• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.299-314
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• 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 Dairy Science and Biotechnology
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• v.25 no.2
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• pp.29-32
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• 2007

An understanding functional properties and molecular interactions of milk proteins was critical to improve qualities of fermented dairy products including yogurts and cheeses. Extensive rearrangements of casein particles were important factors to enhance whey separation in yogurt gel network. The use of high hydrostatic pressure treated whey protein as an ingredient of low fat processed cheese food resulted in the production of low fat processed cheese food with acceptable firmness and enhanced meltabilities. Milk protein-based nano particles produced by self-association of proteins could be better nutrient delivery vehicle than micro particle since particle size reduction in nano particles could lead to increased residence time and surface area available in GI tract.

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

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.668-669
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• 2008

In this paper, we introduce various coarse-grained elastic network modeling (ENM) techniques as a novel computational method for simulating atomic scale dynamics in macromolecules including DNA, RNA, protein, and polymer. In ENM, a system is modeled as a spring network among representative atoms in which each linear elastic spring is well designed to replace both bonded and nonbonded interactions among atoms in the sense of quantum mechanics. Based on this simplified system, a harmonic Hookean potential is defined and used for not only calculating intrinsic vibration modes of a given system, but also predicting its anharmonic conformational change, both of which are strongly related with its functional features. Various nano and bio applications of ENM such as fracture mechanics of nanocomposite and protein dynamics show that ENM is one of promising tools for simulating atomic scale dynamics in a more effective and efficient way comparing to the traditional molecular dynamics simulation.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.573-584
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• 2003

Recent massive data generation by genomics and proteomics requires bioinformatic tools to extract the biological meaning from the massive results. Here we introduce ROSPath, a database system to deal with information on reactive oxygen species (ROS)-mediated cell signaling pathways. It provides a structured repository for handling pathway related data and tools for querying, displaying, and analyzing pathways. ROSPath data model provides the extensibility for representing incomplete knowledge and the accessibility for linking the existing biochemical databases via the Internet. For flexibility and efficient retrieval, hierarchically structured data model is defined by using the object-oriented model. There are two major data types in ROSPath data model: ‘bio entity’ and ‘interaction’. Bio entity represents a single biochemical entity: a protein or protein state involved in ROS cell-signaling pathways. Interaction, characterized by a list of inputs and outputs, describes various types of relationship among bio entities. Typical interactions are protein state transitions, chemical reactions, and protein-protein interactions. A complex network can be constructed from ROSPath data model and thus provides a foundation for describing and analyzing various biochemical processes.

• Food Science of Animal Resources
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• v.43 no.3
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• pp.540-551
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• 2023

Milk protein concentrate (MPC) is widely used to enhance the stability and texture of fermented dairy products. However, most research has focused on yogurt products, and the effects of MPC on sour cream characteristics remain unknown. Therefore, we investigated the effects of different MPC levels (0%, 1%, 2%, and 3% w/w) on the rheological, physicochemical, microbiological, and aroma characteristics of sour creams in this study. We found that MPC supplementation stimulated the growth of lactic acid bacteria (LAB) in sour creams, resulting in higher acidity than that in the control sample due to the lactic acid produced by LAB. Three aroma compounds, acetaldehyde, diacetyl, and acetoin, were detected in all sour cream samples. All sour creams showed shear-thinning behavior (n=0.41-0.50), and the addition of MPC led to an increase in the rheological parameters (η_a,50, K, G', and G"). In particular, sour cream with 3% MPC showed the best elastic property owing to the interaction between denatured whey protein and caseins. In addition, these protein interactions resulted in the formation of a gel network, which enhanced the water-holding capacity and improved the whey separation. These findings revealed that MPC can be used as a supplementary protein to improve the rheological and physicochemical characteristics of sour cream.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1003-1008
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• 2016

Breast cancer is now the leading cause of cancer death in women worldwide. Cancer progression is driven not only by cancer cell intrinsic alterations and interactions with tumor microenvironment, but also by systemic effects. Integration of multiple profiling data may provide insights into the underlying molecular mechanisms of complex systemic processes. We performed a bioinformatic analysis of two public available microarray datasets for breast tumor stroma and peripheral blood mononuclear cells, featuring integrated transcriptomics data, protein-protein interactions (PPIs) and protein subcellular localization, to identify genes and biological pathways that contribute to dialogue between tumor stroma and the peripheral circulation. Genes of the integrin family as well as CXCR4 proved to be hub nodes of the crosstalk network and may play an important role in response to stroma-derived chemoattractants. This study pointed to potential for development of therapeutic strategies that target systemic signals travelling through the circulation and interdict tumor cell recruitment.

• Bulletin of the Korean Chemical Society
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• v.29 no.5
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• pp.1013-1017
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• 2008

Syndecan-3 is a cell-surface heparan sulfate proteoglycan, which performs a variety of functions during cell adhension process. It is also a coreceptor for growth factor, mediating cell-cell and cell-matrix interaction. Syndecan-3 contains a cytoplasmic domain potentially associated with the cytoskeleton. Syndecan-3 is specifically expressed in neuron cell and has related to neuron cell differentiation and development of actin filament in cell migration. Syndecans each have a unique, central, and variable (V) region in their cytoplasmic domains. And that region of syndecan-3 may modulate the interactions of the conserved C1 regions of the cytoplasmic domains by tyrosine phosphorylation. Cytoplasmic domain of syndecan-3 has been synthesized for NMR structural studies. The solution structure of syndecan-3 cytoplasmic domain has been determined by two-dimensional NMR spectroscopy and simulated-annealing calculation. The cytoplasmic domain of the syndecan proteins has a tendency to form a dimmer conformation with a central cavity, however, that of syndecan-3 demonstrated a monomer conformation with a flexible region near C-terminus. The structural information might add knowledge about the structure-function relationships among syndecan proteins.

• Molecules and Cells
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• v.25 no.3
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• pp.352-357
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• 2008

Osteoprotegerin (OPG) is a soluble decoy receptor that inhibits osteoclastogenesis and is closely associated with bone resorption processes. We have designed and determined the solution structures of potent OPG analogue peptides, derived from sequences of the cysteine-rich domain of OPG. The inhibitory effects of the peptides on osteoclastogenesis are dose-dependent ($10^{-6}M-10^{-4}M$), and the activity of the linear peptide at $10^{-4}M$ is ten-fold higher than that of the cyclic OPG peptide. Both linear and cyclic peptides have a ${\beta}$-turn-like conformation and the cyclic peptide has a rigid conformation, suggesting that structural flexibility is an important factor for receptor binding. Based on structural and biochemical information about RANKL and the OPG peptides, we suggest that complex formation between the peptide and RANKL is mediated by both hydrophobic and hydrogen bonding interactions. These results provide structural insights that should aid in the design of peptidyl-mimetic inhibitors for treating metabolic bone diseases caused by abnormal osteoclast recruitment.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.60-67
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• 2009

Analyzing protein-protein interactions(PPI) is an important task in bioinformatics as it can help in new drugs' discovery process. However, due to vast amount of PPI data and their complexity, efficient visualization of the data is still remained as a challenging problem. We have developed efficient and effective visualization system that integrates Gene Ontology(GO) and PPI network to provide better insights to scientists. To provide efficient data visualization, we have employed dynamic interactive graph drawing methods and context-based browsing strategy. In addition, quick and flexible cross-reference system between GO and PPI; LCA(Least Common Ancestor) finding for GO; and etc are supported as special features. In terms of interface, our visualization system provides two separate graphical windows side-by-side for GO graphs and PPI network, and also provides cross-reference functions between them.