• The Journal of Natural Sciences
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• v.9 no.1
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• pp.25-29
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• 1997

To study the mechanism of lipocortin-1, the 37 kDa protein, one of the annxin superfamily thought to be a second messenger during the Glucocorticoid dependent anti-inflammatory action, the gene for lipocortin-1 binding protein was isolated using the yeast two hybrid assay, the yeast based genetic assay recognizing the protein-protein interaction. The results showed that this gene has a weak homology to the for the human serine proteinase.

• BMB Reports
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• v.37 no.1
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• pp.45-52
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• 2004

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5325-5330
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• 2014

Background: Chronic myelocytic leukemia is a disease that threatens both adults and children. Great progress has been achieved in treatment but protein-protein interaction networks underlining chronic myelocytic leukemia are less known. Objective: To develop a protein-protein interaction network for chronic myelocytic leukemia based on gene expression and to predict biological pathways underlying molecular complexes in the network. Materials and Methods: Genes involved in chronic myelocytic leukemia were selected from OMIM database. Literature mining was performed by Agilent Literature Search plugin and a protein-protein interaction network of chronic myelocytic leukemia was established by Cytoscape. The molecular complexes in the network were detected by Clusterviz plugin and pathway enrichment of molecular complexes were performed by DAVID online. Results and Discussion: There are seventy-nine chronic myelocytic leukemia genes in the Mendelian Inheritance In Man Database. The protein-protein interaction network of chronic myelocytic leukemia contained 638 nodes, 1830 edges and perhaps 5 molecular complexes. Among them, complex 1 is involved in pathways that are related to cytokine secretion, cytokine-receptor binding, cytokine receptor signaling, while complex 3 is related to biological behavior of tumors which can provide the bioinformatic foundation for further understanding the mechanisms of chronic myelocytic leukemia.

• Korean Journal of Veterinary Service
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• v.42 no.2
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• pp.73-83
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• 2019

Foot-and-Mouth Disease (FMD) is a highly contagious trans-boundary viral disease caused by FMD virus, which causes huge economic losses. FMDV infects cloven hoofed (two-toed) mammals such as cattle, sheep, goats, pigs and various wildlife species. To control the FMDV, it is necessary to understand the life cycle and the pathogenesis of FMDV in host. Especially, the protein-protein interaction between FMDV and host will help to understand the survival cycle of viruses in host cell and establish new therapeutic strategies. However, the computational approach for protein-protein interaction between FMDV and pig hosts have not been applied to studies of the onset mechanism of FMDV. In the present work, we have performed the prediction of the pig's proteins which interact with FMDV based on RNA-Seq data, protein sequence, and structure information. After identifying the virus-host interaction, we looked for meaningful pathways and anticipated changes in the host caused by infection with FMDV. A total of 78 proteins of pig were predicted as interacting with FMDV. The 156 interactions include 94 interactions predicted by sequence-based method and the 62 interactions predicted by structure-based method using domain information. The protein interaction network contained integrin as well as STYK1, VTCN1, IDO1, CDH3, SLA-DQB1, FER, and FGFR2 which were related to the up-regulation of inflammation and the down-regulation of cell adhesion and host defense systems such as macrophage and leukocytes. These results provide clues to the knowledge and mechanism of how FMDV affects the host cell.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.679-688
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• 2006

Protein-protein interaction data obtained from high-throughput experiments includes high false positives. In this paper, we introduce a new protein-protein interaction reliability verification system. The proposed system integrates various biological features related with protein-protein interactions, and then selects the most relevant and informative features among them using a feature selection method. To assess the reliability of each protein-protein interaction data, the system construct a classifier that can distinguish true interacting protein pairs from noisy protein-protein interaction data based on the selected biological evidences using a classification technique. Since the performance of feature selection methods and classification techniques depends heavily upon characteristics of data, we performed rigorous comparative analysis of various feature selection methods and classification techniques to obtain optimal performance of our system. Experimental results show that the combination of feature selection method and classification algorithms provide very powerful tools in distinguishing true interacting protein pairs from noisy protein-protein interaction dataset. Also, we investigated the effects on performances of feature selection methods and classification techniques in the proposed protein interaction verification system.

• Genomics & Informatics
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• v.7 no.3
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• pp.141-147
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• 2009

Developed proteome-scale ortholog and paralog prediction methods are mainly based on sequence similarity. However, it is known that even the closest BLAST hit often does not mean the closest neighbor. For this reason, we added conserved interaction information to find orthologs. We propose a genome-scale, automated ortholog prediction method, named OrthoInterBlast. The method is based on both sequence and interaction similarity. When we applied this method to fly and yeast, 17% of the ortholog candidates were different compared with the results of Inparanoid. By adding protein-protein interaction information, proteins that have low sequence similarity still can be selected as orthologs, which can not be easily detected by sequence homology alone.

• 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.

• Journal of Microbiology and Biotechnology
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• v.11 no.4
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• pp.724-726
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• 2001

The growth of baceriophage T4 is inhibited by the presence of the tin gene product o bacteriophage P2. The interaction between purified Tin and gp32 proteins was observed using coimmunoprecipitation experiments. The in vivo interaction was confirmed by yeast two-hybrid experiments. A deletion analysis showed that the Asp 163 region of gp32 to DNA substrates was not affected by the presence of Tin, Thus, it would appear that the inhibition of 4 growth by Tin was due to a protein-protein interaction rather than affecting the DNA-binding ability of gp32.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.427-435
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• 2005

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However, most of the techniques still have many limitations to be used in real fields. They usually suffer from a low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Probability equations are devised and proposed in the framework of domain combination based protein interaction prediction method. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the precision of the prediction in case of the protein pair group having the matching PIP(Primary Interaction Probability) values in the interacting or non interacting PIP distributions.

• Journal of Life Science
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• v.6 no.3
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• pp.185-192
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• 1996

Bacteriophage T7 gene 2.5 protein, a single-stranded DNA binding protein, is required for T7 DNA replication, recombination, and repair. T7 gene 2.5 protein has two distinctive domains, DNA binding and C-terminal domain, directly involved in protein-protein interaction. Gene 2.5 protein participates in the DNA replication of Bacteriophage T7, which makes this protein essential for the T7 growth and DNA replication. What gene 2.5 protein makes important at T7 growth and DNA replication is its binding affinity to single-stranded DNA and the protein-protein important at T7 DNA replication proteins which are essential for the T7 DNA synthesis. We have constructed pGST2.5(WT) encoding the wild-type gene 2.5 protein and pGST2.5$\Delta $21C lacking C-terminal 21 amino acid residues. The purified GST-fusion proteins, GST2.5(WT) and GST2.5(WT)$\Delta$21C, were used for whether the carboxyl-terminal domain participates in the protein-protein interactions or not. GST2.5(WT) and GST2.5$\Delta$21C showed the difference in the protein-protein interaction. GST2.5(WT) interacted with T7 DNA polymerase and gene 4 protein, but GST2.5$\Delta$21C did not interact with either protein. Secondly, GST2.5(WT) interacts with gene 4 proteins (helicase/primase) but not GST2.5$\Delta$21C. these results proved the involvement of the carboxyl-terminal domain of gene 2.5 protein in the protein-protein interaction. We clearly conclude that carboxy-terminal domain of gene 2.5 protein is firmly involved in protein-protein interactions in T7 replication proteins.