• 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.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.286-288
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• 2004

단백질 상호작용 데이터는 현 생물정보학에서 기능이 알려지지 않은 단백질의 기능 예측에 높은 신뢰성이 있는 프로티오믹스의 계산 모델에 이용되고 있다. 일반적으로 이 단백질 기능 예측 알고리즘들은 대규모의 2차원 단백질-단백질 상호작용 맵에서 Guilt-by-Association 개념 기반으로 개발되고 있다. 본 논문에서는 단백질-단백질 상호작용 데이터를 이용한 그래프 기반 단백질 기능 예측 모델을 개발하였다. 특히, 이 모델은 대량의 상호작용 데이터에서 정확한 기능 예측을 수행할 수 있다는 장점을 가지고 있다. 이를 위해 Yeast에 대한 단백질 상호작용 맵, Homology 및 Interaction Generality를 이용하여 이 모델을 평가하였다.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.1941-1945
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• 2005

To raises the possibility of designing effective inhibitors, 3D-QSAR for the inhibition of calcineurin-NFAT signaling by new N-(4-oxo-1(4H)-naphthalenylidene benzenesulfonamide derivatives as inhibitors of intracellular protein-protein interactions were studied using CoMFA and CoMSIA methodology. The three templates, N-(4-oxo-1(4H)-naphthalenylidene)benzenesulfonamide (A), benzenesulfonamide (B) and 4-oxo-1(4H)-naphthalenylidene (C) were selected to improve the statistic of the present 3D-QSAR models. The best models with combination of standard field in CoMFA, and steric field and electrostatic field in CoMSIA derived from the template, B and C, because most of the compounds tend not to be aligned in template A. From the based on the CoMFA and CoMSIA contour maps, the $R_1$ and $R_2$ groups on 4-oxo-1(4H) naphthalenylidene ring are steric favor. The ortho position on the benzenesulfonyl ring is steric disfavor and the meta position is steric favor. In addition, the oxygene atom of carbonyl group will have better inhibition activities as it has a negative charge favor. From these findings, we can conclude that the analyses of the contour maps provided insight into possible modification of molecules for effective inhibitiors.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.299-308
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• 2004

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 probability 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 for 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 teaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• Bulletin of the Korean Chemical Society
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• v.29 no.9
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• pp.1717-1722
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• 2008

The 70 kDa heat-shock protein (Hsp70) involved in various cellular functions, such as protein folding, translocation and degradation, regulates apoptosis in cancer cells. Recently, it has been reported that the green tea flavonoid (−)-epigallocatechin 3-gallate (EGCG) induces apoptosis in numerous cancer cell lines and could inhibit the anti-apoptotic effect of human Hsp70 ATPase domain (hATPase). In the present study, docking model between EGCG and hATPase was determined using automated docking study. Epi-gallo moiety in EGCG participated in hydrogen bonds with side chain of K71 and T204, and has metal chelating interaction with hATPase. Hydroxyl group of catechin moiety also participated in metal chelating hydrogen bond. Gallate moiety had two hydrogen bondings with side chains of E268 and K271, and hydrophobic interaction with Y15. Based on this docking model, we determined two pharmacophore maps consisted of six or seven features, including three or four hydrogen bonding acceptors, two hydrogen bonding donors, and one lipophilic. We searched a flavonoid database including 23 naturally occurring flavonoids and 10 polyphenolic flavonoids with two maps, and myricetin and GC were hit by map I. Three hydroxyl groups of B-ring in myricetin and gallo moiety of GC formed important hydrogen bonds with hATPase. 7-OH of A-ring in myricetin and OH group of catechin moiety in GC are hydrogen bond donors similar to gallate moiety in EGCG. From these results, it can be proposed that myricetin and GC can be potent inhibitors of hATPase. This study will be helpful to understand the mechanism of inhibition of hATPase by EGCG and give insights to develop potent inhibitors of hATPase.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.26-51
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• 2003

Large scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in thePDB. PSIMAP incorporates both functional and evolutionary information into a single network. It makes it possible to age protein domains in terms of taxonomic diversity, interaction and function. One consequence of it is to predict the most important protein domain structure in evolution. We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: ${\bullet}$ Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. ${\bullet}$ Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. ${\bullet}$ Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. This led to the prediction of the oldest and most important protein domain in evolution of lift. ${\bullet}$ Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level.

