Publications by authors named "Gi Bae Kim"

9 Publications

  • Page 1 of 1

Synthetic Formatotrophs for One-Carbon Biorefinery.

Adv Sci (Weinh) 2021 06 3;8(12):2100199. Epub 2021 May 3.

Metabolic and Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering (BK21 Plus Program) Institute for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea.

The use of CO as a carbon source in biorefinery is of great interest, but the low solubility of CO in water and the lack of efficient CO assimilation pathways are challenges to overcome. Formic acid (FA), which can be easily produced from CO and more conveniently stored and transported than CO, is an attractive CO-equivalent carbon source as it can be assimilated more efficiently than CO by microorganisms and also provides reducing power. Although there are native formatotrophs, they grow slowly and are difficult to metabolically engineer due to the lack of genetic manipulation tools. Thus, much effort is exerted to develop efficient FA assimilation pathways and synthetic microorganisms capable of growing solely on FA (and CO). Several innovative strategies are suggested to develop synthetic formatotrophs through rational metabolic engineering involving new enzymes and reconstructed FA assimilation pathways, and/or adaptive laboratory evolution (ALE). In this paper, recent advances in development of synthetic formatotrophs are reviewed, focusing on biological FA and CO utilization pathways, enzymes involved and newly developed, and metabolic engineering and ALE strategies employed. Also, future challenges in cultivating formatotrophs to higher cell densities and producing chemicals from FA and CO are discussed.
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http://dx.doi.org/10.1002/advs.202100199DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224422PMC
June 2021

DeepTFactor: A deep learning-based tool for the prediction of transcription factors.

Proc Natl Acad Sci U S A 2021 01;118(2)

Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology, 34141 Daejeon, Republic of Korea;

A transcription factor (TF) is a sequence-specific DNA-binding protein that modulates the transcription of a set of particular genes, and thus regulates gene expression in the cell. TFs have commonly been predicted by analyzing sequence homology with the DNA-binding domains of TFs already characterized. Thus, TFs that do not show homologies with the reported ones are difficult to predict. Here we report the development of a deep learning-based tool, DeepTFactor, that predicts whether a protein in question is a TF. DeepTFactor uses a convolutional neural network to extract features of a protein. It showed high performance in predicting TFs of both eukaryotic and prokaryotic origins, resulting in 1 scores of 0.8154 and 0.8000, respectively. Analysis of the gradients of prediction score with respect to input suggested that DeepTFactor detects DNA-binding domains and other latent features for TF prediction. DeepTFactor predicted 332 candidate TFs in K-12 MG1655. Among them, 84 candidate TFs belong to the y-ome, which is a collection of genes that lack experimental evidence of function. We experimentally validated the results of DeepTFactor prediction by further characterizing genome-wide binding sites of three predicted TFs, YqhC, YiaU, and YahB. Furthermore, we made available the list of 4,674,808 TFs predicted from 73,873,012 protein sequences in 48,346 genomes. DeepTFactor will serve as a useful tool for predicting TFs, which is necessary for understanding the regulatory systems of organisms of interest. We provide DeepTFactor as a stand-alone program, available at https://bitbucket.org/kaistsystemsbiology/deeptfactor.
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http://dx.doi.org/10.1073/pnas.2021171118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812831PMC
January 2021

Glutaric acid production by systems metabolic engineering of an l-lysine-overproducing .

Proc Natl Acad Sci U S A 2020 12 16;117(48):30328-30334. Epub 2020 Nov 16.

