipoxygenase production in Escherichia coli. BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2022; 15:105. [PMID: 36217152 PMCID: PMC9552429 DOI: 10.1186/s13068-022-02206-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/30/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Lipoxygenase (EC. 1.13.11.12, LOX) can catalyze the addition of oxygen into polyunsaturated fatty acids to produce hydroperoxides, which are widely used in the food, chemical, and pharmaceutical industries. In recent years, the heterologous production of LOX by Escherichia coli has attracted extensive attention. However, overexpressed recombinant LOX in E. coli aggregates and forms insoluble inclusion bodies owing to protein misfolding. RESULTS In this study, a split green fluorescent protein-based screening method was developed to screen sigma (σ) factors and molecular chaperones for soluble LOX expression. Three mutant libraries of Skp, GroES, and RpoH was analyzed using the high-throughput screening method developed herein, and a series of mutants with significantly higher yield of soluble heterologous LOX were obtained. The soluble expression level of LOX in the isolated mutants increased by 4.2- to 5.3-fold. Further, the highest LOX activity (up to 6240 ± 269 U·g-DCW-1) was observed in E. coli REopt, with the regulatory factor mutants, RpoH and GroES. Based on RNA-Seq analysis of the selected strains, E. coli Eopt, E. coli Sopt, E. coli Ropt, and wild type, amino acid substitutions in σ factors and molecular chaperones regulated the expression level of genes related to gene replication, recombination, and repair. Furthermore, the regulatory factor mutants were identified to be beneficial to the soluble expression of two other heterologous proteins, amylase and bone morphological protein 12. CONCLUSION In this study, a high-throughput screening method was developed for improved soluble LOX expression. The obtained positive mutants of the regulatory factor were analyzed and employed for the expression of other heterologous proteins, thus providing a potential solution for the inclusion-body protein.
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Affiliation(s)
- Cuiping Pang
- grid.258151.a0000 0001 0708 1323National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China ,grid.258151.a0000 0001 0708 1323Science Center for Future Foods, Jiangnan University, Wuxi, 214122 China
| | - Guoqiang Zhang
- grid.258151.a0000 0001 0708 1323National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China ,grid.258151.a0000 0001 0708 1323Science Center for Future Foods, Jiangnan University, Wuxi, 214122 China ,grid.258151.a0000 0001 0708 1323Engineering Research Center of Ministry of Education On Food Synthetic Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China
| | - Song Liu
- grid.258151.a0000 0001 0708 1323National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China ,grid.258151.a0000 0001 0708 1323Science Center for Future Foods, Jiangnan University, Wuxi, 214122 China ,grid.258151.a0000 0001 0708 1323Engineering Research Center of Ministry of Education On Food Synthetic Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China
| | - Jingwen Zhou
- grid.258151.a0000 0001 0708 1323National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China ,grid.258151.a0000 0001 0708 1323Science Center for Future Foods, Jiangnan University, Wuxi, 214122 China ,grid.258151.a0000 0001 0708 1323Engineering Research Center of Ministry of Education On Food Synthetic Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China
| | - Jianghua Li
- grid.258151.a0000 0001 0708 1323Science Center for Future Foods, Jiangnan University, Wuxi, 214122 China ,grid.258151.a0000 0001 0708 1323School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China ,grid.258151.a0000 0001 0708 1323Engineering Research Center of Ministry of Education On Food Synthetic Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China
| | - Guocheng Du
- grid.258151.a0000 0001 0708 1323Science Center for Future Foods, Jiangnan University, Wuxi, 214122 China ,grid.258151.a0000 0001 0708 1323School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China ,grid.258151.a0000 0001 0708 1323Engineering Research Center of Ministry of Education On Food Synthetic Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, 214122 Jiangsu China
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12
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Enhancement of the solubility of recombinant proteins by fusion with a short-disordered peptide. J Microbiol 2022; 60:960-967. [DOI: 10.1007/s12275-022-2122-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 10/17/2022]
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Tang S, Liao D, Li X, Lin Y, Han S, Zheng S. Cell-Free Biosynthesis System: Methodology and Perspective of in Vitro Efficient Platform for Pyruvate Biosynthesis and Transformation. ACS Synth Biol 2021; 10:2417-2433. [PMID: 34529398 DOI: 10.1021/acssynbio.1c00252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The modification of intracellular metabolic pathways by metabolic engineering has generated many engineered strains with relatively high yields of various target products in the past few decades. However, the unpredictable accumulation of toxic products, the cell membrane barrier, and competition between the carbon flux of cell growth and product synthesis have severely retarded progress toward the industrial-scale production of many essential chemicals. On the basis of an in-depth understanding of intracellular metabolic pathways, scientists intend to explore more sustainable methods and construct a cell-free biosynthesis system in vitro. In this review, the synthesis and application of pyruvate as a platform compound is used as an example to introduce cell-free biosynthesis systems. We systematically summarize a proposed methodology workflow of cell-free biosynthesis systems, including pathway design, enzyme mining, enzyme modification, multienzyme assembly, and pathway optimization. Some new methods, such as machine learning, are also mentioned in this review.
