• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.431-436
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• 2005

Living cells are sustained not by individual activities but rather by coordinated summative efforts of different biological functional modules. While recent research works have focused largely on finding individual functional modules, this paper attempts to explore the connections or relationships between different cellular functions through cross-function domain interaction maps. Exploring such a domain interaction map can help understand the underlying inter-function communication mechanisms. To construct a cross-function domain interaction map from existing genome-wide protein-protein interaction datasets, we propose a two-step procedure. First, we infer conserved domain-domain interactions from genome-wide protein-protein interactions of yeast, worm and fly. We then build a cross-function domain interaction map that shows the connections of different functions through various conserved domain interactions. The domain interaction maps reveal that conserved domain-domain interactions can be found in most detected cross-functional relationships and a f9w domains play pivotal roles in these relationships. Another important discovery in the paper is that conserved domains correspond to highly connected protein hubs that connect different functional modules together.

• BMB Reports
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• v.31 no.5
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• pp.459-467
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• 1998

A new set of functional group interaction energy descriptors relevant to the ACE (Angiotensin-Converting Enzyme) inhibitory peptide, QSAR (Quantitative Structure Activity Relationships), is presented. The functional group interaction energies approximate the charged interactions and distances between functional groups in molecules. The effective energies of the computationally derived geometries are useful parameters for deriving 3D-QSAR models, especially in the absence of experimentally known active site conformation. ACE is a regulatory zinc protease in the renin-angiotensin system. Therapeutic inhibition of this enzyme has proven to be a very effective treatment for the management of hypertension. The non bond interaction energy values among functional groups of six-feature of ACE inhibitory peptides were used as descriptor terms and analyzed for multivariate correlation with ACE inhibition activity. The functional group interaction energy descriptors used in the regression analysis were obtained by a series of inhibitor structures derived from molecular mechanics and semi-empirical calculations. The descriptors calculated using electrostatic and steric fields from the precisely defined functional group were sufficient to explain the biological activity of inhibitor. Application of the descriptors to the inhibition of ACE indicates that the derived QSAR has good predicting ability and provides insight into the mechanism of enzyme inhibition. The method, functional group interaction energy analysis, is expected to be applicable to predict enzyme inhibitory activity of the rationally designed inhibitors.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.5-23
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• 2002

Motivation: Protein-protein interaction plays a critical role in the biological processes. The identification of interacting proteins by bioinformatical methods can provide new lead In the functional studies of uncharacterized proteins without performing extensive experiments. Results: Protein-protein interactions are predicted by a computational algorithm based on the weighted scoring system for domain interactions between interacting protein pairs. Here we propose potential interaction domain (PID) pairs can be extracted from a data set of experimentally identified interacting protein pairs. where one protein contains a domain and its interacting protein contains the other. Every combinations of PID are summarized in a matrix table termed the PID matrix, and this matrix has proposed to be used for prediction of interactions. The database of interacting proteins (DIP) has used as a source of interacting protein pairs and InterPro, an integrated database of protein families, domains and functional sites, has used for defining domains in interacting pairs. A statistical scoring system. named "PID matrix score" has designed and applied as a measure of interaction probability between domains. Cross-validation has been performed with subsets of DIP data to evaluate the prediction accuracy of PID matrix. The prediction system gives about 50% of sensitivity and 98% of specificity, Based on the PID matrix, we develop a system providing several interaction information-finding services in the Internet. The system, named PreDIN (Prediction-oriented Database of Interaction Network) provides interacting domain finding services and interacting protein finding services. It is demonstrated that mapping of the genome-wide interaction network can be achieved by using the PreDIN system. This system can be also used as a new tool for functional prediction of unknown proteins.

• Korean Journal of Computational Design and Engineering
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• v.13 no.3
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• pp.209-216
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• 2008

This paper presents a novel approach to design evaluation of portable consumer electronic (PCE) products using augmented reality (AR) based tangible interaction and functional behavior simulation. In the approach, the realistic visualization is acquired by overlaying the rendered image of a PCE product on the real world environment in real-time using computer vision based augmented reality. For tangible user interaction in an AR environment, the user creates input events by touching specified regions of the product-type tangible object with the pointer-type tangible object. For functional behavior simulation, we adopt state transition methodology to capture the functional behavior of the product into a markup language-based information model, and build a finite state machine (FSM) to controls the transition between states of the product based on the information model. The FSM is combined with AR-based tangible objects whose operation in the AR environment facilitates the realistic visualization and functional simulation of the product, and thus realizes faster product design and development. Based on the proposed approach, a product design evaluation system has been developed and applied for the design evaluation of various PCE products with highly encouraging feedbacks from users.

