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Selective targeting of indel-inferred differences in spatial structures of homologous proteins

Journal of bioinformatics and computational biology 2006年第2期4卷 403-14页

作者： Yvonne Y Li Steven J M Jones Artem Cherkasov CIHR/MSFHR Strategic Training Program in Bioinformatics University of British Columbia 570 West 7th Avenue Vancouver British Columbia V5Z 4S6 Canada. yli@bcgsc.ca

The eukaryotic pathogen Leishmania donovani possesses a housekeeping protein Elongation-Factor-1alpha (EF-1alpha) which has been found to be unexpectedly involved in the pathogen's virulence. Because it is associated with virulence and essential for cell survival, this protein is an attractive choice for drug targeting; however, its sequence is highly similar (> 80% sequence identity) to that of its human homolog, rendering it a risky choice for a drug target. The chief difference between these two proteins has been found to be a 12 amino acid sequence present in human EF-1alpha but absent from leishmania EF-1alpha. Furthermore, it has been shown that this 12 amino acid insert in the human sequence corresponds to a hairpin loop on the surface of the protein. In this study, we searched for those spatial features in leishmania EF-1alpha that are impacted or obscured by the extra hairpin loop in the human counterpart. We have also conducted a large-scale in silico screening for small molecules that could plausibly bind to these protein features. While experimental evidence is required to verify our results, our findings thus far appear to support this approach as a new strategy for the development of antagonists against pathogenic targets having close human homologs.

关键词： Indels protein length polymorphism drug design drug targets virtual screening

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Refinement of NMR-determined protein structures with database derived distance constraints

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Journal of bioinformatics and Computational Biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database Structural bioinformatics

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A kernel method for MicroRNA target prediction using sensible data and position-based features

A kernel method for MicroRNA target prediction using sensibl...

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2005 IEEE Symposium on Computational Intelligence in bioinformatics and Computational Biology, CIBCB '05

作者： Kim, Sung-Kyu Nam, Jin-Wu Lee, Wha-Jin Zhang, Byoung-Tak Graduate Program in Bioinformatics School of Computer Science and Engineering Seoul National University Seoul 151-744 Korea Republic of

ISBN: (纸本)0780393872

MicroRNAs (miRNAs) are small endogenous RNAs of ∼22nt that act as direct post-transcriptional regulators in animals and plants. MicroRNAs generally perform a function by binding to the complementary site on the 3' untranslated region of Its target gene and especially the Smers on the 5' part of miRNA seems important as a seed. Computational methods for miRNA target prediction have been focusing on this seed region, but recent researches revealed that the specificity of the seed region may be sharply decreased even by a point mutation. In this paper, we present a kernel method for miRNA target prediction in animals, which improves the prediction performance with biologically sensible data and position-based features reflecting the way of miRNA:mRNA pairing mechanism. In building a training dataset, we choose experimentally verified data only to improve the quality of dataset by excluding randomly synthesized one and consequently to make the result of learning valid. We use sensitivity, specificity, and area under ROC curve as performance measures of our algorithm and compare the results of various dataset configurations. The overall results were 92.1% in sensitivity, 83.3% in specificity, and 0.931 in area under ROC curve. With position-based features, an increase of 3.3% in sensitivity and 1.6% in specificity were observed. In the feature selection experiment to investigate the role of individual position-based features, the result suggests that pairing at positions 4, 5, 6 of the seed part is of greater importance than the other positions together with the general stability of the seed part. © 2005 IEEE.

关键词： RNA

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Identifying differentially expressed genes from probe level intensities in longitudinal affymetrix microarray experiments

Identifying differentially expressed genes from probe level ...

