• Genomics & Informatics
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• 제12권4호
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• pp.268-275
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• 2014

The harshness of legionellosis differs from mild Pontiac fever to potentially fatal Legionnaire's disease. The increasing development of drug resistance against legionellosis has led to explore new novel drug targets. It has been found that phosphoglucosamine mutase, phosphomannomutase, and phosphoglyceromutase enzymes can be used as the most probable therapeutic drug targets through extensive data mining. Phosphoglucosamine mutase is involved in amino sugar and nucleotide sugar metabolism. The purpose of this study was to predict the potential target of that specific drug. For this, the 3D structure of phosphoglucosamine mutase of Legionella pneumophila (strain Paris) was determined by means of homology modeling through Phyre2 and refined by ModRefiner. Then, the designed model was evaluated with a structure validation program, for instance, PROCHECK, ERRAT, Verify3D, and QMEAN, for further structural analysis. Secondary structural features were determined through self-optimized prediction method with alignment (SOPMA) and interacting networks by STRING. Consequently, we performed molecular docking studies. The analytical result of PROCHECK showed that 95.0% of the residues are in the most favored region, 4.50% are in the additional allowed region and 0.50% are in the generously allowed region of the Ramachandran plot. Verify3D graph value indicates a score of 0.71 and 89.791, 1.11 for ERRAT and QMEAN respectively. Arg419, Thr414, Ser412, and Thr9 were found to dock the substrate for the most favorable binding of S-mercaptocysteine. However, these findings from this current study will pave the way for further extensive investigation of this enzyme in wet lab experiments and in that way assist drug design against legionellosis.

• 대한의생명과학회지
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• 제14권1호
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• pp.47-53
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• 2008

Hox genes are known to be transcription factors controlling vertebrate pattern formation along the anteroposterior body axis by regulating many target gene expressions during vertebrate embryogenesis. In order to isolate in vivo Hox responsive target genes, ChIP-cloning technique has been applied using Hoxc8 antibody. Here murine embryo of day 11.5 post coitum (E11.5) highly expressing Hoxc8 gene was used after removing head and tail portions where Hoxc8 is rarely expressing. After fixation with formaldehyde, the chromatin DNAs harboring bound proteins were isolated. After sonication, about 0.5- to 1 Kb chromatin DNAs were immunoprecipitated with anti Hoxc8 antibody. After removing the bound proteins with proteinase K, DNAs were isolated, cloned into the pBluescsript II SK vector, and then sequenced. Total 33 random clones sequenced were anlalyzed to be located at 12 different genomic regions. Among these, 8 turned out to be introns and 4 were intergenic regions localized in random chromosomes. The base composition of total cloned genomic sequences (6608 bp) were AT-rich, i.e., 40% GC. When the Hoxc8 core binding sites, such as TAAT, ATTA, TTAT, and ATAA were analyzed total number of 55, 45, 54, and 55 were found, respectively, which are than twice as many as expected number of 26. Although this in silico analysis does not mean that the ChIP-cloned sequence is real Hoxc8 regulatory element in vivo, these results strongly imply that the DNA fragments cloned through chromatin immunoprecipitation could be very much likely the putative Hoxc8 downstream target genes.

• 대한한방내과학회지
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• 제41권6호
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• pp.1078-1088
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• 2020

Objective: The purpose of this study was to investigate the effect of amorfrutin of licorice for Type2 diabetes mellitus. Method: The PubMed, CNKI, Wanfang, OASIS, NDSL, J-STAGE, and CiNii databases were searched from the beginning of the search to September 20, 2020, with no limits on language. Extractions and selections from the literature were made by two authors. The study included in vivo experiments with amorfrutins in high-fat diet-induced obesity C57BL/6 mice and leptin receptor-deficient db/db mice and in silico studies. Results & Conclusion: Four studies were finally selected. We confirmed that amorfrutin treatment considerably improved insulin sensitivity and glucose tolerance and reduced plasma insulin and glucose. Amorfrutins bind to and selectively activate Peroxisome Proliferator-Activated Receptor Gamma (PPARγ), which plays an important role in glucose metabolism. Amorfrutins also strongly bind to the glucagon receptor (GCGR) and work as antagonist. Using the amorfrutins from licorice could therefore be helpful in treating type2 diabetes mellitus.

• Asian Pacific Journal of Cancer Prevention
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• 제14권12호
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• pp.7473-7482
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• 2013

The G-protein coupled receptor 87 (GPR87) is a recently discovered orphan GPCR which means that the search of their endogenous ligands has been a novel challenge. GPR87 has been shown to be overexpressed in squamous cell carcinomas (SCCs) or adenocarcinomas in lungs and bladder. The 3D structure of GPR87 was here modeled using two templates (2VT4 and 2ZIY) by a threading method. Functional assignment of GPR87 by SVM revealed that along with transporter activity, various novel functions were predicted. The 3D structure was further validated by comparison with structural features of the templates through Verify-3D, ProSA and ERRAT for determining correct stereochemical parameters. The resulting model was evaluated by Ramachandran plot and good 3D structure compatibility was evidenced by DOPE score. Molecular dynamics simulation and solvation of protein were studied through explicit spherical boundaries with a harmonic restraint membrane water system. A DRY-motif (Asp-Arg-Tyr sequence) was found at the end of transmembrane helix3, where GPCR binds and thus activation of signals is transduced. In a search for better inhibitors of GPR87, in silico modification of some substrate ligands was carried out to form polar interactions with Arg115 and Lys296. Thus, this study provides early insights into the structure of a major drug target for SCCs.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.275-283
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• 2018

