- Enhancement of the solubility of recombinant proteins by fusion with a short-disordered peptide
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Jun Ren , Suhee Hwang , Junhao Shen , Hyeongwoo Kim , Hyunjoo Kim , Jieun Kim , Soyoung Ahn , Min-gyun Kim , Seung Ho Lee , Dokyun Na
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J. Microbiol. 2022;60(9):960-967. Published online July 14, 2022
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DOI: https://doi.org/10.1007/s12275-022-2122-z
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90
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 Abstract
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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.
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Citations
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- User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation
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Bilal Shaker , Myung-Sang Yu , Jingyu Lee , Yongmin Lee , Chanjin Jung , Dokyun Na
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J. Microbiol. 2020;58(3):235-244. Published online February 27, 2020
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DOI: https://doi.org/10.1007/s12275-020-9563-z
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32
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35
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Abstract
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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.
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Dokyun Na
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Dokyun Na
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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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71
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Abstract
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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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Citations
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Jang-Cheon Cho
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Jaejin Lee, Minho Lee, Kangseok Lee
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Jaejin Lee, Eunkyoung Shin, Jaeyeong Park, Minho Lee, Kangseok Lee
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- [Minireview]Recent advances in genetic engineering tools based on synthetic biology
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Jun Ren , Jingyu Lee , Dokyun Na
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J. Microbiol. 2020;58(1):1-10. Published online January 2, 2020
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DOI: https://doi.org/10.1007/s12275-020-9334-x
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85
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25
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25
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Abstract
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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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