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2005 IEEE/SP 13th Workshop on Statistical Signal Processing

作者： Zhu, Dongxiao Hero, Alfred O. Bioinformatics Program University of Michigan Ann Arbor MI 48105 Departments of EECS Biomedical Engineering and Statistics University of Michigan Ann Arbor MI 48105

ISBN: (纸本)0780394046

Identifying differentially expressed genes over different physiological/genetic conditions is fundamental to microarray data analysis. Most of the traditional approaches do not consider the inherent correlation structure of the repeated measurements, and hence tend to give rise to inflated statistical significance of estimated treatment effects. We propose including dependency between time points and probes into a mixed linear model for gene microarray data. The approach can be viewed as an extension to existing linear model based approaches such as ANOVA, Li-Wong's Model and the linear mixed effect model proposed by Chu et al. Model fitting diagnostics demonstrate significant performance improvement for longitudinal probe level data. We illustrate our approach for an aging experiment in a mouse model for quantifying retinal gene expression. © 2005 IEEE.

关键词： Gene expression

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Network constrained clustering for gene microarray data

Network constrained clustering for gene microarray data

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Zhu, Dongxiao Hero, Alfred O. Bioinformatics Program University of Michigan Ann Arbor MI 48105 United States Departments of EECS Biomedical Engineering and Statistics University of Michigan Ann Arbor MI 48105 United States

ISBN: (纸本)0780388747

Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways. Based on the construction of co-expression networks that consists of both significantly linear and non-linear gene associations together with controlled biological and statistical significance, we can make accurate discovery of many transitively co-expressed genes and similarly co-expressed genes. Our approach tends to group functionally related genes into a tight cluster. We illustrate our approach and compare it to the traditional clustering approaches on a retinal gene expression dataset. The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://***/~zhud/***/. © 2005 IEEE.

关键词： Genetic engineering

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Gene co-expression network discovery with controlled statistical and biological significance

Gene co-expression network discovery with controlled statist...

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Zhu, Dongxiao Hero, Alfred O. Bioinformatics Program University of Michigan Ann Arbor MI 48105 United States Depatments of EECS Biomedical Engineering and Statistics University of Michigan Ann Arbor MI 48105 United States

ISBN: (纸本)0780388747

Many biological functions are executed as a module of co-expressed genes which can be conveniently viewed as a co-expression network. Genes are network vertices and significant pairwise co-expressions are network edges. Traditional network discovery methods controls either statistical significance or biological significance, but not both. We have designed and implemented a two-stage algorithm that controls both statistical significance (False Discovery Rate, FDR) and biological significance (Minimium Acceptable Strength, MAS) of the discovered network. Based on the estimation of pairwise gene profile correlation, the algorithm provides an initial network discovery that controls only FDR, which is then followed by a second network discovery which controls both FDR and MAS. We illustrate the algorithm for discovery of co-expression networks for yeast galactose metabolism with controlled FDR and MAS. © 2005 IEEE.

关键词： Genes

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Comparative sequence analysis to identify functional elements for functional genomics

Comparative sequence analysis to identify functional element...

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2005 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS'05

作者： Budak, Hikmet Yavuz, Yavuz F. Sabanci University Biological Science and Bioengineering Program Orhanli 34956 Tuzla-Istanbul Turkey University of Nebraska-Lincoln Center for Biotechnology Bioinformatics Core Facility E166 Beadle Center Lincoln NE 68583 United States

ISBN: (纸本)9781932415834

Comparative sequence analysis is a powerful approach to identify functional elements for functional genomics. For gene prediction, this would mean whether given genomic sequences exhibit significant similarity to some arbitrary region in the genome sequence of an evolutionary related organism or not. Three gene prediction methods were examined to see how they perform on randomly chosen sequences. The focus was on examining the methods rather than data analysis. Thus, the dataset was selected in a flexible manner and may be biased. Our analysis of methods indicated that there are not many differences between recent version of *** and GENSCAN in terms of the algorithm used. Both programs use duration HMM and can predict genes and exons on both DNA strands simultaneously. MZEF, on the other hand, uses completely different approach. It employs quadratic discriminant function to distinguish between coding and noncoding regions. The analysis showed that the new generation of programs has substantially better results than the programs analyzed in previous studies. Specifically, *** and GENSCAN performed relatively better than MZEF.

关键词： Genes

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A grid infrastructure for text mining of full text articles and creation of a knowledge base of gene relations

A grid infrastructure for text mining of full text articles ...

