• 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.

• YAKHAK HOEJI
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• v.35 no.6
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• pp.461-465
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• 1991

Silica-based gel filtration chromatography has been used to characterize molecular weight of proteins. However, the molecular weight measured by this method was distorted by protein-silica interactions like hydrophobic and electrostatic forces. Therefore, we characterized protein-silica interaction using two forms of phytochrome (124 kDa) having different hydrophobicity and surface charge. PH and ionic strength affected the retention time of phytochrome suggesting that electrostatic force is the major interaction between protein and silica surface.

• Genomics & Informatics
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• v.2 no.2
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• pp.99-106
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• 2004

In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein­protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S. cerevisiae (bakers yeast) from MIPS and SGD.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.21-28
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• 2012

Predication of protein interaction sites for monomer structures can reduce the search space for protein docking and has been regarded as very significant for predicting unknown functions of proteins from their interacting proteins whose functions are known. In the other hand, the prediction of interaction sites has been limited in crystallizing weakly interacting complexes which are transient and do not form the complexes stable enough for obtaining experimental structures by crystallization or even NMR for the most important protein-protein interactions. This work reports the calculation of 3D surface patches of complex structures and their properties and a machine learning approach to build a predictive model for the 3D surface patches in interaction and non-interaction sites using support vector machine. To overcome classification problems for class imbalanced data, we employed an under-sampling technique. 9 properties of the patches were calculated from amino acid compositions and secondary structure elements. With 10 fold cross validation, the predictive model built from SVM achieved an accuracy of 92.7% for classification of 3D patches in interaction and non-interaction sites from 147 complexes.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.5
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• pp.267-281
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• 2006

PPI(Protein-Protein Interaction) data has information about the organism has maintained a life with some kind of mechanism. So, it is used in study about cure research back, cause of disease, and new medicine development. This PPI data has been increased by geometric progression because high throughput methods are developed such as Yeast-two-hybrid, Mass spectrometry, and Correlated mRNA expression. So, it is impossible that a person directly manage and analyze PPI data. Fortunately, PPI data is able to abstract the graph which has proteins as nodes, interactions as edges. Consequently, Graph theory plentifully researched from the computer science until now is able to be applied to PPI data successfully. In this paper, we introduce Proteinca(PROTEin INteraction CAbaret) workbench system for easily managing, analyzing and visualizing PPI data. Proteinca assists the user understand PPI data intuitively as visualizing a PPI data in graph and provide various analytical function on graph theory. And Protenica provides a simplified visualization with gravity-rule.

• Journal of Microbiology and Biotechnology
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• v.12 no.2
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• pp.286-291
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• 2002

Histone deacetylase I (HDAC1) works as one of the components in a nucleosome remodeling (NuRD) complex that consists of several proteins, including metastasis-associated protein 1 (MTA1). Since the protein-protein interaction of HDAC1 and MTA1 would appear to be important for both the integrity and functionality of the HDAC1 complex, the interruption of the HDAC1 and MTA1 interaction may be an efficient way to regulate the biological function of the HDAC1 complex. Based on this idea, a yeast two-hybrid system was constructed with HDAC1 and MTA1 expressing vectors in the DNA binding and activation domains, respectively. To verify the efficiency of the assay system, 3,500 microbial metabolite libraries were tested using the paper disc method, and KB0699 was found to inhibit the HDAC1 and MTA1 interaction without any toxicity to the wild-type yeast. Furthermore, KB0699 blocked the interaction of HDAC1 and MTA1 in an in vitro GST pull down assay and induced morphological changes in B16/BL6 melanoma cells, indicating the interruption of the HDAC1 complex function. Accordingly, these results demonstrated that the yeast assay strain developed in this study could be a valuable tool for the isolation of a HDAC1 complex disruptor.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.77-84
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• 2009

Intracellular signal transduction is achieved by protein-protein interaction. In this paper, we suggest performance algorithm based on Yeast protein-protein interaction and protein location information. We compare if pathways predicted with high valued weights indicate similar tendency with pathways provided in KEGG.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.1-6
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• 2006

The prediction of protein function basically make use of a protein-protein interaction map based on the concept of guilt-by-association. The method however cannot determine the functions of proteins in case that the target protein does not interact with proteins with known functions directly. This paper studies protein function prediction considering the given problem as a K-class classification problem and proposes a predictive approach utilizing a modular neural network. The proposed method uses interaction data and protein related attributes as well. The experimental results demonstrate that the proposed approach can predict the functional roles of Yeast proteins whose interaction knowledge is not known and shows better performance than the graph-based models that use protein interaction data.

• 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.