• 한국유가공학회:학술대회논문집
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• 2007.09a
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• pp.31-37
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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.

• Genomics & Informatics
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• v.21 no.2
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• pp.23.1-23.9
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• 2023

The mammalian sirtuin family, consisting of SIRT1-SIRT7, plays a vital role in various biological processes, including cancer, diabetes, neurodegeneration, cardiovascular disease, cellular metabolism, and cellular homeostasis maintenance. Due to their involvement in these biological processes, modulating sirtuin activity seems promising to impact immuneand aging-related diseases, as well as cancer pathways. However, more understanding is required regarding the safety and efficacy of sirtuin-targeted therapies due to the complex regulatory mechanisms that govern their activity, particularly in the context of multiple targets. In this study, the interaction landscape of the sirtuin family was analyzed using a systems biology approach. A sirtuin protein-protein interaction network was built using the Cytoscape platform and analyzed using the NetworkAnalyzer and stringApp plugins. The result revealed the sirtuin family's association with numerous proteins that play diverse roles, suggesting a complex interplay between sirtuins and other proteins. Based on network topological and functional analysis, SIRT1 was identified as the most prominent among sirtuin family members, demonstrating that 25 of its protein partners are involved in cancer, 22 in innate immune response, and 29 in aging, with some being linked to a combination of two or more pathways. This study lays the foundation for the development of novel therapies that can target sirtuins with precision and efficacy. By illustrating the various interactions among the proteins in the sirtuin family, we have revealed the multifaceted roles of SIRT1 and provided a framework for their possible roles to be precisely understood, manipulated, and translated into therapeutics in the future.

• CELLMED
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• v.4 no.1
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• pp.6.1-6.12
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• 2014

Anthrax is the deadly disease for human being caused by Bacillus anthracis. Instantaneous research work on the mode of infection of the organism revealed that different proteases are involved in different steps of pathogenesis. Present study reports the in silico characterization and the detection of pathogenic proteases involved in anthrax infection through protein-protein interaction. A total of 13 acid, 9 neutral, and 1 alkaline protease of Bacillus anthracis were selected for analysing the physicochemical parameter, the protein superfamily and family search, multiple sequence alignment, phylogenetic tree construction, protein-protein interactions and motif finding. Among the 13 acid proteases, 10 were found as extracellular enzymes that interact with immune inhibitor A (InhA) and help the organism to cross the blood brain barrier during the process of infection. Multiple sequence alignment of above acid proteases revealed the position 368, 489, and 498-contained 100% conserved amino acids which could be used to deactivate the protease. Among the groups analyzed, only acid protease were found to interact with InhA, which indicated that metalloproteases of acid protease group have the capability to develop pathogenesis during B. anthracis infection. Deactivation of conserved amino acid position of germination protease can stop the sporulation and germination of B anthracis cell. The detailed interaction study of neutral and alkaline proteases could also be helpful to design the interaction network for the better understanding of anthrax disease.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.474-492
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• 2007

In the protein interaction network, there are many meaningful functional modules, each involving several protein interactions to perform discrete functions. Pathways and protein complexes are the examples of the functional modules. In this paper, we propose a new method for detecting the functional modules based on concept. A conceptual functional module, briefly concept module is introduced to match the modules taking them as its instances. It is defined by the corresponding rule composed of triples and operators between the triples. The triples represent conceptual relations reifying the protein interactions of a module, and the operators specify the structure of the module with the relations. Furthermore, users can define a composite concept module by the counterpart rule which, in turn, is defined in terms of the predefined rules. The concept module makes it possible to detect functional modules that are conceptually similar as well as structurally identical to users' queries. The rules are managed in the XML format so that they can be easily applied to other networks of different species. In this paper, we also provide a visualized environment for intuitionally describing complexly structured rules.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2273-2278
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• 2014

Purpose: The purpose of our study was to explore the molecular mechanisms in the process of oral squamous cells carcinoma (OSCC) development. Method: We downloaded the affymetrix microarray data GSE31853 and identified differentially expressed genes (DEGs) between OSCC and normal tissues. Then Gene Ontology (GO) and Protein-Protein interaction (PPI) networks analysis was conducted to investigate the DEGs at the function level. Results: A total 372 DEGs with logFCI >1 and P value < 0.05 were obtained, including NNMT, BAX, MMP9 and VEGF. The enriched GO terms mainly were associated with the nucleoplasm, response to DNA damage stimuli and DNA repair. PPI network analysis indicated that GMNN and TSPO were significant hub proteins and steroid biosynthesis and synthesis and degradation of ketone bodies were significantly dysregulated pathways. Conclusion: It is concluded that the genes and pathways identified in our work may play critical roles in OSCC development. Our data provides a comprehensive perspective to understand mechanisms underlying OSCC and the significant genes (proteins) and pathways may be targets for therapy in the future.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• IMMUNE NETWORK
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• v.1 no.3
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• pp.187-195
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• 2001