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6th International Symposium on Biological and Medical Data Analysis, ISBMDA 2005

作者： Natarajan, Jeyakumar Mulay, Niranjan DeSesa, Catherine Hack, Catherine J. Dubitzky, Werner Bremer, Eric G. Bioinformatics Research Group University of Ulster United Kingdom United Devices Inc. Austin TX United States Brain Tumor Research Program Northwestern University Children's Memorial Hospital Chicago IL United States

ISBN: (纸本)3540296743

We demonstrate the application of a grid infrastructure for conducting text mining over distributed data and computational resources. The approach is based on using LexiQuest Mine, a text mining workbench, in a grid computing environment. We describe our architecture and approach and provide an illustrative example of mining full-text journal articles to create a knowledge base of gene relations. The number of patterns found increased from 0.74 per full-text articles from a corpus of 1000 articles to 0.83 when the corpus contained 5000 articles. However, it was also shown that mining a corpus of 5000 full-text articles took 26 hours on a single computer, whilst the process was completed in less than 2.5 hours on a grid comprising of 20 computers. Thus whilst increasing the size of the corpus improved the efficiency of the text-mining process, a grid infrastructure was required to complete the task in a timely manner. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Distributed computer systems

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Computational design of combinatorial peptide library for modulating protein-protein interactions

Computational design of combinatorial peptide library for mo...

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10th Pacific Symposium on Biocomputing, PSB 2005

作者： Li, Xiang Liang, Jie Graduate Program in Bioinformatics SEO University of Illinois 851 S. Morgan Street Chicago IL United States Department of Bioengineering SEO University of Illinois 851 S. Morgan Street Chicago IL United States

ISBN: (纸本)9812560467

Screening phage-displayed combinatorial peptide library is an effective approach for discovery of peptide modulators for protein-protein interactions. However, as peptide length increases, the chance of finding active peptides in a finite size library diminishes. To increase the likelihood of finding peptides that bind to a protein, we develop statistical potential for computational construction of biased combinatorial antibody-like peptide libraries. Based on the alpha shapes of antibody- antigen complexes, we developed an empirical pair potential for antigen-antibody interactions that depends on local packing. We validate this potential and show that it can successfully discriminate the native interface peptides from a simulated library of 10,000 random peptides for 34 antigen-antibody complexes. In addition, we show that it can successfully recognize the native binding surface patch among all possible surface patches taken from either the antibody or the antigen for seven antibody-antigen protein complexes contained in the CAPRI (Critical Assessment of Predicted Interactions) dataset. We then develop a Weighted Amino Acid Residue sequence Generator (WAARG) for design of biased peptide library. When compared with a random peptide library, WAARG libraries contain more native-like binding peptides at a significantly smaller size. Our method can be used to construct peptide library for screening of antibody variants with improved specificity and affinity to a target antigen. It can also be used for screening of antibody-like antagonist peptides modulating other protein-protein interactions.

关键词： Peptides

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Multiple alignment of continuous time series

Multiple alignment of continuous time series

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18th Annual Conference on Neural Information Processing Systems, NIPS 2004

作者： Listgarten, Jennifer Neal, Radford M. Roweis, Sam T. Emili, Andrew Department of Computer Science University of Toronto Toronto ON M5S 3G4 Canada Banting and Best Department of Medical Research Program in Proteomics and Bioinformatics University of Toronto Toronto ON M5S 3G4 Canada

ISBN: (纸本)0262195348

Multiple realizations of continuous-valued time series from a stochastic process often contain systematic variations in rate and amplitude. To leverage the information contained in such noisy replicate sets, we need to align them in an appropriate way (for example, to allow the data to be properly combined by adaptive averaging). We present the Continuous Profile Model (CPM), a generative model in which each observed time series is a non-uniformly subsampled version of a single latent trace, to which local rescaling and additive noise are applied. After unsupervised training, the learned trace represents a canonical, high resolution fusion of all the replicates. As well, an alignment in time and scale of each observation to this trace can be found by inference in the model. We apply CPM to successfully align speech signals from multiple speakers and sets of Liquid Chromatography-Mass Spectrometry proteomic data.

关键词： Time series

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