• 대한한의학방제학회지
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• 제30권3호
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• pp.175-182
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• 2022

Objective : This study aims to investigate the active ingredients and potential mechanisms of the beneficial herb on human cancers such as the liver by employing network pharmacology. Methods : Ingredients and their target information was obtained from various databases such as TM-MC, TTD, and Drugbank. Related protein for liver cancer was retrieved from the Comparative Toxicogenomics Database and literature. A hypergeometric test and gene set enrichment analysis were conducted to evaluate associations between protein targets of red ginseng (Panax ginseng C. A. Meyer) and liver cancer-related proteins and identify related signaling pathways, respectively. Network proximity was employed to identify active ingredients of red ginseng on liver cancer. Results : A compound-target network of red ginseng was constructed, which consisted of 363 edges between 53 ingredients and 121 protein targets. MAPK signaling pathway, PI3K-Akt signaling pathway, p53 signaling pathway, TGF-beta signaling pathway, and cell cycle pathway was significantly associated with protein targets of red ginseng. Network proximity results indicated that Ginsenoside Rg1, Acetic Acid, Ginsenoside Rh2, 20(R)-Ginsenoside Rg3, Notoginsenoside R1, Ginsenoside Rk1, 2-Methylfuran, Hexanal, Ginsenoside Rd, Ginsenoside Rh1 could be active ingredients of red ginseng against liver cancer. Conclusion : This study suggests that network-based approaches could be useful to explore potential mechanisms and active ingredients of red ginseng for liver cancer.

• IMMUNE NETWORK
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• 제2권3호
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• pp.125-132
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• 2002

In the past, bioinformatics was often regarded as a difficult and rather remote field, practiced only by computer scientists and not a practical tool available to biologists. However, the various on-going genome projects have had a serious impact on biological sciences in various ways and now there is little doubt that bioinformatics is an essential part of the research environment, with a wealth of biological information to analyze and predict. Fully sequenced genomes made us to have additional insights into the functional properties of the encoded proteins and made it possible to develop new tools and schemes for functional biology on a proteomic scale. Among those are the yeast two-hybrid system, mass spectrometry and microarray: the technology of choice to detect protein-protein interactions. These functional insights emerge as networks of interacting proteins, also known as "pathway informatics" or "interactomics". Without exception it is no longer possible to make advances in the signaling/regulatory pathway studies without integrating information technologies with experimental technologies. In this paper, we will introduce the databases of protein interaction worldwide and discuss several challenging issues regarding the actual implementation of databases.

• BMB Reports
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• 제43권10호
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• pp.670-676
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• 2010

In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.

• 인지과학
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• 제28권4호
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• pp.299-314
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• 2017

In-silico 기반의 약물-표적 단백질 연관관계 예측은 신약 탐색 단계에서 매우 중요하다. 그러나 기존의 예측모델은 입력 값이 고정적이며 표적 단백질의 특질 값이 가공된 데이터로 한정됨으로써 예측 모델의 확장성과 유연성이 부족하다. 본 논문에서는 약물-표적 단백질 연관관계를 예측하는 확장 가능한 형태의 머신러닝 모델을 소개한다. 확장 가능한 머신러닝 모델의 핵심 아이디어는 쌍기반의 뉴럴 네트워크로써, 약물과 단백질의 미가공 데이터를 사용하여 특질을 추출하고 특질 값을 각각의 뉴럴 네트워크 레이어에 입력한다. 이 방법은 추가적인 지식없이 자동적으로 약물과 단백질의 특질을 추출한다. 또한 쌍기반 레이어는 특질 값을 풍부한 저차원의 벡터로 향상 시킴으로써 입력 값의 차이로 인한 편향 학습을 방지한다. PubChem BioAssay(PCBA) 데이터 셋에 기반한 5-폴드 교차 검증법을 통하여 제안한 모델의 성능을 평가했으며, 이전의 모델보다 우월한 성능을 보였다.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.41-47
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• 2021

Detection of protein-protein interactions (PPIs) remains essential for the development of therapies against diseases. Experimental studies to detect PPI are longer and more expensive. Today, with the availability of PPI data, several computer models for predicting PPIs have been proposed. One of the big challenges in this task is feature extraction. The relevance of the information extracted by some extraction techniques remains limited. In this work, we first propose an extraction method based on correlation relationships between the physicochemical properties of amino acids. The proposed method uses a correlation matrix obtained from the hydrophobicity and hydrophilicity properties that it then integrates in the calculation of the bigram. Then, we use the SVM algorithm to detect the presence of an interaction between 2 given proteins. Experimental results show that the proposed method obtains better performances compared to the approaches in the literature. It obtains performances of 94.75% in accuracy, 95.12% in precision and 96% in sensitivity on human HPRD protein data.

