Journal of Microbiology

Review

Untranslated region engineering strategies for gene overexpression, fine-tuning, and dynamic regulation: Jun Ren, So Hee Oh, Dokyun Na; J. Microbiol. 2025;63(3):e2501033. Published online March 28, 2025; DOI: https://doi.org/10.71150/jm.2501033

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Precise and tunable gene expression is crucial for various biotechnological applications, including protein overexpression, fine-tuned metabolic pathway engineering, and dynamic gene regulation. Untranslated regions (UTRs) of mRNAs have emerged as key regulatory elements that modulate transcription and translation. In this review, we explore recent advances in UTR engineering strategies for bacterial gene expression optimization. We discuss approaches for enhancing protein expression through AU-rich elements, RG4 structures, and synthetic dual UTRs, as well as ProQC systems that improve translation fidelity. Additionally, we examine strategies for fine-tuning gene expression using UTR libraries and synthetic terminators that balance metabolic flux. Finally, we highlight riboswitches and toehold switches, which enable dynamic gene regulation in response to environmental or metabolic cues. The integration of these UTR-based regulatory tools provides a versatile and modular framework for optimizing bacterial gene expression, enhancing metabolic engineering, and advancing synthetic biology applications.

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Advancing microbial engineering through synthetic biology
Ki Jun Jeong
Journal of Microbiology.2025; 63(3): e2503100. CrossRef

Minireview

A review on computational models for predicting protein solubility: Teerapat Pimtawong, Jun Ren, Jingyu Lee, Hyang-Mi Lee, Dokyun Na; J. Microbiol. 2025;63(1):e.2408001. Published online January 24, 2025; DOI: https://doi.org/10.71150/jm.2408001

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Abstract PDF: 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.

Journal Article

Enhancement of the solubility of recombinant proteins by fusion with a short-disordered peptide: Jun Ren , Suhee Hwang , Junhao Shen , Hyeongwoo Kim , Hyunjoo Kim , Jieun Kim , Soyoung Ahn , Min-gyun Kim , Seung Ho Lee , Dokyun Na; J. Microbiol. 2022;60(9):960-967. Published online July 14, 2022; DOI: https://doi.org/10.1007/s12275-022-2122-z

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Abstract

In protein biotechnology, large soluble fusion partners are widely utilized for increased yield and solubility of recombinant proteins. However, the production of additional large fusion partners poses an additional burden to the host, leading to a decreased protein yield. In this study, we identified two highly disordered short peptides that were able to increase the solubility of an artificially engineered aggregationprone protein, GFP-GFIL4, from 0.6% to 61% (D3-DP00592) and 46% (D4-DP01038) selected from DisProt database. For further confirmation, the peptides were applied to two insoluble E. coli proteins (YagA and YdiU). The peptides also enhanced solubility from 52% to 90% (YagA) and from 27% to 93% (YdiU). Their ability to solubilize recombinant proteins was comparable with strong solubilizing tags, maltosebinding protein (40 kDa) and TrxA (12 kDa), but much smaller (< 7 kDa) in size. For practical application, the two peptides were fused with a restriction enzyme, I-SceI, and they increased I-SceI solubility from 24% up to 75%. The highly disordered peptides did not affect the activity of I-SceI while I-SceI fused with MBP or TrxA displayed no restriction activity. Despite the small size, the highly disordered peptides were able to solubilize recombinant proteins as efficiently as conventional fusion tags and did not interfere with the function of recombinant proteins. Consequently, the identified two highly disordered peptides would have practical utility in protein biotechnology and industry.

