• 대한한의학방제학회지
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• 제30권3호
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• pp.205-210
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• 2022

Conventional pharmacology has followed the notion of the reductionist 'single target selective drug paradigm'. Network pharmacology has made conventional pharmacology newer while meeting the challenges of this era. Conventional pharmacological methods have not solved the problems of Korean Medicine. For this reason, Network pharmaco- logy needs urgently and desperately for Korean medicine research. However, the information on drug interactions in herbal medicines is complex and less known. There are still some hurdles before network pharmacology emerges, one factor which constitutes Korean medicine research. There is a need to look for solutions other than inheriting the network pharmacology to solve problems that Korean medicine has before. The way of 'in silico' research should be the best to meet this challenge. With the help of 'in silico' research, there might have been emerged new findings of experimental data in Korean Medicine. If 'herbalomics' has been close to foundation through the 'in silico' method, it will contribute to the formation of modern Korean medicine and, simultaneously, come to a foundation for revitalizing exchanges with orthodox Western medicine. Eventually, it ends with a significant profitable and healthy result for the patients.

• 제어로봇시스템학회논문지
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• 제11권10호
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• pp.823-829
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• 2005

We have newly constructed an in silico model of fermentative metabolism for Lactococcus lactis in order to analyze the characteristics of metabolite flux for dynamic network. A rigorous mathematical model for metabolic flux has been developed and simulation researches have been performed by using GEPASI program. In this simulation task, we were able to predict the whole flux distribution trend for lactate metabolism and analyze the flux ratio on the pyruvate branch point by using metabolic flux analysis(MFA). And we have studied flux control coefficients of key reaction steps in the model by using metabolic control analysis(MCA). The role of pyruvate branch seems to be essential for the secretion of lactate and other organic byproducts. Then we have made an effort to elucidate its metabolic regulation characteristics and key reaction steps, and find an optimal condition for the production of lactate.

• Genomics & Informatics
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• 제17권3호
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• pp.31.1-31.7
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• 2019

We sought the novel concept, transcript capacity (TC) and analyzed TC. Our approach to estimate TC was through an in silico method. TC refers to the capacity that a transcript exerts in a cell as enzyme or protein function after translation. We used the genome-wide association study (GWAS) beta effect and transcription level in RNA-sequencing to estimate TC. The trait was body fat percent and the transcript reads were obtained from the human protein atlas. The assumption was that the GWAS beta effect is the gene's effect and TC was related to the corresponding gene effect and transcript reads. Further, we surveyed gene ontology (GO) in the highest TC and the lowest TC genes. The most frequent GOs with the highest TC were neuronal-related and cell projection organization related. The most frequent GOs with the lowest TC were wound-healing related and embryo development related. We expect that our analysis contributes to estimating TC in the diverse species and playing a benevolent role to the new bioinformatic analysis.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.221-228
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• 2003

This paper presents a novel method that can identify the individual's haplotype from the given genotypes. Because of the limitation of the conventional single-locus analysis, haplotypes have gained increasing attention in the mapping of complex-disease genes. Conventionally there are two approaches which resolve the individual's haplotypes. One is the molecular haplotypings which have many potential limitations in cost and convenience. The other is the in-silico haplotypings which phase the haplotypes from the diploid genotyped populations, and are cost effective and high-throughput method. In-silico haplotyping is divided into two sub-categories - statistical and computational method. The former computes the frequencies of the common haplotypes, and then resolves the individual's haplotypes. The latter directly resolves the individual's haplotypes using the perfect phylogeny model first proposed by Dan Gusfield [7]. Our method combines two approaches in order to increase the accuracy and the running time. The individuals' haplotypes are resolved by considering the MLE (Maximum Likelihood Estimation) in the process of computing the frequencies of the common haplotypes.

• Bulletin of the Korean Chemical Society
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• 제32권4호
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• pp.1237-1240
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• 2011

In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

• 미생물학회지
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• 제44권4호
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• pp.333-338
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• 2008

Bacillus anthracis, B. cereus, B. thuringiensis 를 분류하기 위해 rpoB 유전자 배열을 이용한 컴퓨터 분석 작업을 수행하였다. 17개의 B. anthracis, 9개의 B. cereus, 7개의 B. thuringiensis 를 database에서 구하였다. B. anthracis 는 rpoB 유전자의 in silico 제한효소 절단에 의해, B. cereus, B. thuringiensis 2 group과 구별되었다. 그러나 B. cereus와 B. thuringiensis 는 제한효소 절단에 의해 구분되지는 않고, 염기배열과 Blast 탐색의 도움으로 구분이 가능하였다. 본 연구를 통해 3 종류의 Bacillus 종을 동정할 수 있는 알고리즘이 개발되었다.

