Volume: 2, Issue: 3

Review Article

Proteomics Reveals that Proteins Expressed During the Early Stage of Bacillus anthracis Infection Are Potential Targets for the Devel- opment of Vaccines and Drugs

In this review, we advance a new concept in developing vaccines and/or drugs to target specific proteins expressed during the early stage of Bacillus anthracis (an- thrax) infection and address existing challenges to this concept. Three proteins (immune inhibitor A, GPR-like spore protease, and alanine racemase) initially identified by proteomics in our laboratory were found to have differential expres- sions during anthrax spore germination and early outgrowth. Other studies of different bacillus strains indicate that these three proteins are involved in either germination or cytotoxicity of spores, suggesting that they may serve as potential targets for the design of anti-anthrax vaccines and drugs.
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Page 143-151

Review Article

Application of Proteomics in the Study of Tumor Metastasis

Tumor metastasis is the dominant cause of death in cancer patients. However, the molecular and cellular mechanisms underlying tumor metastasis are still elusive. The identification of protein molecules with their expressions correlated to the metastatic process would help to understand the metastatic mechanisms and thus facilitate the development of strategies for the therapeutic interventions and clini- cal management of cancer. Proteomics is a systematic research approach aiming to provide the global characterization of protein expression and function under given conditions. Proteomic technology has been widely used in biomarker discovery and pathogenetic studies including tumor metastasis. This article provides a brief review of the application of proteomics in identifying molecular factors in tumor metastasis process. The combination of proteomics with other experimental ap- proaches in biochemistry, cell biology, molecular genetics and chemistry, together with the development of new technologies and improvements in existing method- ologies will continue to extend its application in studying cancer metastasis.
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Page 152-166

Research Article

Identification of Protein Coding Regions of Rice Genes Using Alternative Spectral Rotation Measure and Linear Discriminant Analysis

An improved method, called Alternative Spectral Rotation (ASR) measure, for predicting protein coding regions in rice DNA has been developed. The method is based on the Spectral Rotation (SR) measure proposed by Kotlar and Lavner, and its accuracy is higher than that of the SR measure and the Spectral Content (SC) measure proposed by Tiwari et al. In order to increase the identifying accuracy, we chose three different coding characters, namely the asymmetric, purine, and stop-codon variables as parameters, and an approving result was presented by the method of Linear Discriminant Analysis (LDA).
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Page 167-173

Research Article

A Systematical Analysis of Tryptic Peptide Identification with Reverse Phase Liquid Chromatography and Electrospray Ion Trap Mass Spectrometry

In this study we systematically analyzed the elution condition of tryptic peptides and the characteristics of identified peptides in reverse phase liquid chromatogra- phy and electrospray tandem mass spectrometry (RPLC-MS/MS) analysis. Fol- lowing protein digestion with trypsin, the peptide mixture was analyzed by on-line RPLC-MS/MS. Bovine serum albumin (BSA) was used to optimize acetonitrile (ACN) elution gradient for tryptic peptides, and Cytochrome C was used to retest the gradient and the sensitivity of LC-MS/MS. The characteristics of identified peptides were also analyzed. In our experiments, the suitable ACN gradient is 5% to 30% for tryptic peptide elution and the sensitivity of LC-MS/MS is 50 fmol. Analysis of the tryptic peptides demonstrated that longer (more than 10 amino acids) and multi-charge state (+2, +3) peptides are likely to be identified, and the hydropathicity of the peptides might not be related to whether it is more likely to be identified or not. The number of identified peptides for a protein might be used to estimate its loading amount under the same sample background. Moreover, in this study the identified peptides present three types of redundancy, namely iden- tification, charge, and sequence redundancy, which may repress low abundance protein identification.
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Page 174-183

Letter

A Novel Algorithm for Finding Interspersed Repeat Regions

The analysis of repeats in the DNA sequences is an important subject in bioin- formatics. In this paper, we propose a novel projection-assemble algorithm to find unknown interspersed repeats in DNA sequences. The algorithm employs random projection algorithm to obtain a candidate fragment set, and exhaustive search algorithm to search each pair of fragments from the candidate fragment set to find potential linkage, and then assemble them together. The complexity of our projection-assemble algorithm is nearly linear to the length of the genome sequence, and its memory usage is limited by the hardware. We tested our algo- rithm with both simulated data and real biology data, and the results show that our projection-assemble algorithm is efficient. By means of this algorithm, we found an un-labeled repeat region that occurs five times in Escherichia coli genome, with its length more than 5,000 bp, and a mismatch probability less than 4%.
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Page 184-191

Method

SeeDNA: A Visualization Tool for K-string Content of Long DNA Sequences and Their Randomized Counterparts

An interactive tool to visualize the K-string composition of long DNA sequences including bacterial complete genomes is described. It is especially useful for ex- ploring short palindromic structures in the sequences. The SeeDNA program runs on Red Hat Linux with GTK+ support. It displays two-dimensional (2D) or one-dimensional (1D) histograms of the K-string distribution of a given sequence and/or its randomized counterpart. It is also capable of showing the difference of K -string distributions between two sequences. The C source code using the GTK+ package is freely available.
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Page 192-196

Research Resource

Timeline of Genomics (1951–1976)*

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Page 197-208