• Herbal Formula Science
• /
• v.30 no.3
• /
• pp.175-182
• /
• 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
• /
• v.2 no.3
• /
• pp.125-132
• /
• 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
• /
• v.43 no.10
• /
• pp.670-676
• /
• 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.

• Korean Journal of Cognitive Science
• /
• v.28 no.4
• /
• pp.299-314
• /
• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.41-47
• /
• 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
• /
• v.14 no.1
• /
• pp.2-11
• /
• 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
• /
• v.29 no.2
• /
• pp.347-351
• /
• 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.

• Proceedings of the Korea Contents Association Conference
• /
• 2008.05a
• /
• pp.842-845
• /
• 2008

The protein interaction network contains a small number of highly connected protein, denoted hub and many destitutely connected proteins. Recently, several studies described that a hub protein is more likely to be essential than a non-hub protein. This phenomenon called as the centrality-lethality rule. This rule is widely credited to exhibit the importance of hub proteins in the complex network and the significance of network architecture as well. To confirm whether the rule is accurate, we investigated all protein interaction DBs of yeast in the public sites such as Uetz, Ito, MIPS, DIP, SGD, and BioGRID. Interestingly, the protein network shows that the rule is correct in lower scale DBs (e.g., Uetz, Ito, and DIP) but is not correct in higher scale DBs (e.g., SGD and BioGRID). We are now analyzing the features of networks obtained from the SGD and BioGRD and comparing those of network from the DIP.

• BMB Reports
• /
• v.38 no.5
• /
• pp.550-554
• /
• 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.

• Korean journal of food and cookery science
• /
• v.6 no.3 s.12
• /
• pp.33-41
• /
• 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.