Metabolic and Biomolecular Engineering National Research Laboratory, Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology, Yuseong-gu, 34141 Daejeon, Republic of Korea;

There is increasing industrial demand for five-carbon platform chemicals, particularly glutaric acid, a widely used building block chemical for the synthesis of polyesters and polyamides. Here we report the development of an efficient glutaric acid microbial producer by systems metabolic engineering of an l-lysine-overproducing BE strain. Based on our previous study, an optimal synthetic metabolic pathway comprising l-lysine monooxygenase () and 5-aminovaleramide amidohydrolase () genes and 4-aminobutyrate aminotransferase () and succinate-semialdehyde dehydrogenase () genes, was introduced into the BE strain. Through system-wide analyses including genome-scale metabolic simulation, comparative transcriptome analysis, and flux response analysis, 11 target genes to be manipulated were identified and expressed at desired levels to increase the supply of direct precursor l-lysine and reduce precursor loss. A glutaric acid exporter encoded by was discovered and overexpressed to further enhance glutaric acid production. Fermentation conditions, including oxygen transfer rate, batch-phase glucose level, and nutrient feeding strategy, were optimized for the efficient production of glutaric acid. Fed-batch culture of the final engineered strain produced 105.3 g/L of glutaric acid in 69 h without any byproduct. The strategies of metabolic engineering and fermentation optimization described here will be useful for developing engineered microorganisms for the high-level bio-based production of other chemicals of interest to industry.
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http://dx.doi.org/10.1073/pnas.2017483117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720191PMC
December 2020

Tools and strategies of systems metabolic engineering for the development of microbial cell factories for chemical production.

Chem Soc Rev 2020 Jul;49(14):4615-4636

Metabolic and Biomolecular Engineering National Research Laboratory, Systems Metabolic Engineering and Systems Healthcare (SMESH) Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. and Bioinformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea and BioProcess Engineering Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.

Sustainable production of chemicals from renewable non-food biomass has become a promising alternative to overcome environmental issues caused by our heavy dependence on fossil resources. Systems metabolic engineering, which integrates traditional metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, is enabling the development of microbial cell factories capable of efficiently producing a myriad of chemicals and materials including biofuels, bulk and fine chemicals, polymers, amino acids, natural products and drugs. In this paper, many tools and strategies of systems metabolic engineering, including in silico genome-scale metabolic simulation, sophisticated enzyme engineering, optimal gene expression modulation, in vivo biosensors, de novo pathway design, and genomic engineering, employed for developing microbial cell factories are reviewed. Also, detailed procedures of systems metabolic engineering used to develop microbial strains producing chemicals and materials are showcased. Finally, future challenges and perspectives in further advancing systems metabolic engineering and establishing biorefineries are discussed.
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http://dx.doi.org/10.1039/d0cs00155dDOI Listing
July 2020

Enhanced succinic acid production by Mannheimia employing optimal malate dehydrogenase.

Nat Commun 2020 04 23;11(1):1970. Epub 2020 Apr 23.

Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

Succinic acid (SA), a dicarboxylic acid of industrial importance, can be efficiently produced by metabolically engineered Mannheimia succiniciproducens. Malate dehydrogenase (MDH) is one of the key enzymes for SA production, but has not been well characterized. Here we report biochemical and structural analyses of various MDHs and development of hyper-SA producing M. succiniciproducens by introducing the best MDH. Corynebacterium glutamicum MDH (CgMDH) shows the highest specific activity and least substrate inhibition, whereas M. succiniciproducens MDH (MsMDH) shows low specific activity at physiological pH and strong uncompetitive inhibition toward oxaloacetate (ki of 67.4 and 588.9 μM for MsMDH and CgMDH, respectively). Structural comparison of the two MDHs reveals a key residue influencing the specific activity and susceptibility to substrate inhibition. A high-inoculum fed-batch fermentation of the final strain expressing cgmdh produces 134.25 g L of SA with the maximum productivity of 21.3 g L h, demonstrating the importance of enzyme optimization in strain development.
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http://dx.doi.org/10.1038/s41467-020-15839-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7181634PMC
April 2020

Machine learning applications in systems metabolic engineering.

Curr Opin Biotechnol 2020 08 30;64:1-9. Epub 2019 Sep 30.

Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea. Electronic address:

Systems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of bio big data, for example, omics data, has led to active application of machine learning techniques across various stages of systems metabolic engineering, including host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation. In this paper, recent contributions of machine learning approaches to each major step of systems metabolic engineering are discussed. As the use of machine learning in systems metabolic engineering will become more widespread in accordance with the ever-increasing volume of bio big data, future prospects are also provided for the successful applications of machine learning.
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http://dx.doi.org/10.1016/j.copbio.2019.08.010DOI Listing
August 2020

Current status and applications of genome-scale metabolic models.

Genome Biol 2019 06 13;20(1):121. Epub 2019 Jun 13.

Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
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http://dx.doi.org/10.1186/s13059-019-1730-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567666PMC
June 2019

Enhanced production of poly‑3‑hydroxybutyrate (PHB) by expression of response regulator DR1558 in recombinant Escherichia coli.

Int J Biol Macromol 2019 Jun 7;131:29-35. Epub 2019 Mar 7.

Department of Biotechnology and Bioengineering, Interdisciplinary Program for Bioenergy & Biomaterials, Chonnam National University, Gwangju 61186, Republic of Korea. Electronic address:

During microbial production of target product, accumulation of by-products and target product itself may be toxic to host strain. Thus, development of abiotic stress tolerant strains are essential to achieve high productivity of target product with sustained metabolism. Expression of DR1558 from Deinococcus radiodurans, a response regulator in two-component signal transduction system, was reported to increase the tolerance against oxidative stress in Escherichia coli. In this study, the effect of overexpression of DR1558 was examined on poly‑3‑hydroxybutyrate (PHB) production in recombinant E. coli expressing Ralstonia eutropha PHB biosynthesis genes. It was found that dr1558 overexpressing E. coli produced 5.31 g PHB/L and 9.24 g dry cell weight/L, while control strain produced 1.52 g PHB/L and 4.47 g dry cell weight/L in 48 h shake-flask cultivation. Transcriptional analysis of E. coli suggested that DR1558 could improve the expression efficiency of the genes related to central carbon metabolism and threonine bypass pathway in PHB producing E. coli. When thrABC genes were overexpressed, PHB content was increased in recombinant E. coli, which suggests that stress-tolerant genes from extremophiles should be useful in the development of engineered strains for the production of bio-based products.
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http://dx.doi.org/10.1016/j.ijbiomac.2019.03.044DOI Listing
June 2019

Metabolic engineering of Corynebacterium glutamicum for the production of glutaric acid, a C5 dicarboxylic acid platform chemical.

Metab Eng 2019 01 23;51:99-109. Epub 2018 Aug 23.

Bio-based Chemistry Research Center, Advanced Convergent Chemistry Division, Korea Research Institute of Chemical Technology, P.O. Box 107, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114 Republic of Korea. Electronic address:

Corynebacterium glutamicum was metabolically engineered for the production of glutaric acid, a C5 dicarboxylic acid that can be used as platform building block chemical for nylons and plasticizers. C. glutamicum gabT and gabD genes and Pseudomonas putida davT and davD genes encoding 5-aminovalerate transaminase and glutarate semialdehyde dehydrogenase, respectively, were examined in C. glutamicum for the construction of a glutaric acid biosynthesis pathway along with P. putida davB and davA genes encoding lysine 2-monooxygenase and delta-aminovaleramidase, respectively. The glutaric acid biosynthesis pathway constructed in recombinant C. glutamicum was engineered by examining strong synthetic promoters P and P, C. glutamicum codon-optimized davTDBA genes, and modification of davB gene with an N-terminal His-tag to improve the production of glutaric acid. It was found that use of N-terminal His-tagged DavB was most suitable for the production of glutaric acid from glucose. Fed-batch fermentation using the final engineered C. glutamicum H30_GA strain, expressing davTDA genes along with davB fused with His-tag at N-terminus could produce 24.5 g/L of glutaric acid with low accumulation of l-lysine (1.7 g/L), wherein 5-AVA accumulation was not observed during fermentation.
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http://dx.doi.org/10.1016/j.ymben.2018.08.007DOI Listing
January 2019
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