• Proceedings of the Microbiological Society of Korea Conference
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• 2000.10a
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• pp.15-23
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• 2000

The subcellular localization of the SopB protein, which is encoded by the Escherichia coli F plasmid and is involved in the partition of the single-copy plasmid, was directly visualized through the expression of the protein fused to the jellyfish green fluorescent protein (GFP). The fusion protein was found to localize to positions close but not at the poles of exponentially growing cells. Examination of derivatives of the fusion protein lacking various regions of SopB suggests that the signal for the cellular localization of SopB resides in a region close to its N terminus. Overexpression of SopB led to silencing of genes linked to, but well-separated from, a cluster of SopB-binding sites termed sopC. In this SopB-mediated repression of sopC-linked genes, all but the N-terminal 82 amino acids of SopB can be replaced by the DNA-binding domain of a sequence-specific DNA -binding protein, provided that the sopC locus is also replaced by the recognition sequence of the DNA-binding domain. These results suggest a mechanism of gene silencing: patches of closely packed DNA-binding protein is localized to specific cellular sites; such a patch can capture a DNA carrying the recognition site of the DNA -binding domain and sequestrate genes adjacent to the recognition site through nonspecific binding of DNA.

• The Plant Pathology Journal
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• v.33 no.1
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• pp.53-65
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• 2017

Barley yellow dwarf virus (BYDV) belongs to Luteovirus and is limited only at phloem related tissues. An open reading frame (ORF) 4 of BYDV codes for the movement protein (MP) of BYDV gating plasmodesmata (PD) to facilitate virus movement. Like other Luteoviruses, ORF 4 of BYDV is embedded in the ORF3 but expressed from the different reading frame in leaky scanning manner. Although MP is a very important protein for systemic infection of BYDV, there was a little information. In this study, MP was characterized in terms of subcellular localization and programmed cell death (PCD). Gene of MP or its mutant (ΔMP) was expressed by Agroinfiltration method. MP was clearly localized at the nucleus and the PD, but ΔMP which was deleted distal N-terminus of MP showed no localization to PD exhibited the different target with original MP. In addition to PD localization, MP appeared associated with small granules in cytoplasm whereas ΔMP did not. MP associated with PD and small granules induced PCD, but ΔMP showed no association with PD and small granules did not exhibit PCD. Based on this study, the distal N-terminal region within MP is seemingly responsible for the localization of PD and the induction small granules and PCD induction. These results suggest that subcellular localization of BYDV MP may modulate the PCD in Nicotiana benthamiana.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.803-811
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• 2010

Since protein subcellular location and biological function are highly correlated, the prediction of protein subcellular localization can provide information about the function of a protein. In order to enhance the prediction performance, external information other than amino acids sequence information is actively exploited in many researches. This paper compares the prediction capabilities resided in amino acid sequence similarity, protein profile, gene ontology, motif, and textual information. In the experiments using PLOC dataset which has proteins less than 80% sequence similarity, sequence similarity information and gene ontology are effective information, achieving a classification accuracy of 94.8%. In the experiments using BaCelLo IDS dataset with low sequence similarity less than 30%, using gene ontology gives the best prediction accuracies, 93.2% for animals and 86.6% for fungi.

• Molecules and Cells
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• v.20 no.1
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• pp.119-126
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• 2005

${\alpha}$-Expansins are bound to the cell wall of plants and can be solubilized with an extraction buffer containing 1 M NaCl. Localization of ${\alpha}$-expansins in the cell wall was confirmed by immunogold labeling and electron microscopy. The subcellular localization of vegetative ${\beta}$-expansins has not yet been studied. Using antibodies specific for OsEXPB3, a vegetative ${\beta}$-expansin of rice (Oryza sativa L.), we found that OsEXPB3 is tightly bound to the cell wall and, unlike ${\alpha}$-expansins, cannot be solubilized with extraction buffer containing 1 M NaCl. OsEXPB3 protein could only be extracted with buffer containing SDS. The subcellular localization of the OsEXPB3 protein was confirmed by immunogold labeling and electron microscopy. Gold particles were mainly distributed over the primary cell walls. Immunohistochemistry showed that OsEXPB3 is present in all regions of the coleoptile and root tissues tested.