• 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.

• BMB Reports
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• v.50 no.11
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• pp.535-536
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• 2017

A novel approach has been used to identify functional interactions relevant to human disease. Using high-throughput human-yeast genetic interaction screens, a first draft of disease interactome was obtained. This was achieved by first searching for candidate human disease genes that confer toxicity in yeast, and second, identifying modulators of toxicity. This study found potentially disease-relevant interactions by analyzing the network of functional interactions and focusing on genes implicated in amyotrophic lateral sclerosis (ALS), for example. In the subsequent proof-of-concept study focused on ALS, similar functional relationships between a specific kinase and ALS-associated genes were observed in mammalian cells and zebrafish, supporting findings in human-yeast genetic interaction screens. Results of combined analyses highlighted MAP2K5 kinase as a potential therapeutic target in ALS.

• Genomics & Informatics
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• v.5 no.3
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.

• Journal of Magnetics
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• v.16 no.4
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• pp.413-416
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• 2011

An array of FeCo nanotubes has been successfully fabricated in the pores of porous anodic aluminum oxide (AAO) templates by wetting templates method. The morphology and structure of the nanotube array were characterized by scanning electron microscopy, transmission electron microscopy and x-ray diffraction. The average diameter of the nanotubes was about 200 nm, and the length was more than 10 ${\mu}m$. Vibrating sample magnetometer and superconducting quantum interference device were used to investigate the magnetic properties of the nanotube array. Interaction between the nanotubes has been found to be demagnetizing as expected and the switching field distribution is broad.

• BMB Reports
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• v.34 no.5
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• pp.446-451
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• 2001

The interaction of cytochrome c with binary phospholipid mixtures was investigated by solid-state $^2H$- and $^{31}P$-NMR. To examine the effect of the interaction on the glycerol backbones, the glycerol moieties of phosphatidylcholine (PC), and cardioliph (CL) were specifically deuterated. On the binding of cytochrome c to the binary mixed bilayers, no changes in the quadrupole splittings of each of the components were observed for the PC/PG, PE/CL and PE/PG liposomes. In contrast, the splittings of CL decreased on binging of protein to the PC/CL liposomes, although those of PC did not change at all. This showed that cytochrome c specifically interacts with CL in PC/CL bilayers, and penetrates into the lipid bilayer to some extent so as to perturb the dynamic structure of the glycerol backbone. This is distinctly different from the mode of interaction of cytochrome c with other binary mixed bilayers. In the $^{31}P$-NMR spectra, line broadening and a decrease of the chemical shift anisotropy were observed on the binding of cytochrome c for all binary mixed bilayers that were examined. These changes were more significant for the PC/CL bilayers. Furthermore, the line broadening is more significant for PC than for CL in PC/CL bilayers. Therefore, it can be concluded that with the polar head groups, not only CL but also PC are involved in the interaction with cytochrome c.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1602-1610
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• 2004

As various genome projects have produced enormous amount of biosequence data, functional sequence analysis in terms of tile nucleic acid and protein becomes very significant. In functional genomics and proteomics, the functional analysis of each individual gene and protein remains a big challenge. Contrary to traditional studies, which regard proteins as not components of a whole protein interaction network but individual entities, recent studies have focused on examining functions and roles of each individual gene and protein in view of a whole life system. In this regard, it has been recognized as an appropriate method to analyze protein function on the basis of synthetic information of its interaction and domain modularity. In this context, this paper introduces the PIVS (Protein-protein interaction Inference & Visualization System), which predicts the interaction relationship of input proteins by taking advantage of information on homology degree, domain modules which input sequences contain, and protein interaction relationship. The information on domain modules can increase the accuracy of the function and interaction relationship analysis in terms of the specificity and sensitivity.