• Bulletin of the Korean Chemical Society
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• 제30권8호
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• pp.1835-1838
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• 2009

Inhibition of human phenylethanolamine N-methyltransferase (hPNMT) has been proposed as a method for the treatment of several mental processes which related on adrenaline metabolism. We performed in silico screening to identify flavonoid inhibitors of hPNMT using automated docking method and selected 9 inhibitor candidates based on ligand score (LigScore) and binding free energy (${\Delta}G_{bind}$) estimation. Among 9 flavonoid candidates, 7 flavonoids belong to flavones while the rest of them belong to flavanone. All candidates have common chemical features; two hydrogen bond interactions with side chain of Lys75 and backbone carbonyl oxygen of Asn39, and two hydrophobic interactions. One hydrophobic site is formed by Val53, Leu262, and Met258 and the other is made up of Phe182, Ala186, Tyr222, and Val269. This study can be helpful to understand the structural features for inhibition of PNMT and showed flavonoids as promising inhibitor candidates for hPNMT.

• 통합자연과학논문집
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• 제16권4호
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• pp.123-131
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• 2023

Breast cancer continues to pose a substantial worldwide health challenge, thereby requiring the development of innovative strategies to discover new therapeutic interventions. Signal Transducer and Activator of Transcription 3 (STAT-3) has been identified as a significant factor in the development of several types of cancer, including breast cancer. This is primarily attributed to its diverse functions in promoting tumour formation and conferring resistance to therapeutic interventions. This study presents an in silico drug repositioning approach that focuses on identifying specific inhibitors of STAT-3 for the purpose of treating breast cancer. We initially examined the structural and functional attributes of STAT-3, thereby elucidating its crucial involvement in cellular signalling cascades. A comprehensive virtual screening was performed on a diverse collection of drugs that have been approved by the FDA from zinc15 database. Various computational techniques, including molecular docking, cross docking, and cDFT analysis, were utilised in order to prioritise potential candidates. This prioritisation was based on their predicted binding energies and outer molecular orbital reactivity. The findings of our study have unveiled a Dihydroergotamine and Paritaprevir that have been approved by the FDA and exhibit considerable promise as selective inhibitors of STAT-3. In conclusion, the utilisation of our in silico drug repositioning approach presents a prompt and economically efficient method for the identification of potential compounds that warrant subsequent experimental validation as selective STAT-3 inhibitors in the context of breast cancer. The present study highlights the considerable potential of employing computational strategies to expedite the drug discovery process. Moreover, it provides valuable insights into novel avenues for targeted therapeutic interventions in the context of breast cancer treatment.

• Journal of Microbiology and Biotechnology
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• 제29권1호
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• pp.44-54
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• 2019

Geldanamycin and its derivatives, inhibitors of heat shock protein 90, are considered potent anticancer drugs, although their biosynthetic pathways have not yet been fully elucidated. The key step of conversion of 4,5-dihydrogeldanamycin to geldanamycin was expected to catalyze by a P450 monooxygenase, Gel16. The adequate bioconversions by cytochrome P450 mostly rely upon its interaction with redox partners. Several ferredoxin and ferredoxin reductases are available in the genome of certain organisms, but only a few suitable partners can operate in full efficiency. In this study, we have expressed cytochrome P450 gel16 in Escherichia coli and performed an in vitro assay using 4,5-dihydrogeldanamycin as a substrate. We demonstrated that the in silico method can be applicable for the efficient mining of convenient endogenous redox partners (9 ferredoxins and 6 ferredoxin reductases) against CYP Gel16 from Streptomyces hygroscopicus. The distances for ligand FDX4-FDR6 were found to be $9.384{\AA}$. Similarly, the binding energy between Gel16-FDX4 and FDX4-FDR6 were -611.88 kcal/mol and -834.48 kcal/mol, respectively, suggesting the lowest distance and binding energy rather than other redox partners. These findings suggest that the best redox partners of Gel16 could be NADPH ${\rightarrow}$ FDR6 ${\rightarrow}$ FDX4 ${\rightarrow}$ Gel16.

• Genomics & Informatics
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• 제6권4호
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• pp.223-226
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• 2008

We introduce a computational approach for analysis of glycosylation in Post-PKS tailoring steps. It is a computational method to predict the deoxysugar biosynthesis unit pathway and the substrate specificity of glycosyltransferases involved in the glycosylation of polyketides. In this work, a directed and weighted graph is introduced to represent and predict the deoxysugar biosynthesis unit pathway. In addition, a homology based gene clustering method is used to predict the substrate specificity of glycosyltransferases. It is useful for the rational design of polyketide natural products, which leads to in silico drug discovery.