• YAKHAK HOEJI
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• v.35 no.6
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• pp.461-465
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• 1991

Silica-based gel filtration chromatography has been used to characterize molecular weight of proteins. However, the molecular weight measured by this method was distorted by protein-silica interactions like hydrophobic and electrostatic forces. Therefore, we characterized protein-silica interaction using two forms of phytochrome (124 kDa) having different hydrophobicity and surface charge. PH and ionic strength affected the retention time of phytochrome suggesting that electrostatic force is the major interaction between protein and silica surface.

• Journal of Microbiology and Biotechnology
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• v.12 no.2
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• pp.286-291
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• 2002

Histone deacetylase I (HDAC1) works as one of the components in a nucleosome remodeling (NuRD) complex that consists of several proteins, including metastasis-associated protein 1 (MTA1). Since the protein-protein interaction of HDAC1 and MTA1 would appear to be important for both the integrity and functionality of the HDAC1 complex, the interruption of the HDAC1 and MTA1 interaction may be an efficient way to regulate the biological function of the HDAC1 complex. Based on this idea, a yeast two-hybrid system was constructed with HDAC1 and MTA1 expressing vectors in the DNA binding and activation domains, respectively. To verify the efficiency of the assay system, 3,500 microbial metabolite libraries were tested using the paper disc method, and KB0699 was found to inhibit the HDAC1 and MTA1 interaction without any toxicity to the wild-type yeast. Furthermore, KB0699 blocked the interaction of HDAC1 and MTA1 in an in vitro GST pull down assay and induced morphological changes in B16/BL6 melanoma cells, indicating the interruption of the HDAC1 complex function. Accordingly, these results demonstrated that the yeast assay strain developed in this study could be a valuable tool for the isolation of a HDAC1 complex disruptor.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.5
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• pp.267-281
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• 2006

PPI(Protein-Protein Interaction) data has information about the organism has maintained a life with some kind of mechanism. So, it is used in study about cure research back, cause of disease, and new medicine development. This PPI data has been increased by geometric progression because high throughput methods are developed such as Yeast-two-hybrid, Mass spectrometry, and Correlated mRNA expression. So, it is impossible that a person directly manage and analyze PPI data. Fortunately, PPI data is able to abstract the graph which has proteins as nodes, interactions as edges. Consequently, Graph theory plentifully researched from the computer science until now is able to be applied to PPI data successfully. In this paper, we introduce Proteinca(PROTEin INteraction CAbaret) workbench system for easily managing, analyzing and visualizing PPI data. Proteinca assists the user understand PPI data intuitively as visualizing a PPI data in graph and provide various analytical function on graph theory. And Protenica provides a simplified visualization with gravity-rule.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.7-16
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• 2003

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-bability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated fur the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• Journal of Applied Biological Chemistry
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• v.42 no.3
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• pp.125-129
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• 1999

The changes in gel characteristics of soy protein and succinylated soy protein due to various specific interaction-altering reagents which affect the formation and textural properties of gels, were studied. The reagents were added to 15% soy protein solutions prior to heat treatment. Succinylated soy protein formed harder gel without the addition of reagents. Hardly no gels were formed with urea, indicating that hydrogen bonds significantly contributed to the formation and hardness of the gel and the effects of urea on the hardness of succinylated soy protein gel were more significant. Disulfide bonds were important in the formation of hard gels whether they were succinylated or not, but the contributions of hydrophobic interactions to gel hardness were relatively insignificant. The hardness reducing effects of NaCl and NaSCN were more significant in succinylated soy protein gel. As such, electrostatic interactions were important for succinylated soy protein to form hard gel but not for unmodified soy protein.

• Archives of Pharmacal Research
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• v.4 no.2
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• pp.99-107
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• 1981

To investigate the protein binding characteristics of ibuprofenlysine, the effects of drub conentration, pH, ionic strength and protein concentration on the binding of drug to protein concentration on the binding of drug to protein were studied by fluorescence probe method. The conformational change of protein was investigated by circular dichroism (CD) measurement. As the concentration of drug increases, the association constant decreases. These may be due to complex formation of the probe and drug, or the interaction of the protein-probe complex and drug. The association constant for ibuprofenlysine increased with increasing protein concentration. These finding suggest a sharing of one ibuprofenlysine molecule by more than one protein molecule in the binding. The binding between ibuprofenlysine and protein was dependent on pH and ionic strength. It seems that both hydrophobic binding and some electrostatic forces are involved in the binding of ibuprofenlysing to protein.

• Journal of Life Science
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• v.18 no.3
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• pp.318-322
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• 2008

This paper deals with an efficient management and automatic update sub-system for WASPIFA (Web-based Assistant System for Protein-protein Interaction and Function Analysis) system that had been developed in the past and now provides the comprehensive information on protein-protein interaction and protein function. Protein interacting data has increased exponentially, so that it costs enormous time and effort. In other words, it is actually impossible to manually update and manage an analysis system based on protein interacting data. Even though there exists a good analysis system, it could be useless if it was able to be updated timely and managed properly. Unfortunately, in most cases, biologists without professional knowledge on their analysis systems have to cope with a great difficulty in running them. In this respect, the efficient management and automatic update subsystem of protein interacting and its related data has been developed to facilitate experimental biologists as well as bioinformaticians to update and manage the WASPIFA system.

• Journal of the Korean Magnetic Resonance Society
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• v.15 no.1
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• pp.80-89
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• 2011

3D structures of STAM1 UIM-ubiquitin complex were presented to predict and analyze the interaction between UIM and ubiquitin. To generate the protein-peptide complex structure, the RosettaDock method was used with and without NMR restraints. High resolution complex structure was acquired successfully and evaluated electrostatic interaction in the protein-peptide binding with several charged residues at the binding site. From docking results, the Rosettadock method could be useful to acquire essential information of protein-protein or protein-peptide interaction with minimal biological evidences.

• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.503-513
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• 2005

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity($77\%$) and specificity($95\%$) are achieved for test protein pairs containing common domains with teaming sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.