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- Volume 19, Issue 1, 2012
Protein and Peptide Letters - Volume 19, Issue 1, 2012
Volume 19, Issue 1, 2012
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Preface
By Ben M. DunnThis issue marks the start of Volume 19 of Protein & Peptide Letters. Since the beginning in 1994, the journal has continued to attract new readers and contributors. This has resulted in a steady increase in the Impact Factor of PPL to it's current level of 1.849. We anticipate that this will continue to climb, especially with the large number of special Hot Topics issues we have published in 2011 and the ones planned for 2012. Of equ Read More
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Editorial [Hot Topic: The Application of Systems Biology and Bioinformatics Methods in Proteomics, Transcriptomics and Metabolomics (Guest Editor: Yu-Dong Cai)]
By Yu-Dong CaiWe 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 the Read More
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iLoc-Gpos: A Multi-Layer Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Gram-Positive Bacterial Proteins
Authors: Zhi-Cheng Wu, Xuan Xiao and Kuo-Chen ChouBy introducing the “multi-layer scale”, as well as hybridizing the information of gene ontology and the sequential evolution information, a novel predictor, called iLoc-Gpos, has been developed for predicting the subcellular localization of Gram positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Read More
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PSCL: Predicting Protein Subcellular Localization Based on Optimal Functional Domains
Authors: Kai Wang, Le-Le Hu, Xiao-He Shi, Ying-Song Dong, Hai-Peng Li and Tie-Qiao WenIt is well known that protein subcellular localizations are closely related to their functions. Although many computational methods and tools are available from Internet, it is still necessary to develop new algorithms in this filed to gain a better understanding of the complex mechanism of plant subcellular localization. Here, we provide a new web server named PSCL for plant protein subcellular localization prediction by employin Read More
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Prediction of Protein Quaternary Structure with Feature Selection and Analysis Based on Protein Biological Features
Authors: Le-Le Hu, Kai-Yan Feng, Lei Gu and Xiao-Jun LiuInformation of protein quaternary structure can help to understand the biological functions of proteins. Because wet-lab experiments are both time-consuming and costly, we adopt a novel computational approach to assign proteins into 10 kinds of quaternary structures. By coding each protein using its biochemical and physicochemical properties, feature selection was carried out using Incremental Feature Selection (IFS Read More
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Prediction of Optimal pH and Temperature of Cellulases Using Neural Network
Authors: Shao-Min Yan and Guang WuCellulase is an important enzyme widely used in various industries, and now in fermentation of biomass into biofuels. Enzymatic function of cellulase is closely related to pH, temperature, substrate concentration, etc. For newly found cellulase, it would be more cost-effective to predict its optimal pH and temperature before conducting the costly experiments. In this study, we used a 20-2 feedforward backpropagation neur Read More
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CRYSpred: Accurate Sequence-Based Protein Crystallization Propensity Prediction Using Sequence-Derived Structural Characteristics
Authors: MarcinJ. Mizianty snm and Lukasz A. KurganRelatively low success rates of X-ray crystallography, which is the most popular method for solving proteins structures, motivate development of novel methods that support selection of tractable protein targets. This aspect is particularly important in the context of the current structural genomics efforts that allow for a certain degree of flexibility in the target selection. We propose CRYSpred, a novel in-silico crystallization Read More
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RSARF: Prediction of Residue Solvent Accessibility from Protein Sequence Using Random Forest Method
Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction. In this work, we propose a random forest method, RSARF, to predict residue accessible surface area Read More
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SCYPPred: A Web-Based Predictor of SNPs for Human Cytochrome P450
Authors: Li Li, Dong-Qing Wei, Jing-Fang Wang and Kuo-Chen ChouHuman cytochrome P450(CYP 450) enzymes mediate over 60% of the phase I-dependent metabolism of clinical drugs. They are also known for the polymorphism functions that have significant impacts on the enzyme activities. In this study, a web-server called SCYPPred was developed for predicting human cytochrome P450 SNPs (Single Nucleotide Polymorphisms) based on the SVM flanking sequence method; SCYPPred ca Read More
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Prostate Cancer with Variants in CYP17 and UGT2B17 Genes: A Meta-Analysis
Authors: Lai Cai, Wei Huang and Kuo-Chen ChouBoth CYP17 and UGT2B17 are suggested to be potential risk factors of prostate cancer (PCa). To date, many studies have evaluated the relationship between CYP17 T-34C and UGT2B17 Del polymorphisms and Prostate cancer with conflicting results. Here, we performed comprehensive meta-analyses of over 25 studies, including results from about 17,000 subjects on the association of CYP17 T-34C and UGT2B17 Del polymorp Read More
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A Novel Sequence-Based Method for Phosphorylation Site Prediction with Feature Selection and Analysis