• BMB Reports
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• v.41 no.2
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• pp.170-175
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• 2008

In protein therapy, it is important for exogenous protein to be delivered into the target subcellular localization. To transduce a therapeutic protein into its specific subcellular localization, we synthesized nuclear localization signal (NLS) and membrane translocation sequence signal (MTS) peptides and produced a genetic in-frame SOD fusion protein. The purified SOD fusion proteins were efficiently transduced into mammalian cells with enzymatic activities. Immunofluorescence and Western blot analysis revealed that the SOD fusion proteins successfully transduced into the nucleus and the cytosol in the cells. The viability of cells treated with paraquat was markedly increased by the transduced fusion proteins. Thus, our results suggest that these peptides should be useful for targeting the specific localization of therapeutic proteins in various human diseases.

• Korean Journal of Microbiology
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• v.42 no.4
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• pp.271-276
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• 2006

Previously we demonstrated that virus-inducible stress protein (VISP) is induced in fish cells by the infection of a fish rhabdovirus. In this paper, we investigated the subcellular localization of the VISP and determined the region of VISP responsible for the subcellular localization. The CHSE-214 cells were stained with monoclonal antibody raised against VISP and observed with confocal microscope to detect the endogenous VISP. The results showed that the VISP localizes to the perinuclear region as spots. A plasmid expressing VISP fused to enhanced green fluorescent protein (EGFP) was constructed. The transient expression of full-length VISP fused to EGFP in CHSE-214 cells confirmed the spot formation of the VISP at perinuclear region. To determine the region responsible for the perinuclear localization of the VISP, we constructed a series of deletion mutants and, by using these deletion mutants, we found that C-terminal region of the VISP (aa 612-710) is essential for the perinuclear distribution of VISP and that this region contained nuclear receptor binding motif (691-TLTSLLL-697). Our results suggest that VISP localizes to the perinuclear region and C-terminal regions are important for this localization. Further studies on the role of the perinuclear localization of VISP in IHNV growth mali reveal the novel mechanism of IHNV pathogenecity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.992-999
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• 2014

This paper presents an extensive experimental comparison of a variety of multi-label learning methods for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. We compared several methods from three categories of multi-label classification algorithms: algorithm adaptation, problem transformation, and meta learning. Experimental results are analyzed using 12 multi-label evaluation measures to assess the behavior of the methods from a variety of view-points. We also use a new summarization measure to find the best performing method. Experimental results show that the best performing methods are power-set method pruning a infrequently occurring subsets of labels and classifier chains modeling relevant labels with an additional feature. futhermore, ensembles of many classifiers of these methods enhance the performance further. The recommendation from this study is that the correlation of subcellular locations is an effective clue for classification, this is because the subcellular locations of proteins performing certain biological function are not independent but correlated.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.12-16
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• 2007

The function of a protein is closely co-related with its subcellular location(s). Given a protein sequence, therefore, how to determine its subcellular location is a vitally important problem. We have developed a new prediction method for protein subcellular location(s), which is based on n-gram feature extraction and k-nearest neighbor (kNN) classification algorithm. It classifies a protein sequence to one or more subcellular compartments based on the locations of top k sequences which show the highest similarity weights against the input sequence. The similarity weight is a kind of similarity measure which is determined by comparing n-gram features between two sequences. Currently our method extract penta-grams as features of protein sequences, computes scores of the potential localization site(s) using kNN algorithm, and finally presents the locations and their associated scores. We constructed a large-scale data set of protein sequences with known subcellular locations from the SWISS-PROT database. This data set contains 51,885 entries with one or more known subcellular locations. Our method show very high prediction precision of about 93% for this data set, and compared with other method, it also showed comparable prediction improvement for a test collection used in a previous work.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.237-242
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• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.

• Proceedings of the Korean Aquaculture Society Conference
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• 2006.05a
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• pp.522-523
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• 2006