• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.

• 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.

• Bulletin of the Korean Chemical Society
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• v.35 no.1
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• pp.83-86
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• 2014

Poly(A) polymerase (PAP) play an essential role for maturation of mRNA by adding the adenylate residues at the 3' end. PAP functions are regulated through protein-protein interaction at its C-terminal region. In this study, cyclophilin A (CypA), a member of the peptidyl-prolyl cis-trans isomerase family, was identified as a partner protein interacting with the C-terminal region PAP. The interaction between PAP and CypA was inhibited by the immunosuppressive drug cyclosporine A. Deletion analysis revealed that the N-terminal 56 residues of CypA are sufficient for the interaction with PAP. Interestingly, we observed that PAP and CypA colocalize in the nucleus during SDF-1-induced chemotaxis, implying that CypA could be involved in the regulation of polyadenylation by PAP in the chemotactic cells.

• 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.

• 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.

• 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.

• 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 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.

• 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.

• 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.