• Proceedings of the Korean Society of Plant Pathology Conference
• /
• 2003.10a
• /
• pp.29-29
• /
• 2003

Incompatible plant-pathogen interactions result in the rapid cell death response known as hypersensitive response (HR) and activation of host defense-related genes. To understand the molecular and cellular mechanism controlling defense response better, several approaches including isolation and characterization of novel genes, promoter analysis of those genes, protein-protein interaction analysis and reverse genetic approach etc. By using the yeast two-hybrid system a clone named Tsipl, Tsil -interacting protein 1, was isolated whose translation product apparently interacted with Tsil, an EREBP/AP2 type DNA binding protein. RNA gel blot analysis showed that the expression of Tsipl was increased by treatment with NaCl, ethylene, salicylic acid, or gibberellic acid. Transient expression analysis using a Tsipl::smGFP fusion gene in Arabidopsis protoplasts indicated that the Tsipl protein was targeted to the outer surface of chloroplasts. The targeted Tsipl::smGFP proteins were diffused to the cytoplasm of protoplasts in the presence of salicylic acid (SA) The PEG-mediated co-transfection analysis showed that Tsipl could interact with Tsil in the nucleus. These results suggest that Tsipl-Tsil interaction might serve to regulate defense-related gene expression. Basically the useful promoters are valuable tools for effective control of gene expression related to various developmental and environmental condition.(중략)

• BMB Reports
• /
• v.51 no.10
• /
• pp.526-531
• /
• 2018

Protein-Protein Interactions (PPIs) play essential roles in diverse biological processes and their misregulations are associated with a wide range of diseases. Especially, the growing attention to PPIs as a new class of therapeutic target is increasing the need for an efficient method of cell-based PPI analysis. Thus, we newly developed a robust PPI assay (SeePPI) based on the co-translocation of interacting proteins to the discrete subcellular compartment 'processing body' (p-body) inside living cells, enabling a facile analysis of PPI by the enriched fluorescent signal. The feasibility and strength of SeePPI (${\underline{S}}ignal$ ${\underline{e}}nhancement$ ${\underline{e}}xclusively$ on ${\underline{P}}-body$ for ${\underline{P}}rotein-protein$ ${\underline{I}}nteraction$) assay was firmly demonstrated with FKBP12/FRB interaction induced by rapamycin within seconds in real-time analysis of living cells, indicating its recapitulation of physiological PPI dynamics. In addition, we applied p53/MDM2 interaction and its dissociation by Nutlin-3 to SeePPI assay and further confirmed that SeePPI was quantitative and well reflected the endogenous PPI. Our SeePPI assay will provide another useful tool to achieve an efficient analysis of PPIs and their modulators in cells.

• Journal of Animal Science and Technology
• /
• v.60 no.6
• /
• pp.11.1-11.7
• /
• 2018

After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.

• The Journal of the Korea Contents Association
• /
• v.9 no.4
• /
• pp.114-120
• /
• 2009

In this study, we employed the idea that disease-related proteins tend to be work as an important factor for architecture of the disease network. We initially obtained 43 atopy-related proteins from the Online Mendelian Inheritance in Man (OMIM) and then constructed atopy-related protein interaction network. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived three diseasomal proteins, CCR5, CCL11, and IL/4R. Although we use the relatively small subnetwork, an atopy-related disease network, it is sufficient that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.

• 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.

• Journal of Microbiology
• /
• v.39 no.1
• /
• pp.24-30
• /
• 2001

In addition to effecting the catalysis of sugar uptake, the bacterial phosphoenolpyruvate::sugar phosphotransferase system regulates a variety of physiological processes. In a previous paper [Seok et al.,(1997) J. Biol. Chem. 272, 26511-26521], we reported the interaction with and allosteric regulation of Esiherichia coli glycogen phosphorylase activity by the histidine-containing phosphocarrier protein HPr in vitro. Here, we show that the specific interaction between HPr and glycogen phosphorylase occurs in vivo. To address the physiological role of the HPr-glycogen phosphorylase complex, intracellular glycogen levels were measured in E. coli strains transformed with various plasmids. While glycogen accumulated during the transition between exponential and stationary growth phases in wildtype cells, it did not accumulate in cells overproducing HPr or its inactive mutant regardless of the growth stage. From these results, we conclude that HPr mediates crosstalk between sugar uptake through the phosphoenolpyruvate:sugar phosphotransferase system and glycogen breakdown. The evolutionary significance of the HPr-glycogen phosphorylase complex is suggested.

• Biomedical Science Letters
• /
• v.26 no.1
• /
• pp.47-50
• /
• 2020

Recently, decades of robust researches on degenerative brain disorder have been highlighted on the interactive connection of gut and brain. In terms of inflammatory cytokine production, others have shown that Nucleotide-Binding Oligomerization Domain Containing 2 (NOD2) is involved with Leucine-Rich Repeat Kinase 2 (LRRK2). HEK293T cells were transiently co-transfected with Myc-tagged LRRK2 and Flag-tagged NOD2 and then followed by co-immunoprecipitation assay. In this study, we provide the novel finding of physical protein-protein interaction between NOD2 and LRRK2. G2019S variant has shown stronger interactions against NOD2 than those of wild type LRRK2. In an axis of NOD2-LRRK2 communication, it is believed to pave a new way in the understanding of the bidirectional molecular mechanism of brain disorder, including Parkinson's disease into gut inflammatory disease, including Crohn's disease.

• Journal of Microbiology
• /
• v.38 no.2
• /
• pp.113-116
• /
• 2000

Escherichia coli F plasmid partition apparatus is composed of two trans-acting proteins (SopA and SopB) and one cis-acting DNA sequence (sopC). The SopB-sopC complex has been suggested to serve a centromere-like function through its interaction with chromosomally encoded proteins which remain to be identified. In this paper, we are introducing a new yeast 1.5-hybrid system which assembles the two-hybrid and one-hybrid system as a mean to find and additional component of the F plasmid partition system, interacting with DNA (sopC)-bound SopB protein. The results indicates that this system is a promising one, capable of selecting an interacting component.

• The Journal of the Korea Contents Association
• /
• v.8 no.1
• /
• pp.259-271
• /
• 2008

We propose a protein function finding algorithm that is able to predict specific molecular function for unannotated proteins through domain analysis from protein-protein network. To do this, we first construct protein-protein interaction(PPI) network in Saccharomyces cerevisiae from MIPS databases. The PPI network(proteins; 3,637, interactions; 10,391) shows the characteristics of a scale-free network and a hierarchical network that proteins with a number of interactions occur in small and the inherent modularity of protein clusters. Protein-protein interaction databases obtained from a Y2H(Yeast Two Hybrid) screen or a composite data set include random false positives. To filter the database, we reconstruct the PPI networks based on the cellular localization. And then we analyze Hub proteins and the network structure in the reconstructed network and define structural modules from the network. We analyze protein domains from the structural modules and derive functional modules from them. From the derived functional modules with high certainty, we find tentative functions for unannotated proteins.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2003.10a
• /
• pp.17-25
• /
• 2003

Determining the binding sites in protein-nucleic acid complexes is essential to the complete understanding of protein-nucleic acid interactions and to the development of new drugs. We have developed a set of algorithms for analyzing protein-nucleic acid interactions and for predicting potential binding sites in protein-nucleic acid complexes. The algorithms were used to analyze the hydrogen-bonding interactions in protein-RNA and protein-DNA complexes. The analysis was done both at the atomic and residue level, and discovered several interesting interaction patterns and differences between the two types of nucleic acids. The interaction patterns were used for predicting potential binding sites in new protein-RNA complexes.