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Affiliation(s)
- Shiming Tang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
- Guangdong Research Center of Industrial Enzyme and Green Manufacturing Technology, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
| | - Daocheng Liao
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
- Guangdong Research Center of Industrial Enzyme and Green Manufacturing Technology, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
| | - Xuewen Li
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
- Guangdong Research Center of Industrial Enzyme and Green Manufacturing Technology, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
| | - Ying Lin
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
- Guangdong Research Center of Industrial Enzyme and Green Manufacturing Technology, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
| | - Shuangyan Han
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
- Guangdong Research Center of Industrial Enzyme and Green Manufacturing Technology, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
| | - Suiping Zheng
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
- Guangdong Research Center of Industrial Enzyme and Green Manufacturing Technology, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China
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14
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Melo MCR, Maasch JRMA, de la Fuente-Nunez C. Accelerating antibiotic discovery through artificial intelligence. Commun Biol 2021; 4:1050. [PMID: 34504303 PMCID: PMC8429579 DOI: 10.1038/s42003-021-02586-0] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023] Open
Abstract
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.
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Affiliation(s)
- Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline R M A Maasch
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
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15
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Siedhoff NE, Illig AM, Schwaneberg U, Davari MD. PyPEF-An Integrated Framework for Data-Driven Protein Engineering. J Chem Inf Model 2021; 61:3463-3476. [PMID: 34260225 DOI: 10.1021/acs.jcim.1c00099] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. Here, we present a general-purpose framework (PyPEF: pythonic protein engineering framework) for performing data-driven protein engineering using machine learning methods combined with techniques from signal processing and statistical physics. PyPEF guides the identification and selection of beneficial proteins of a defined sequence space by systematically or randomly exploring the fitness of variants and by sampling random evolution pathways. The performance of PyPEF was evaluated concerning its predictive accuracy and throughput on four public protein and enzyme data sets using common regression models. It was proved that the program could efficiently predict the fitness of protein sequences for different target properties (predictive models with coefficient of determination values ranging from 0.58 to 0.92). By combining machine learning and protein evolution, PyPEF enabled the screening of proteins with various functions, reaching a screening capacity of more than 500,000 protein sequence variants in the timeframe of only a few minutes on a personal computer. PyPEF displayed significant accuracies on four public data sets (different proteins and properties) and underlined the potential of integrating data-driven technologies for covering different philosophies by either predicting the fitness of the variants to the highest accuracy accounting for epistatic effects or capturing the general trend of introduced mutations on the fitness in directed protein evolution campaigns. In essence, PyPEF can provide a powerful solution to current sequence exploration and combinatorial problems faced in protein engineering through exhaustive in silico screening of the sequence space.