• Mass Spectrometry Letters
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• v.9 no.2
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• pp.51-55
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• 2018

Capture of non-glycoproteins during lectin affinity chromatography is frequently observed, although it would seem to be anomalous. In actuality, lectin affinity chromatography works at post-translational modification (PTM) sites on a glycoprotein which is not involved in protein-protein interactions (PPIs). In this study, serial affinity column set (SACS) using lectins followed by proteomics methods was used to identify PPI mechanisms of captured proteins in human plasma. MetaCore, STRING, Ingenuity Pathway Analysis (IPA), and IntAct were individually used to elucidate the interactions of the identified abundant proteins and to obtain the corresponding interaction maps. The abundant non-glycoproteins were captured with the binding to the selected glycoproteins. Therefore, depletion process in sample pretreatment for abundant protein removal should be considered with more caution because it may lose precious disease-related low abundant proteins through PPIs of the removed abundant proteins in human plasma during the depletion process in biomarker discovery. Glycoproteins bearing specific glycans are frequently associated with cancer and can be specifically isolated by lectin affinity chromatography. Therefore, SACS using Lycopersicon esculentum lectin (LEL) can also be used to study disease interactomes.

• Genomics & Informatics
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• v.19 no.2
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• pp.14.1-14.6
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• 2021

Coronavirus disease 2019 (COVID-19) is an on-going pandemic disease infecting millions of people across the globe. Recent reports of reduction in antibody levels and the re-emergence of the disease in recovered patients necessitated the understanding of the pandemic at the core level. The cases of multiple organ failures emphasized the consideration of different organ systems while managing the disease. The present study employed RNA sequencing data to determine the disease associated differentially regulated genes and their related protein interactions in several organ systems. It signified the importance of early diagnosis and treatment of the disease. A map of protein interactions of multiple organ systems was built and uncovered CAV1 and CTNNB1 as the top degree nodes. A core interactions sub-network was analyzed to identify different modules of functional significance. AR, CTNNB1, CAV1, and PIK3R1 proteins were unfolded as bridging nodes interconnecting different modules for the information flow across several pathways. The present study also highlighted some of the druggable targets to analyze in drug re-purposing strategies against the COVID-19 pandemic. Therefore, the protein interactions map and the modular interactions of the differentially regulated genes in the multiple organ systems would incline the scientists and researchers to investigate in novel therapeutics for the COVID-19 pandemic expeditiously.

• Genomics & Informatics
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• v.6 no.3
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• pp.153-156
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• 2008

Complex diseases such as stroke and cancer have two or more genetic loci and are affected by environmental factors that contribute to the diseases. Due to the complex characteristics of these diseases, identifying candidate genes requires a system-level analysis of the following: gene ontology, pathway, and interactions. A database and user interface, termed StrokeBase, was developed; StrokeBase provides queries that search for pathways, candidate genes, candidate SNPs, and gene networks. The database was developed by using in silico data mining of HGNC, ENSEMBL, STRING, RefSeq, UCSC, GO, HPRD, KEGG, GAD, and OMIM. Forty candidate genes that are associated with cerebrovascular disease were selected by human experts and public databases. The networked cerebrovascular disease gene maps also were developed; these maps describe genegene interactions and biological pathways. We identified 1127 genes, related indirectly to cerebrovascular disease but directly to the etiology of cerebrovascular disease. We found that a protein-protein interaction (PPI) network that was associated with cerebrovascular disease follows the power-law degree distribution that is evident in other biological networks. Not only was in silico data mining utilized, but also 250K Affymetrix SNP chips were utilized in the 320 control/disease association study to generate associated markers that were pertinent to the cerebrovascular disease as a genome-wide search. The associated genes and the genes that were retrieved from the in silico data mining system were compared and analyzed. We developed a well-curated cerebrovascular disease-associated gene network and provided bioinformatic resources to cerebrovascular disease researchers. This cerebrovascular disease network can be used as a frame of systematic genomic research, applicable to other complex diseases. Therefore, the ongoing database efficiently supports medical and genetic research in order to overcome cerebrovascular disease.