Article Online

Articles Online (Volume 4, Issue 1)

Review Article

Sequence Similarity and Functional Relationship Among Eukaryotic ZIP and CDF Transporters

Taiho Kambe ,Tomoyuki Suzuki,Masaya Nagao,Yuko Yamaguchi-Iwai

ZIP (ZRT/IRT-like Protein) and CDF (Cation Diffusion Facilitator) are two large metal transporter families mainly transporting zinc into and out of the cytosol. Several ZIP and CDF transporters have been characterized in mammals and various model organisms, such as yeast, nematode, fruit fly, and zebrafish, and many candidate genes have been identified by genome projects. Unexpected functions of ZIP and CDF transporters have been recently reported in some model organisms, leading to major advances in our understanding of the functions of mammalian counterparts. Here, we review the recent information on the sequence similarity and functional relationship among eukaryotic ZIP and CDF transporters obtained from the representative model organisms.

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Changes of Nuclear Matrix Proteins Following the Differentiation of Human Osteosarcoma MG-63 Cells

Chun-Hong Zhao, Qi-Fu Li, Yan Zhao, Jing-Wen Niu, Zhi-Xing Li, Jin-An Chen

Human osteosarcoma MG-63 cells were induced into differentiation by 5 mmol/L hexamethylene bisacetamide (HMBA). Their nuclear matrix proteins (NMPs) were selectively extracted and subjected to two-dimensional gel electrophoresis analysis. The results of protein patterns were analyzed by Melanie software. The spots of differentially expressed NMPs were excised and subjected to in situ digestion with trypsin. The maps of peptide mass fingerprinting were obtained by MALDI-TOF-MS analysis, and were submitted for NCBI database searches by Mascot tool. There were twelve spots changed remarkably during the differentiation induced by HMBA, nine of which were identified. The roles of the regulated proteins during the MG-63 differentiation were analyzed. This study suggests that the induced differentiation of cancer cells is accompanied by the changes of NMPs, and confirms the presence of some specific NMPs related to the cancer cell proliferation and differentiation. The changed NMPs are potential markers for cancer diagnosis or targets for cancer therapy.

Page 10–17


Effect of 5-azacytidine on the Protein Expression of Porcine Bone Marrow Mesenchymal Stem Cells in vitro

Neng-Sheng Ye, Rong-Li Zhang, Yan-Feng Zhao, Xue Feng, Yi-Ming Wang, Guo-An Luo

Bone marrow-darived mesenchymal stem cells (MSCs) are pluripotent stem cells that show a vital potential in the clinical application for cell transplantation. In the present paper, proteomic techniques were used to approach the protein profiles associated with porcine bone marrow MSCs and investigate the regulation of MSC proteins on the effect of 5-azacytidine (5-aza). Over 1,700 protein species were separated from MSCs according to gel analysis. Compared with the expression profiling of control MSCs, there were 11 protein spots up-regulated and 26 down-regulated in the protein pattern of 5-aza-treated cells. A total of 21 proteins were successfully identified by MALDI-TOF-MS analysis, among which some interesting proteins, such as alpha B-crystallin, annexin A2, and stathmin 1, had been reported to involve in cell proliferation and differentiation through different signaling pathways. Our data should be useful for the future study of MSC differentiation and apoptosis.

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High-throughput Three-dimensional Gel Electrophoresis for Versatile Utilities: A Stacked Slice-gel System for Separation and Reactions (4SR)

Md. Salimullah, Masaki Mori, Koichi Nishigaki

A novel high-throughput system, called the stacked slice-gel system for separation and reactions (4SR), was developed for the analysis of DNA/RNA and protein/peptide. The system provides a novel three-dimensional gel electrophoresis approach that exploits the property of stacked slice gels. It allows multiple samples simultaneously to react as well as to be separated, offering a two-dimensional (m × n) sample loading system. For this purpose, high-throughput multi-micro vessels (MMVs) containing variable numbers of wells (100 wells in this paper) have been used, which are made of 25 mm square-size polyacrylamide gels. Furthermore, after electrophoretic separation, a slice gel containing a desired sample can be easily removed and proceeded to the next step. Different biological reactions as well as successive separation of products were effectively carried out dealing with DNA/RNA and protein/peptide. It shows that this system has a diversity of potentials to be developed.