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Affiliation(s)
- Niklas E Siedhoff
- Institute of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074 Aachen, Germany
| | | | - Ulrich Schwaneberg
- Institute of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074 Aachen, Germany.,DWI-Leibniz Institute for Interactive Materials, Forckenbeckstraße 50, 52074 Aachen, Germany
| | - Mehdi D Davari
- Institute of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074 Aachen, Germany
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16
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Shukla V, Runthala A, Rajput VS, Chandrasai PD, Tripathi A, Phulara SC. Computational and synthetic biology approaches for the biosynthesis of antiviral and anticancer terpenoids from Bacillus subtilis. Med Chem 2021; 18:307-322. [PMID: 34254925 DOI: 10.2174/1573406417666210712211557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 04/18/2021] [Accepted: 04/25/2021] [Indexed: 11/22/2022]
Abstract
Recent advancements in medicinal research have identified several antiviral and anticancer terpenoids that are usually deployed as a source of flavor, fragrances and pharmaceuticals. Under the current COVID-19 pandemic conditions, natural therapeutics with least side effects are the need of the hour to save the patients, especially, which are pre-affected with other medical complications. Although, plants are the major sources of terpenoids; however, for the environmental concerns, the global interest has shifted to the biocatalytic production of molecules from microbial sources. The gram-positive bacterium Bacillus subtilis is a suitable host in this regard due to its GRAS (generally regarded as safe) status, ease in genetic manipulations and wide industrial acceptability. The B. subtilis synthesizes its terpenoid molecules from 1-deoxy-d-xylulose-5-phosphate (DXP) pathway, a common route in almost all microbial strains. Here, we summarize the computational and synthetic biology approaches to improve the production of terpenoid-based therapeutics from B. subtilis by utilizing DXP pathway. We focus on the in-silico approaches for screening the functionally improved enzyme-variants of the two crucial enzymes namely, the DXP synthase (DXS) and farnesyl pyrophosphate synthase (FPPS). The approaches for engineering the active sites are subsequently explained. It will be helpful to construct the functionally improved enzymes for the high-yield production of terpenoid-based anticancer and antiviral metabolites, which would help to reduce the cost and improve the availability of such therapeutics for the humankind.
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Affiliation(s)
- Vibha Shukla
- Food, Drug and Chemical Toxicology Group, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31 Mahatma Gandhi Marg, Lucknow-226001, India
| | - Ashish Runthala
- Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522502, Andhra Pradesh, India
| | | | - Potla Durthi Chandrasai
- Department of Biotechnology, National Institute of Technology Warangal, Warangal-506004, Telangana, India
| | - Anurag Tripathi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad- 201002, India
| | - Suresh Chandra Phulara
- Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522502, Andhra Pradesh, India
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17
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Chen J, Zheng S, Zhao H, Yang Y. Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map. J Cheminform 2021; 13:7. [PMID: 33557952 PMCID: PMC7869490 DOI: 10.1186/s13321-021-00488-1] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/20/2021] [Indexed: 11/26/2022] Open
Abstract
Protein solubility is significant in producing new soluble proteins that can reduce the cost of biocatalysts or therapeutic agents. Therefore, a computational model is highly desired to accurately predict protein solubility from the amino acid sequence. Many methods have been developed, but they are mostly based on the one-dimensional embedding of amino acids that is limited to catch spatially structural information. In this study, we have developed a new structure-aware method GraphSol to predict protein solubility by attentive graph convolutional network (GCN), where the protein topology attribute graph was constructed through predicted contact maps only from the sequence. GraphSol was shown to substantially outperform other sequence-based methods. The model was proven to be stable by consistent [Formula: see text] of 0.48 in both the cross-validation and independent test of the eSOL dataset. To our best knowledge, this is the first study to utilize the GCN for sequence-based protein solubility predictions. More importantly, this architecture could be easily extended to other protein prediction tasks requiring a raw protein sequence.
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Affiliation(s)
- Jianwen Chen
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Shuangjia Zheng
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Huiying Zhao
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China.
- Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-Sen University), Guangzhou, 510000, China.