Cancer show distinct pattern of gene expression when it is compared to normal. This difference results malignant characteristic of cancer. Many cancer drugs are targeting this difference so that it can selectively kill cancer cells. One of the recent demand for personalized treating cancer is retrieving normal tissue from a patient so that the gene expression difference between cancer and normal be assessed. However, in most clinical situation it is hard to retrieve normal tissue from a patient. This is because biopsy of normal tissues may cause damage to the organ function or a risk of infection or side effect what a patient to take. Thus, there is a challenge to estimate normal cell's gene expression where cancers are originated from without taking additional biopsy. In this paper, we propose in-silico based prediction of normal cell's gene expression from gene expression data of a tumor sample. We call this challenge as reverting the cancer into normal. We divided this challenge into two parts. The first part is making a generator that is able to fool a pretrained discriminator. Pretrained discriminator is from the training of public data (9,601 cancers, 7,240 normals) which shows 0.997 of accuracy to discriminate if a given gene expression pattern is cancer or normal. Deceiving this pretrained discriminator means our method is capable of generating very normal-like gene expression data. The second part of the challenge is to address whether generated normal is similar to true reverse form of the input cancer data. We used, cycle-consistent adversarial networks to approach our challenges, since this network is capable of translating one domain to the other while maintaining original domain's feature and at the same time adding the new domain's feature. We evaluated that, if we put cancer data into a cycle-consistent adversarial network, it could retain most of the information from the input (cancer) and at the same time change the data into normal. We also evaluated if this generated gene expression of normal tissue would be the biological reverse form of the gene expression of cancer used as an input.

• Korean Chemical Engineering Research
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• 제61권4호
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• pp.523-541
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• 2023

3D 프린터의 활용이 높아짐에 따라 발생하는 화학물질에 대한 노출 빈도가 증가하고 있다. 그러나 3D 프린팅 발생 화학물질의 독성 및 유해성에 대한 연구는 미비하며, 분자 구조 데이터의 결측치로 인해 in silico 기법을 사용한 독성예측 연구는 저조한 실정이다. 본 연구에서는 화학물질의 분자구조 정보를 나타내는 주요 분자표현자의 결측치를 보간하여 3D 프린팅의 독성 및 유해성을 예측한 Data-centric QSAR 모델을 개발하였다. 먼저 MissForest 알고리즘을 사용해 3D 프린팅으로 발생되는 유해물질의 분자표현자 결측치를 보완하였으며, 서로 다른 4가지 기계학습 모델(결정트리, 랜덤포레스트, XGBoost, SVM)을 기반으로 Data-centric QSAR 모델을 개발하여 생물 농축 계수(Log BCF)와 옥탄올-공기분배계수(Log Koa), 분배계수(Log P)를 예측하였다. 또한, 설명 가능한 인공지능(XAI) 방법론 중 TreeSHAP (SHapley Additive exPlanations) 기법을 활용하여 Data-centric QSAR 모델의 신뢰성을 입증하였다. MissForest 알고리즘 기반 결측지 보간 기법은, 기존 분자구조 데이터에 비하여 약 2.5배 많은 분자구조 데이터를 확보할 수 있었다. 이를 바탕으로 개발된 Data-centric QSAR 모델의 성능은 Log BCF, Log Koa와 Log P를 각각 73%, 76%, 92% 의 예측 성능으로 예측할 수 있었다. 마지막으로 Tree-SHAP 분석결과 개발된 Data-centric QSAR 모델은 각 독성치와 물리적으로 상관성이 높은 분자표현자를 통하여 선택함을 설명할 수 있었고 독성 정보에 대한 높은 예측 성능을 확보할 수 있었다. 본 연구에서 개발한 방법론은 다른 프린팅 소재나 화학공정, 그리고 반도체/디스플레이 공정에서 발생 가능한 오염물질의 독성 및 인체 위해성 평가에 활용될 수 있을 것으로 사료된다.

• Nutrition Research and Practice
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• 제17권4호
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• pp.682-697
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• 2023

BACKGROUND/OBJECTIVES: Tibetan tea is a kind of dark tea, due to the inherent complexity of natural products, the chemical composition and beneficial effects of Tibetan tea are not fully understood. The objective of this study was to unravel the composition of Tibetan tea using knowledge-guided multilayer network (KGMN) techniques and explore its potential antioxidant and hypolipidemic mechanisms in mice. MATERIALS/METHODS: The C57BL/6J mice were continuously gavaged with Tibetan tea extract (T group), green tea extract (G group) and ddH2O (H group) for 15 days. The activity of total antioxidant capacity (T-AOC) and superoxide dismutase (SOD) in mice was detected. Transcriptome sequencing technology was used to investigate the molecular mechanisms underlying the antioxidant and lipid-lowering effects of Tibetan tea in mice. Furthermore, the expression levels of liver antioxidant and lipid metabolism related genes in various groups were detected by the real-time quantitative polymerase chain reaction (qPCR) method. RESULTS: The results showed that a total of 42 flavonoids are provisionally annotated in Tibetan tea using KGMN strategies. Tibetan tea significantly reduced body weight gain and increased T-AOC and SOD activities in mice compared with the H group. Based on the results of transcriptome and qPCR, it was confirmed that Tibetan tea could play a key role in antioxidant and lipid lowering by regulating oxidative stress and lipid metabolism related pathways such as insulin resistance, P53 signaling pathway, insulin signaling pathway, fatty acid elongation and fatty acid metabolism. CONCLUSIONS: This study was the first to use computational tools to deeply explore the composition of Tibetan tea and revealed its potential antioxidant and hypolipidemic mechanisms, and it provides new insights into the composition and bioactivity of Tibetan tea.