Citations

Citations to this article as recorded by

A review on computational models for predicting protein solubility
Teerapat Pimtawong, Jun Ren, Jingyu Lee, Hyang-Mi Lee, Dokyun Na
Journal of Microbiology.2025; 63(1): e:2408001. CrossRef
Synthetic intrinsically disordered protein fusion tags that enhance protein solubility
Nicholas C. Tang, Jonathan C. Su, Yulia Shmidov, Garrett Kelly, Sonal Deshpande, Parul Sirohi, Nikhil Peterson, Ashutosh Chilkoti
Nature Communications.2024;[Epub] CrossRef
Biosynthesis of Indigo Dyes and Their Application in Green Chemical and Visual Biosensing for Heavy Metals
Yan Guo, Shun-Yu Hu, Can Wu, Chao-Xian Gao, Chang-Ye Hui
ACS Omega.2024; 9(31): 33868. CrossRef
Functional small peptides for enhanced protein delivery, solubility, and secretion in microbial biotechnology
Hyang-Mi Lee, Thi Duc Thai, Wonseop Lim, Jun Ren, Dokyun Na
Journal of Biotechnology.2023; 375: 40. CrossRef
Directed Evolution of Soluble α-1,2-Fucosyltransferase Using Kanamycin Resistance Protein as a Phenotypic Reporter for Efficient Production of 2'-Fucosyllactose
Jonghyeok Shin, Seungjoo Kim, Wonbeom Park, Kyoung Chan Jin, Sun-Ki Kim, Dae-Hyuk Kweon
Journal of Microbiology and Biotechnology.2022; 32(11): 1471. CrossRef
Effects of spray drying, freeze drying, and vacuum drying on physicochemical and nutritional properties of protein peptide powder from salted duck egg white
Tianyin Du, Jicheng Xu, Shengnan Zhu, Xinjun Yao, Jun Guo, Weiqiao Lv
Frontiers in Nutrition.2022;[Epub] CrossRef

Editorial

User guides for biologists to learn computational methods: Dokyun Na; J. Microbiol. 2020;58(3):173-175. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9723-1

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Abstract

System-wide studies of a given molecular type are referred to as “omics.” These include genomics, proteomics, and metabolomics, among others. Recent biotechnological advances allow for high-throughput measurement of cellular components, and thus it becomes possible to take a snapshot of all molecules inside cells, a form of omics study. Advances in computational modeling methods also make it possible to predict cellular mechanisms from the snapshots. These technologies have opened an era of computation-based biology. Component snapshots allow the discovery of gene-phenotype relationships in diseases, microorganisms in the human body, etc. Computational models allow us to predict new outcomes, which are useful in strain design in metabolic engineering and drug discovery from protein-ligand interactions. However, as the quantity of data increases or the model becomes complicated, the process becomes less accessible to biologists. In this special issue, six protocol articles are presented as user guides in the field of computational biology.

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Transcript-specific selective translation by specialized ribosomes bearing genome-encoded heterogeneous rRNAs in V. vulnificus CMCP6
Younkyung Choi, Minju Joo, Wooseok Song, Minho Lee, Hana Hyeon, Hyun-Lee Kim, Ji-Hyun Yeom, Kangseok Lee, Eunkyoung Shin
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Trans-acting regulators of ribonuclease activity
Jaejin Lee, Minho Lee, Kangseok Lee
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Regulator of ribonuclease activity modulates the pathogenicity of Vibrio vulnificus
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Journal Article

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation: Bilal Shaker , Myung-Sang Yu , Jingyu Lee , Yongmin Lee , Chanjin Jung , Dokyun Na; J. Microbiol. 2020;58(3):235-244. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9563-z

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Abstract

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate- L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Citations

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Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope
Smita Singh, Pranjal Kumar Singh, Kapil Sachan, Mukesh Kumar, Poonam Bhardwaj
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In Silico and In Vitro Studies of Antibacterial Activity of Cow Urine Distillate (CUD)
LokRaj Pant, Shankar Thapa, Bibek Dahal, Ravindra Khadka, Mahalakshmi Suresha Biradar, Rajeev K. Singla
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Antimicrobial Peptides: The Game-Changer in the Epic Battle Against Multidrug-Resistant Bacteria
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Review

[Minireview]Recent advances in genetic engineering tools based on synthetic biology: Jun Ren , Jingyu Lee , Dokyun Na; J. Microbiol. 2020;58(1):1-10. Published online January 2, 2020; DOI: https://doi.org/10.1007/s12275-020-9334-x

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Abstract

Genome-scale engineering is a crucial methodology to rationally regulate microbiological system operations, leading to expected biological behaviors or enhanced bioproduct yields. Over the past decade, innovative genome modification technologies have been developed for effectively regulating and manipulating genes at the genome level. Here, we discuss the current genome-scale engineering technologies used for microbial engineering. Recently developed strategies, such as clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9, multiplex automated genome engineering (MAGE), promoter engineering, CRISPR-based regulations, and synthetic small regulatory RNA (sRNA)-based knockdown, are considered as powerful tools for genome-scale engineering in microbiological systems. MAGE, which modifies specific nucleotides of the genome sequence, is utilized as a genome-editing tool. Contrastingly, synthetic sRNA, CRISPRi, and CRISPRa are mainly used to regulate gene expression without modifying the genome sequence. This review introduces the recent genome-scale editing and regulating technologies and their applications in metabolic engineering.

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