Authors: Zhi-Song He, Xiao-He Shi, Xiang-Ying Kong, Yu-Bei Zhu and Kuo-Chen ChouPhosphorylation is one of the most important post-translational modifications, and the identification of protein phosphorylation sites is particularly important for studying disease diagnosis. However, experimental detection of phosphorylation sites is labor intensive. It would be beneficial if computational methods are available to provide an extra reference for the phosphorylation sites. Here we developed a novel sequence- Read More
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Nucleosome Positioning Based on the Sequence Word Composition
Authors: Xian-Fu Yi, Zhi-Song He, Kuo-Chen Chou and Xiang-Yin KongThe DNA of all eukaryotic organisms is packaged into nucleosomes (a basic repeating unit of chromatin). A nucleosome consists of histone octamer wrapped by core DNA and linker histone H1 associated with linker DNA. It has profound effects on all DNA-dependent processes by affecting sequence accessibility. Understanding the factors that influence nucleosome positioning has great help to the study of genomic control Read More
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A Nearest Neighbor Algorithm Based Predictor for the Prediction of Enzyme - Small Molecule Interaction
Authors: Le-Le Hu, Zhi-Song He, Xiao-He Shi, Xiang-Ying Kong, Hai-Peng Li and Wen-Cong LuIt is of great use to find out and clear up the interactions between enzymes and small molecules, for understanding the molecular and cellular functions of organisms. In this study, we developed a novel method for the prediction of enzyme-small molecules interactions based on machine learning approach. The biochemical and physicochemical description of proteins and the functional group composition of small molecule Read More
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Analysis of Metabolic Pathway Using Hybrid Properties
Authors: Lei Chen, Yu-Dong Cai, Xiao-He Shi and Tao HuangGiven a compounds-forming system, i.e., a system consisting of some compounds and their relationship, can it form a biologically meaningful pathway? It is a fundamental problem in systems biology. Nowadays, a lot of information on different organisms, at both genetic and metabolic levels, has been collected and stored in some specific databases. Based on these data, it is feasible to address such an essential proble Read More
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Prediction of the Functional Roles of Small Molecules in Lipid Metabolism Based on Ensemble Learning
Authors: Chun-Rong Peng, Wen-Cong Lu, Bing Niu, Ya-Jun Li and Le-Le HuAs many diseases like high cholesterol are referred to lipid metabolism, studying the lipid metabolic pathway has a positive effect on finding the knowledge about interactions between different elements within high complex living systems. Here, we employed a typical ensemble learning method, Bagging learner, to study and predict the possible sub lipid metabolic pathway of small molecules based on physical and chemi Read More
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Selection of Reprogramming Factors of Induced Pluripotent Stem Cells Based on the Protein Interaction Network and Functional Profiles
Authors: Tao Huang, Yu-Dong Cai, Lei Chen, Le-Le Hu, Xiang-Yin Kong, Yi-Xue Li and Kuo-Chen ChouInduced pluripotent stem cells have displayed great potential in disease investigation and drug development applications. However, selection of reprogramming factors in each cell type or disease state is both expensive and time consuming. To deal with this kind of situation, a fast computational framework was developed by optimize the reprogramming factors via the protein interaction network and gene function Read More
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Improved Candidate Biomarker Detection Based on Mass Spectrometry Data Using the Hilbert-Huang Transform
Mass spectrometry biomarker discovery may assist patient's diagnosis in time and realize the characteristics of new diseases. Our previous work built a preprocess method called HHTmass which is capable of removing noise, but HHTmass only a proof of principle to be peak detectable and did not tested for peak reappearance rate and used on medical data. We developed a modified version of biomarker discovery method call Read More
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Volumes & issues
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Volume 32 (2025)
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Volume 31 (2024)
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Volume 30 (2023)
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Volume 29 (2022)
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Volume 28 (2021)
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Volume 27 (2020)
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Volume 26 (2019)
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Volume 25 (2018)
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Volume 24 (2017)
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Volume 23 (2016)
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Volume 22 (2015)
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Volume 21 (2014)
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Volume 20 (2013)
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Volume 19 (2012)
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Volume 18 (2011)
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Volume 17 (2010)
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Volume 16 (2009)
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Volume 15 (2008)
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Volume 14 (2007)
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Volume 13 (2006)
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Volume 12 (2005)
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Volume 11 (2004)
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Volume 10 (2003)
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Volume 9 (2002)
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Volume 8 (2001)
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