The expression of recombinant proteins fused to 26 kDa glutathione S-transferase (GST) extracted from Schistosoma japonicum represents an attractive system for purifiying proteins of interest in a single step using GST-affinity chromatography. In addition, the GST-tag is used conveniently for detecting fused proteins since its high solubility as well as its relatively small size rarely interferes with the biological activity of the fused protein. In this regard, the GST system is frequently applied for tracing fusion proteins in both prokaryotic and eukaryotic cells to elucidate the physiological interactions and functional compartments of proteins. To provide a further tool in analyzing GST-fusion proteins, a new monoclonal antibody, with a high specificity to the S. japonicum GST was produced. Methods: BALB/c mice were immunized both with recombinant S. japonicum GST proteins, and by the fusion of splenocytes from these mice with myeloma cells. From this, a new anti -GST monoclonal antibody, termed SARAH, was generated. The specificity and reactivity of this antibody was confirmed by ELISA and by Western blot analysis. Results: SARAH showed a high reactivity to recombinant GST and GST fusion protein but not with native mammalian GST proteins as derived from other species including humans, cows, rabbits and rats. The applicability of SARAH was further demonstrated by confocal laser scanning microscopy, where GST proteins that were expressed transiently in mouse fibroblast cells, were specifically detected without interference of endogenous GST. Conclusion: SARAH is new monoclonal antibody with a high specificity to recombinant GST proteins but not to endogenous GST in mammalian cells.

• Journal of Korean Neurosurgical Society
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• v.65 no.2
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• pp.236-244
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• 2022

Objective : To evaluate the interactions among differentially expressed autophagy and mitophagy markers in subarachnoid hemorrhage (SAH) patients with delayed cerebral ischemia (DCI). Methods : The expression data of autophagy and mitophagy-related makers in the cerebrospinal fluid (CSF) cells was analyzed by real-time reverse transcription-polymerase chain reaction and Western blotting. The markers included death-associated protein kinase (DAPK)-1, BCL2 interacting protein 3 like (BNIP3L), Bcl-1 antagonist X, phosphatase and tensin homolog-induced kinase (PINK), Unc-51 like autophagy activating kinase 1, nuclear dot protein 52, and p62. In silico functional analyses including gene ontology enrichment and the protein-protein interaction network were performed. Results : A total of 56 SAH patients were included and 22 (38.6%) of them experienced DCI. The DCI patients had significantly increased mRNA levels of DAPK1, BNIP3L, and PINK1, and increased expression of BECN1 compared to the non-DCI patients. The most enriched biological process was the positive regulation of autophagy, followed by the response to mitochondrial depolarization. The molecular functions ubiquitin-like protein ligase binding and ubiquitin-protein ligase binding were enriched. In the cluster of cellular components, Lewy bodies and the phagophore assembly site were enriched. BECN1 was the most connected gene among the differentially expressed markers related to autophagy and mitophagy in the development of DCI. Conclusion : Our study may provide novel insight into mitochondrial dysfunction in DCI pathogenesis.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.529-535
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• 2015

Biological networks have been handled with the static concept. However, life phenomena in cells occur depending on the cellular state and the external environment, and only a few proteins and their interactions are selectively activated. Therefore, we should adopt the dynamic network concept that the structure of a biological network varies along the flow of time. This concept is effective to analyze the progressive transition of the disease. In this paper, we applied the proposed method to Alzheimer's disease to analyze the structural and functional characteristics of the disease network. Using gene expression data and protein-protein interaction data, we constructed the sub-networks in accordance with the progress of disease (normal, early, middle and late). Based on this, we analyzed structural properties of the network. Furthermore, we found module structures in the network to analyze the functional properties of the sub-networks using the gene ontology analysis (GO). As a result, it was shown that the functional characteristics of the dynamics network is well compatible with the stage of the disease which shows that it can be used to describe important biological events of the disease. Via the proposed approach, it is possible to observe the molecular network change involved in the disease progression which is not generally investigated, and to understand the pathogenesis and progression mechanism of the disease at a molecular level.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.382-393
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• 2006

Mechanical system dynamics plays an important role in the area of computational structural biology. Elastic network models (ENMs) for macromolecules (e.g., polymers, proteins, and nucleic acids such as DNA and RNA) have been developed to understand the relationship between their structure and biological function. For example. a protein, which is basically a folded polypeptide chain, can be simply modeled as a mass-spring system from the mechanical viewpoint. Since the conformational flexibility of a protein is dominantly subject to its chemical bond interactions (e.g., covalent bonds, salt bridges, and hydrogen bonds), these constraints can be modeled as linear spring connections between spatially proximal representatives in a variety of coarse-grained ENMs. Coarse-graining approaches enable one to simulate harmonic and anharmonic motions of large macromolecules in a PC, while all-atom based molecular dynamics (MD) simulation has been conventionally performed with an aid of supercomputer. A harmonic analysis of a macroscopic mechanical system, called normal mode analysis, has been adopted to analyze thermal fluctuations of a microscopic biological system around its equilibrium state. Furthermore, a structure-based system optimization, called elastic network interpolation, has been developed to predict nonlinear transition (or folding) pathways between two different functional states of a same macromolecule. The good agreement of simulation and experiment allows the employment of coarse-grained ENMs as a versatile tool for the study of macromolecular dynamics.