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At least 1,711 records · Page 95Linked to original sources
PubMed2006

Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization.

Predicting subcellular localization of human proteins is a challenging problem, especially when unknown query proteins do not have significant homology to proteins of known subcellular locations and when more locations need to be covered. To tackle the challenge, protein samples are expressed by hybridizing the gene ontology (GO) database and amphiphilic pseudo amino acid composition (PseAA). Based on such a representation frame, a novel ensemble classifier, called "Hum-PLoc", was developed by fusing many basic individual classifiers through a voting system. The "engine" of these basic classifiers was operated by the KNN (K-nearest neighbor) rule. As a demonstration, tests were performed with the ensemble classifier for human proteins among the following 12 locations: (1) centriole; (2) cytoplasm; (3) cytoskeleton; (4) endoplasmic reticulum; (5) extracell; (6) Golgi apparatus; (7) lysosome; (8) microsome; (9) mitochondrion; (10) nucleus; (11) peroxisome; (12) plasma membrane. To get rid of redundancy and homology bias, none of the proteins investigated here had > or = 25% sequence identity to any other in a same subcellular location. The overall success rates thus obtained via the jackknife cross-validation test and independent dataset test were 81.1% and 85.0%, respectively, which are more than 50% higher than those obtained by the other existing methods on the same stringent datasets. Furthermore, an incisive and compelling analysis was given to elucidate that the overwhelmingly high success rate obtained by the new predictor is by no means due to a trivial utilization of the GO annotations. This is because, for those proteins with "subcellular location unknown" annotation in Swiss-Prot database, most (more than 99%) of their corresponding GO numbers in GO database are also annotated with "cellular component unknown". The information and clues for predicting subcellular locations of proteins are actually buried into a series of tedious GO numbers, just like they are buried into a pile of complicated amino acid sequences although with a different manner and "depth". To dig out the knowledge about their locations, a sophisticated operation engine is needed. And the current predictor is one of these kinds, and has proved to be a very powerful one. The Hum-PLoc classifier is available as a web-server at http://202.120.37.186/bioinf/hum.

Algorithms↗
PubMed2006

SELDI-TOF mass spectra: a view on sources of variation.

Adequate interpretation of mass spectrometry data can yield valuable biomarkers. However, spectrum interpretation is a complicated task. This paper reviews the various factors that determine a sample's spectrum and demonstrates the role of these factors in the interpretation process. We derive a simulation model that adequately predicts the expected spectrum based on known sample content and, in the reverse mode, obtain an analysis model that adequately fits an observed spectrum based on the hypothesized sources of variation.

Algorithms↗
PubMed2005

Functional proteome of bones in rats with osteoporosis following ovariectomy.

Osteoporosis is a chronic condition chiefly affecting postmenopausal women, in whom the skeleton loses a significant percentage of its mineralized mass and mechanical resiliency, thereby becoming prone to fracture. Although the effect of the loss of estrogen on bone metabolism has been documented, its mechanism is still poorly understood. In the present proteomic study, we characterized the effect of estrogen deficiency on protein expression in rat bones. Using two-dimensional gel electrophoresis, mass spectrometry and rat protein database, we successfully identified three distinctly changed proteins named thioredoxin peroxidase 1, myosin light polypeptide 2 and ubiquitin-conjugating enzyme E2-17 kD, among which ubiquitin-conjugating enzyme E2-17 kD has been documented to be an estrogen-related protein, but the other two are first reported to be osteoporosis-related proteins in the current study. These results provide valuable experimental evidences for the elucidation of the molecular mechanism of osteoporosis related to the loss of estrogen.

Animals↗
PubMed2004

Genome information resources - developments at Ensembl.

Genome information resources such as Ensembl manage and present DNA sequence and annotation arising from genome projects. Recent developments in the Ensembl system include access to inter-species comparisons at both the genomic and protein sequence levels and improvements to the display of polymorphism data. Users can now display their own data in the context of other annotation. In addition, a fast and flexible data retrieval system, EnsMart, has been developed.

Animals↗
PubMed2005

Protein identification using 2D-LC-MS/MS.

Multidimensional liquid chromatography techniques have been coupled to tandem mass spectrometry to provide a robust method to identify proteins in complex mixtures. Data acquisition is interfaced directly with search algorithms for identification through cross-correlation with databases. This review describes the most recent advances in methodologies for protein identification by mass spectrometry and describes the limitations of the application of the technologies.

Algorithms↗
PubMed2002

Reliability of transmembrane predictions in whole-genome data.

Transmembrane prediction methods are generally benchmarked on a set of proteins with experimentally verified topology. We have investigated if the accuracy measured on such datasets can be expected in an unbiased genomic analysis, or if there is a bias towards 'easily predictable' proteins in the benchmark datasets. As a measurement of accuracy, the concordance of the results from five different prediction methods was used (TMHMM, PHD, HMMTOP, MEMSAT, and TOPPRED). The benchmark dataset showed significantly higher levels (up to five times) of agreement between different methods than in 10 tested genomes. We have also analyzed which programs are most prone to make mispredictions by measuring the frequency of one-out-of-five disagreeing predictions.

Algorithms↗
PubMed2003

Interdisciplinary sciences in the 21st century.

With the advance of genome projects the search for the order of cellular processes has become a realistic goal. Yet this order is hidden behind a screen of data, which obscures the actual connectivity of genes and proteins. In order to expose the true nature of these networks it becomes necessary to apply in silico analysis based on certain parallels between biological and complex technical systems. These efforts far exceed current notions of computational biology.

Computational Biology↗
PubMed2006

Topological properties of protein-protein and metabolic interaction networks of Drosophila melanogaster.