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18
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Katsimpouras C, Stephanopoulos G. Enzymes in biotechnology: Critical platform technologies for bioprocess development. Curr Opin Biotechnol 2021; 69:91-102. [PMID: 33422914 DOI: 10.1016/j.copbio.2020.12.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/09/2020] [Accepted: 12/08/2020] [Indexed: 01/02/2023]
Abstract
Enzymes are core elements of biosynthetic pathways employed in the synthesis of numerous bioproducts. Here, we review enzyme promiscuity, enzyme engineering, enzyme immobilization, and cell-free systems as fundamental strategies of bioprocess development. Initially, promiscuous enzymes are the first candidates in the quest for new activities to power new, artificial, or bypass pathways that expand substrate range and catalyze the production of new products. If the activity or regulation of available enzymes is unsuitable for a process, protein engineering can be applied to improve them to the required level. When cell toxicity and low productivity cannot be engineered away, cell-free systems are an attractive option, especially in combination with enzyme immobilization that allows extended enzyme use. Overall, the above methods support powerful platforms for bioprocess development and optimization.
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Affiliation(s)
- Constantinos Katsimpouras
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139 MA, USA
| | - Gregory Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139 MA, USA.
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19
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Zhou K, Ng W, Cortés-Peña Y, Wang X. Increasing metabolic pathway flux by using machine learning models. Curr Opin Biotechnol 2020; 66:179-185. [PMID: 32896771 DOI: 10.1016/j.copbio.2020.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 08/03/2020] [Accepted: 08/11/2020] [Indexed: 01/19/2023]
Abstract
Machine learning is transforming many industries through self-improving models that are fueled by big data and high computing power. The field of metabolic engineering, which uses cellular biochemical network to manufacture useful small molecules, has also witnessed the first wave of machine learning applications in the past five years, covering reaction route design, enzyme selection, pathway engineering and process optimization. This review focuses on pathway engineering, and uses a few recent studies to illustrate (1) how machine learning models can be useful in overcoming an evident rate-limiting step, and (2) how the models may be used to exhaustively search - or guide optimization algorithms to search - a large design space when the cellular regulation of the reaction network is more convoluted.
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Affiliation(s)
- Kang Zhou
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore.
| | - Wenfa Ng
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Yoel Cortés-Peña
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Xiaonan Wang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
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20
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Han X, Ning W, Ma X, Wang X, Zhou K. Improving protein solubility and activity by introducing small peptide tags designed with machine learning models. Metab Eng Commun 2020; 11:e00138. [PMID: 32642423 PMCID: PMC7334598 DOI: 10.1016/j.mec.2020.e00138] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/26/2020] [Accepted: 06/15/2020] [Indexed: 01/20/2023] Open
Abstract
Improving catalytic ability of enzymes is critical to the success of many metabolic engineering projects, but the search space of possible protein mutants is too large to explore exhaustively through experiments. To some extent, highly soluble enzymes tend to exhibit high activity due to their better folding quality. Here, we demonstrate that an optimization algorithm based on a regression model can effectively design short peptide tags to improve solubility of a few model enzymes. Based on the protein sequence information, a support vector regression model we recently developed was used to evaluate protein solubility after small peptide tags were introduced to a target protein. The optimization algorithm guided the sequences of the tags to evolve towards variants that had higher solubility. The optimization results were validated successfully by measuring solubility and activity of the model enzyme with and without the identified tags. The solubility of one protein (tyrosine ammonia lyase) was more than doubled and its activity was improved by 250%. This strategy successfully increased solubility of another two enzymes (aldehyde dehydrogenase and 1-deoxy-D-xylulose-5-phosphate synthase) we tested. The presented optimization methodology thus provides a valuable tool for improving enzyme performance for metabolic engineering and other biotechnology projects. Short tags were added to proteins to improve their protein solubility. Machine learning model was used to optimize amino acid composition of the tags. Multiple proteins’ solubility was substantially improved. Catalytic activity of one enzyme was increased when its solubility was enhanced.