• Genomics & Informatics
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• 제14권1호
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• pp.2-11
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• 2016

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.

• Bulletin of the Korean Chemical Society
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• 제29권2호
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• pp.347-351
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• 2008

Acetyl-CoA carboxylase (ACC) is an excellent candidate for antibiotics drug target, which mediates malonyl-CoA synthesis from acetyl-CoA through acetylation process. It is also involved in the committed step of fatty acid synthesis which is essential for living organisms. We have determined the three dimensional structure of C terminal domain of HP0371, biotin-carboxyl carrier protein of H. pyroli, in solution state using heteronuclear multi-dimensional NMR spectroscopy. The structure of HP0371 shows a flatten b-sheet fold which is similar with that of E. coli. However, the sequence and structure of protruding thumb are different with that of E. coli and the thumb shows different basis of structural rigidity based on backbone dynamics data.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2008년도 춘계 종합학술대회 논문집
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• pp.842-845
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• 2008

단백질 상호작용 네트워크는 허브(Hub)라 할 수 있는 상호작용 수가 많은 소수의 단백질과 상호작용 수가 적은 다수의 단백질들로 구성된다. 최근 들어 여러 연구들에서 허브 단백질이 비 허브(Non-hub) 단백질보다 상호작용 네트워크에 필수적인 단백질일 가능성이 높다고 설명하고 있다. 이러한 현상을 중심-치명 룰(Centrality-lethality Rule)이라 하는데, 이는 복잡계 네트워크에서 허브단백질의 중요성 및 네트워크 구조의 중요성을 설명하기 위한 방법으로 폭넓게 신뢰받고 있다. 이에 본 논문에서는 중심-치명 룰이 항상 옳게 적용되는지를 확인하기 위해 Uetz, Ito, MIPS, DIP, SGD, BioGRID와 같은 효모에 관한 공개된 모든 단백질 상호작용 데이터베이스들을 분석하였다. 흥미롭게도, 상호작용 데이터가 적은 데이터베이스들(Uetz, Ito, DIP)에서는 중심-치명 룰을 잘 나타냈지만 상호작용 데이터가 대용량인 데이터베이스들(SGD, BioGRID)에서는 중심-치명 룰이 잘 맞지 않음을 확인하였다. 이에 따라 SGD와 BioGRID 데이터베이스로 부터 얻은 상호작용 네트워크의 특징을 분석하고 DIP 데이터베이스의 상호작용 네트워크와 비교해보았다.

• BMB Reports
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• 제38권5호
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• pp.550-554
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• 2005

YKR049C is a mitochondrial protein in Saccharomyces cerevisiae that is conserved among yeast species, including Candida albicans. However, no biological function for YKR049C has been ascribed based on its primary sequence information. In the present study, NMR spectroscopy was used to determine the putative biological function of YKR049C based on its solution structure. YKR049C shows a well-defined thioredoxin fold with a unique insertion of helices between two $\beta$-strands. The central $\beta$-sheet divides the protein into two parts; a unique face and a conserved face. The 'unique face' is located between ${\beta}2$ and ${\beta}3$. Interestingly, the sequences most conserved among YKR049C families are found on this 'unique face', which incorporates L109 to E114. The side chains of these conserved residues interact with residues on the helical region with a stretch of hydrophobic surface. A putative active site composed by two short helices and a single Cys97 was also well observed. Our findings suggest that YKR049C is a redox protein with a thioredoxin fold containing a single active cysteine.

• 한국식품조리과학회지
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• 제6권3호통권12호
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• pp.33-41
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• 1990

This study was conducted to compare the characteristics of the ordinary soybean curd and 3 protein-adding soybean curds (soy protein, casein, gelatin). The sensory evaluation, textural analysis by Instron Universal Testing Machine & the microstructure analysis by SEM for 4 soybean curds were carried out. The results were as follows: 1. In sensory evaluation. 1) The differentiation of soybean curds was greatly explained by `hardness in mouth' through ANOVA test. 2) Discriminant analysis showed that the properties of casein soybean curd were different from those of other three soybean curds by discriminant function I, and the properties of soy protein soybean curd were slightly different from those of ordinary and gelatin soybean curds by discriminant function II. 2. In textural analysis by Instron, protein-adding soybean curds showed significantly lower hardness than ordinary soybean curd. 3. In microstructure analysis by SEM, soy protein soybean curd showed regular, good honeycomb-like network structure and other soybean curds showed lumpy network. The structure of gelatin soybean curd was slightly similar to that of ordinary soybean curd.