The underlying principle governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. A key feature of the scale-free architecture is the vitality of the most connected nodes (hubs). The major objective of this article was to analyze the protein-protein and metabolic interaction networks of Drosophila melanogaster by considering the architectural patterns and the consequence of removal of hubs on the topological parameter of the two interaction systems. Analysis showed that both interaction networks follow a scale-free model, establishing the fact that most real world networks, from varied situations, conform to the small world pattern. The average path length showed a two-fold and a three-fold increase (changing from 9.42 to 20.93 and from 5.29 to 17.75, respectively) for the protein-protein and metabolic interaction networks, respectively, due to the deletion of hubs. On the contrary, the arbitrary elimination of nodes did not show any remarkable disparity in the topological parameter of the protein-protein and metabolic interaction networks (average path length: 9.42+/-0.02 and 5.27+/-0.01, respectively). This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks.

Algorithms↗
PubMed2006

Central limit theorem as an approximation for intensity-based scoring function.

In this paper, we present an intensity-based probability function to identify peptides from tandem mass spectra and amino acid sequence databases. The function is an approximation to the central limiting theorem, and it explicitly depends on the cumulative product ion intensities, number of product ions of a peptide, and expectation value of the cumulative intensity. We compare the results of database searches using the new scoring function and scoring functions from earlier algorithms, which implement hypergeometric probability, Poisson's model, and cross-correlation scores. For a standard protein mixture (tandem mass spectra generated from the mixture of five known proteins), we generate receiver operating curves with all scoring schemes. The receiver operating curves show that the shared peaks count-based probability methods (like Poisson and hypergeometric models) are the most specific for matching high-quality tandem mass spectra. The intensity-based (central limit model) and intensity-modeled (cross-correlation) methods are more sensitive when matching low-quality tandem mass spectra, where the number of shared peaks is insufficient to correctly identify a peptide. Cross-correlation methods show a small advantage over the intensity-based probability method.

Algorithms↗
PubMedDate not supplied

Detection and identification of protein interactions of S100 proteins by ProteinChip technology.

The aim of this work was to establish an approach for identification of protein interactions. This assay used an anti-S100A8 antibody coupled on beads and incubated with cell extract. The bead eluates were analyzed using ProteinChip technology and subsequently subjected to an appropriate digestion. Molecular masses of digestion fragments were determined by SELDI-MS, and database analysis revealed S100A10 as interacting protein. This result was confirmed by co-immunoprecipitation and immunocapturing. Using S100A10 as new bait, a specific interaction with S100A7 was detectable.

Annexin A2↗
PubMed2000

Patent protection for protein structures and databases.

Patent protection is available for certain inventions in the field of structural genomics. A review of the patent application procedure is provided, and patentable aspects of protein structural information under US law are discussed. Strategic and international factors to consider when seeking patent protection for an invention also are presented.

Computational Biology↗
PubMed2002

The structure of the protein universe and genome evolution.

Despite the practically unlimited number of possible protein sequences, the number of basic shapes in which proteins fold seems not only to be finite, but also to be relatively small, with probably no more than 10,000 folds in existence. Moreover, the distribution of proteins among these folds is highly non-homogeneous -- some folds and superfamilies are extremely abundant, but most are rare. Protein folds and families encoded in diverse genomes show similar size distributions with notable mathematical properties, which also extend to the number of connections between domains in multidomain proteins. All these distributions follow asymptotic power laws, such as have been identified in a wide variety of biological and physical systems, and which are typically associated with scale-free networks. These findings suggest that genome evolution is driven by extremely general mechanisms based on the preferential attachment principle.

Databases, Protein↗
PubMed2002

Analyzing yeast protein-protein interaction data obtained from different sources.

High-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, continue to produce vast amounts of data that may be exploited to infer protein function and regulation. As this article went to press, the pool of all published interaction information on Saccharomyces cerevisiae was 15,143 interactions among 4,825 proteins, and power-law scaling supports an estimate of 20,000 specific protein interactions. To investigate the biases, overlaps, and complementarities among these data, we have carried out an analysis of two high-throughput mass spectrometry (HMS)-based protein interaction data sets from budding yeast, comparing them to each other and to other interaction data sets. Our analysis reveals 198 interactions among 222 proteins common to both data sets, many of which reflect large multiprotein complexes. It also indicates that a "spoke" model that directly pairs bait proteins with associated proteins is roughly threefold more accurate than a "matrix" model that connects all proteins. In addition, we identify a large, previously unsuspected nucleolar complex of 148 proteins, including 39 proteins of unknown function. Our results indicate that existing large-scale protein interaction data sets are nonsaturating and that integrating many different experimental data sets yields a clearer biological view than any single method alone.

Chromatography, Liquid↗
PubMed2004

Automated yeast two-hybrid screening for nuclear receptor-interacting proteins.

High throughput analysis of protein-protein interactions is an important sector of hypothesis-generating research. Using an improved and automated version of the yeast two-hybrid system, we completed a large interaction screening project with a focus on nuclear receptors and their cofactors. A total of 425 independent yeast two-hybrid cDNA library screens resulted in 6425 potential interacting protein fragments involved in 1613 different interaction pairs. We show that simple statistical parameters can be used to narrow down the data set to a high confidence set of 377 interaction pairs where validated interactions are enriched to 61% of all pairs. Within the high confidence set, there are 64 novel proteins potentially binding to nuclear receptors or their cofactors. We discuss several examples of high interest, and we expect that communication of this huge data set will help to complement our knowledge of the protein interaction repertoire of this family of transcription factors and instigate the characterization of the various novel candidate interactors.

Automation↗
PubMed2006

Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL.

Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is 'enriched' for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.

Data Interpretation, Statistical↗