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Affiliation(s)
- Xi Han
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Wenbo Ning
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Xiaoqiang Ma
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, 138602, Singapore
| | - Xiaonan Wang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Kang Zhou
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore.,Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, 138602, Singapore
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21
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Li C, Swofford CA, Sinskey AJ. Modular engineering for microbial production of carotenoids. Metab Eng Commun 2020; 10:e00118. [PMID: 31908924 PMCID: PMC6938962 DOI: 10.1016/j.mec.2019.e00118] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 12/02/2019] [Accepted: 12/08/2019] [Indexed: 12/12/2022] Open
Abstract
There is an increasing demand for carotenoids due to their applications in the food, flavor, pharmaceutical and feed industries, however, the extraction and synthesis of these compounds can be expensive and technically challenging. Microbial production of carotenoids provides an attractive alternative to the negative environmental impacts and cost of chemical synthesis or direct extraction from plants. Metabolic engineering and synthetic biology approaches have been widely utilized to reconstruct and optimize pathways for carotenoid overproduction in microorganisms. This review summarizes the current advances in microbial engineering for carotenoid production and divides the carotenoid biosynthesis building blocks into four distinct metabolic modules: 1) central carbon metabolism, 2) cofactor metabolism, 3) isoprene supplement metabolism and 4) carotenoid biosynthesis. These four modules focus on redirecting carbon flux and optimizing cofactor supplements for isoprene precursors needed for carotenoid synthesis. Future perspectives are also discussed to provide insights into microbial engineering principles for overproduction of carotenoids.
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Affiliation(s)
- Cheng Li
- Department of Biology, Massachusetts Institute of Technology, Boston, MA, 02139, USA
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, 138602, Singapore
| | - Charles A. Swofford
- Department of Biology, Massachusetts Institute of Technology, Boston, MA, 02139, USA
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, 138602, Singapore
| | - Anthony J. Sinskey
- Department of Biology, Massachusetts Institute of Technology, Boston, MA, 02139, USA
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, 138602, Singapore
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22
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One Pot Use of Combilipases for Full Modification of Oils and Fats: Multifunctional and Heterogeneous Substrates. Catalysts 2020. [DOI: 10.3390/catal10060605] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Lipases are among the most utilized enzymes in biocatalysis. In many instances, the main reason for their use is their high specificity or selectivity. However, when full modification of a multifunctional and heterogeneous substrate is pursued, enzyme selectivity and specificity become a problem. This is the case of hydrolysis of oils and fats to produce free fatty acids or their alcoholysis to produce biodiesel, which can be considered cascade reactions. In these cases, to the original heterogeneity of the substrate, the presence of intermediate products, such as diglycerides or monoglycerides, can be an additional drawback. Using these heterogeneous substrates, enzyme specificity can promote that some substrates (initial substrates or intermediate products) may not be recognized as such (in the worst case scenario they may be acting as inhibitors) by the enzyme, causing yields and reaction rates to drop. To solve this situation, a mixture of lipases with different specificity, selectivity and differently affected by the reaction conditions can offer much better results than the use of a single lipase exhibiting a very high initial activity or even the best global reaction course. This mixture of lipases from different sources has been called “combilipases” and is becoming increasingly popular. They include the use of liquid lipase formulations or immobilized lipases. In some instances, the lipases have been coimmobilized. Some discussion is offered regarding the problems that this coimmobilization may give rise to, and some strategies to solve some of these problems are proposed. The use of combilipases in the future may be extended to other processes and enzymes.
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23
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Kuroda D, Tsumoto K. Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design. J Pharm Sci 2020; 109:1631-1651. [DOI: 10.1016/j.xphs.2020.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/25/2019] [Accepted: 01/10/2020] [Indexed: 12/18/2022]
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Affiliation(s)
- Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Ann’s Hospital, 602 00 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Ann’s Hospital, 602 00 Brno, Czech Republic
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25
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Han X, Zhang L, Zhou K, Wang X. ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.106533] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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