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Free Content Editorial [Hot Topic: The Application of Systems Biology and Bioinformatics Methods in Proteomics, Transcriptomics and Metabolomics (Guest Editor: Yu-Dong Cai)]

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We are glad to offer this special issue to the readers with fifteen papers focused on the application of systems biology and bioinformatics methods in proteomics, transcriptomics, and metabolomics.

Currently, more and more large-scale biology data, such as sequences, gene expression and protein-protein interactions, have been stored in the databases. Therefore, data analysis methods, such as machine learning, graph theory approach, and statistics analysis, are widely used in various areas of systems biology and bioinformatics, in order to predict the functions of proteins, their genes and networks, as well as their interactions with compounds.

The articles of this issue can be roughly categorized into the following four groups.

1. Proteomics

The first two papers are focused on protein subcellular location prediction. Wu et al. report a novel web-server predictor, called “iLoc-Gpos”, by introducing the multi-layer scale. It can be used to predict the subcellular localization of Gram positive bacterial proteins with both single-location and multiple-location sites. The web-server for the iLoc-Gpos predictor is freely accessible to the public at Wang et al. developed a new web-server named “PSCL” for plant protein subcellular localization prediction based on the optimized functional domains. For the dataset constructed by them, PSCL achieved a first-order predicted accuracy of 75.7% by jackknife test, a quite encouraging outcome.

The next four papers are focused on the prediction of protein structure and properties. Hu et al. report a new approach to classify protein quaternary structure based on the sequence information. The results thus obtained are important for identifying protein function. In the paper by Yan et al., the authors report the application of using neural network to predict the optimal pH and temperature of cellulases. The information thus acquired is particularly useful for finding the optimal working conditions in enzymatic reactions. Mizianty and Kurgan have developed a novel approach for high-throughput sequence-based prediction of the propensity of protein chains for X-ray crystallography-based structure determination. Their method is shown to outperform the current approaches as evidenced by empirical tests on three benchmark datasets. This methodology finds useful applications in support of the target selection procedures that are implemented by structural genomics centers. Pugalenthi et al. have used the Random Forest Method to predict the residue solvent accessibility based on the protein sequence information. Their approach can be used for the prediction of residue solvent accessibility from protein sequence without the need of structural information.

The other two papers are written to address the relationship between the amino acid variants and disease. Li et al. report a web-server called SCYPPred that was developed based on the SVM flanking sequence method and that can be used to predict human cytochrome P450 SNPs (Single Nucleotide Polymorphisms). Prostate Cancer is a serious disease. Cai et al. report that UGT2B17 might be one of the risk factors for Prostate Cancer in men. The conclusion was based on a comprehensive metaanalysis on the correlation of prostate cancer with variants in CYP17 and UGT2B17.

The last one is about the post-translation modification. He et al. developed a novel sequence-based method for serine, threonine, and tyrosine phosphorylation site prediction, by applying the machine learning approach and feature selection procedure. It can be used to predict whether a protein contains phosphorylation sites and their exact sites according to the sequence of the protein concerned.....
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Document Type: Research Article

Publication date: 01 January 2012

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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