Page 26–33


Predicting the Nuclear Localization Signals of 107 Types of HPV L1 Proteins by Bioinformatic Analysis

Jun Yang, Yi-Li Wang, Lü-Sheng Si

In this study, 107 types of human papillomavirus (HPV) L1 protein sequences were obtained from available databases, and the nuclear localization signals (NLSs) of these HPV L1 proteins were analyzed and predicted by bioinformatic analysis. Out of the 107 types, the NLSs of 39 types were predicted by PredictNLS software (35 types of bipartite NLSs and 4 types of monopartite NLSs). The NLSs of the remaining HPV types were predicted according to the characteristics and the homology of the already predicted NLSs as well as the general rule of NLSs. According to the result, the NLSs of 107 types of HPV L1 proteins were classified into 15 categories. The different types of HPV L1 proteins in the same NLS category could share the similar or the same nucleocytoplasmic transport pathway. They might be used as the same target to prevent and treat different types of HPV infection. The results also showed that bioinformatic technology could be used to analyze and predict NLSs of proteins.

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VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition

Sudipto Saha, G.P.S. Raghava

In this study, an attempt has been made to predict the major functions of gramnegative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins (255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors). First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins (

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PredSL: A Tool for the N-terminal Sequence-based Prediction of Protein Subcellular Localization

Evangelia I. Petsalaki, Pantelis G. Bagos, Zoi I. Litou, Stavros J. Hamodrakas

The ability to predict the subcellular localization of a protein from its sequence is of great importance, as it provides information about the protein's function. We present a computational tool, PredSL, which utilizes neural networks, Markov chains, profile hidden Markov models, and scoring matrices for the prediction of the subcellular localization of proteins in eukaryotic cells from the N-terminal amino acid sequence. It aims to classify proteins into five groups: chloroplast, thylakoid, mitochondrion, secretory pathway, and “other”. When tested in a fivefold cross-validation procedure, PredSL demonstrates 86.7% and 87.1% overall accuracy for the plant and non-plant datasets, respectively. Compared with TargetP, which is the most widely used method to date, and LumenP, the results of PredSL are comparable in most cases. When tested on the experimentally verified proteins of the Saccharomyces cerevisiae genome, PredSL performs comparably if not better than any available algorithm for the same task. Furthermore, PredSL is the only method capable for the prediction of these subcellular localizations that is available as a stand-alone application through the URL:

Page 48–55



Axel Mosig, Katrin Sameith, Peter Stadler

Many classes of non-coding RNAs (ncRNAs; including Y RNAs, vault RNAs, RNase P RNAs, and MRP RNAs, as well as a novel class recently discovered in Dictyostelium discoideum) can be characterized by a pattern of short but well-conserved sequence elements that are separated by poorly conserved regions of sometimes highly variable lengths. Local alignment algorithms such as BLAST are therefore ill-suited for the discovery of new homologs of such ncRNAs in genomic sequences. The Fragrep tool instead implements an efficient algorithm for detecting the pattern fragments that occur in a given order. For each pattern fragment, the mismatch tolerance and bounds on the length of the intervening sequences can be specified separately. Furthermore, matches can be ranked by a statistically well-motivated scoring scheme.

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A Quasi-physical Algorithm for the Structure Optimization in an Off-lattice Protein Model

Jing-Fa Liu, Wen-Qi Huang

In this paper, we study an off-lattice protein AB model with two species of monomers, hydrophobic and hydrophilic, and present a heuristic quasi-physical algorithm. First, by elaborately simulating the movement of the smooth solids in the physical world, we find low-energy conformations for a given monomer chain. A subsequent off-trap strategy is then proposed to trigger a jump for a stuck situation in order to get out of the local minima. The algorithm has been tested in the three-dimensional AB model for all sequences with lengths of 13–55 monomers. In several cases, we renew the putative ground state energy values. The numerical results show that the proposed methods are very promising for finding the ground states of proteins.

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