Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Computational proteomics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39Linked to original sources
PubMed2006

Proteome resolution by two-dimensional gel electrophoresis varies with the commercial source of IPG strips.

By facilitating reproducible first dimension separations, commercial immobilized pH gradient (IPG) strips enable high throughput and high-resolution proteomic analyses using two-dimensional gel electrophoresis (2DE). Amersham, Biorad, Invitrogen, and Sigma all market linear pH 3-10 IPG strips. We have applied optimized 2DE protocols with both membrane and soluble brain protein extracts to critically evaluate all four products. Resolved protein spots were quantitatively evaluated after carrying out these protocols using IPG strips from the four companies. Biorad and Amersham IPG strips resolved a high number of membrane and soluble proteins, respectively. Furthermore, Amersham IPG strips eluted the largest amount of protein into the second dimension gels and had the most protein remaining in the strip after 2DE. Biorad and Amersham IPG strips maintained a consistent linear pH 3-10 gradient, whereas those from Invitrogen appeared nonlinear or "compressed" within the central pH region. The gradient range within Sigma IPG strips appeared to be slightly less than pH 3-10, due to one extended pH unit within the gradient. Overall, all four commercially available IPG strips have the ability to resolve both membrane and soluble brain proteomes. The difference is that Amersham and Biorad do so more consistently and with better spot resolution. It appears that the physical/chemical nature of commercially available IPG strips can vary considerably, leading to marked differences in subsequent protein resolution in 2DE. These differences likely reflect variations in the uptake of proteins into the strips, and differences in the focusing and elution of proteins from the first to the second dimension. These differences would appear, in part, to underlie some inter-lab variations in the effective resolution of proteomes.

Animals↗
PubMed2002

Application of proteomics in the search for novel proteins associated with the anti-cancer effect of the synthetic cyclin-dependent kinases inhibitor, bohemine.

The purpose of this study was to use the proteomics approach, which is based on high resolution two-dimensional electrophoresis coupled with multivariate correspondence analysis and mass spectrometry, to classify objectively the biochemical basis of the anti-cancer activity of the synthetic cyclin-dependent kinase inhibitor, bohemine (BOH). The changes in the cell cycle and corresponding protein composition of the A549 human lung adenocarcinoma cell line after treatment with BOH were evaluated and proteins differentially expressed in the BOH treated A549 cells, compared to the untreated A549 counterparts, were selected. Thirteen of these candidate proteins associated with the drug effects in vitro were identified by mass spectrometry. Many of these proteins fall into one of three functional categories: i) metabolic pathways (glycolysis, nucleic acid synthesis and NADPH production), ii) stress response and protein folding, and iii) cytoskeleton and exocytosis. Changes in protein expression patterns corresponded to a higher resistance of A549 lung carcinoma cells to BOH when compared to the CEM leukaemia cell line. These protein changes reflect a fine balance of the resistant versus the susceptible phenotype in response to the drug. Since BOH is a selective cyclin-dependent kinase inhibitor, changes in the protein expression pattern can be more generally associated with cell cycle regulation as evidenced by inhibition of cell cycling in A549 cells. Our conclusions further underline the importance of cell cycle control in both the cellular signalling and metabolic pathways.

Antineoplastic Agents↗
PubMed2003

Proteome analysis identified maspin as a special feature of papillary thyroid carcinoma.

This study aimed at investigating new mechanisms of carcinogenesis in thyroid cancer at the molecular level and at finding potential protein markers involved in the initiation of the different histological subtypes. For this, we performed differential proteome analysis on primary cultured thyrocytes (PT) and transformed thyrocytes (TT) derived from 238Pu alpha-particle irradiation using 2-dimensional electrophoresis (2-DE) and peptide mass fingerprinting (PMF) with matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF MS). Image analysis showed that one protein was very strongly expressed in TT; 55 proteins were weaker, different in intensity, including 26 spots that were increased in PT, and 29 spots were decreased. The hot spot was identified as maspin, a unique member of the serpin family considered to be a class II tumor suppressor gene. To clarify the role of maspin in thyroid carcinogenesis we searched for protein expression in 20 normal (tumor-free) tissues, as well as in 20 follicular adenomas (FAD), 20 papillary carcinomas (PTC), 20 follicular carcinomas (FTC), 20 poorly differentiated carcinomas (PDTC), and 20 undifferentiated carcinomas (UTC). Maspin protein expression was detectable in none of the cases of normal tumor-free thyroid tissue, nor in FAD, FTC, PDTC and UTC. In contrast 14 of 20 PTC (70%) showed a moderate or strong cytoplasmic staining; 4 of these 14 cases had a moderate cytoplasmic and nuclear staining. In conclusion, we hypothesize that maspin protein expression is a special feature in the cascade of PTC genesis and that the way of initiating PTC is different from other thyroid carcinoma types.

Carcinoma, Papillary↗
PubMed2005

Proteome analysis of human hepatocellular carcinoma tissues by two-dimensional difference gel electrophoresis and mass spectrometry.

Proteome analysis of human hepatocellular carcinoma tissues was conducted using two-dimensional difference gel electrophoresis coupled with mass spectrometry. Paired samples from the normal and tumor region of resected human liver were labeled with Cy3 and Cy5, respectively while the pooled standard sample was labeled with Cy2. After analysis by the DeCyder software, protein spots that exhibited at least a two-fold difference in intensity were excised for in-gel tryptic digestion and matrix-assisted laser desorption/ionization-time of flight mass spectrometry. A total of 6 and 42 proteins were successfully identified from the well- and poorly-differentiated samples, respectively. The majority of these proteins are related to detoxification/oxidative stress and metabolism. Three down-regulated metabolic enzymes, methionine adenosyltransferase, glycine N-methyltransferase, and betaine-homocysteine S-methyltransferase that are involved in the methylation cycle in the liver are of special interest. Their expression levels, especially, methionine adenosyltransferase, seemed to have a major influence on the level of S-adenosylmethionine (AdoMet), a vital intermediate metabolite required for the proper functioning of the liver. Recent work has shown that chronic deficiency in AdoMet in the liver results in spontaneous development of steatohepatitis and hepatocellular carcinoma, and hence the down-regulation of hepatic methionine adenosyltransferase in our hepatocellular carcinoma samples is in line with this observation. Moreover, when a comparison is made between the differentially expressed proteins from our human hepatocellular carcinoma samples and from the liver tissues of knockout mice deficient in methionine adenosyltransferase, there is a fairly good correlation between them.

Carcinoma, Hepatocellular↗
PubMedDate not supplied

A comparative study of proteomics maps using graph theoretical biodescriptors.

This paper reports the development of new methods for mathematical characterization of effects of different toxic agents on the cellular proteome. We describe numerical characterization of proteomics maps based on mathematical invariants. A graph is first associated with a proteomics map by considering partial ordering of spots on 2-D gels by ordering proteins with respect to the mass and the charge, the two properties by which proteins are separated. The graph is then embedded over the map, and several graph theoretical invariants have been constructed. In particular we consider invariants that can be extracted from the Euclidean distance-adjacency matrix of the embedded graph, in which only Euclidean distances between adjacent vertices of a graph are considered. The approach is illustrated using proteomics patterns of normal liver cells of rats and those derived from liver cells of animals exposed to four peroxisome proliferators. In contrast to direct comparison of spot abundance our approach incorporates information on spots locations. The difference between the two approaches is that in the first case only changes in abundances are considered as a measure of perturbation of the proteome map, but in the second case not only the charge but also the mass of proteins are used for ordering protein spots.

Animals↗
PubMed2002

Subcellular localization of the yeast proteome.

Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome-wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass approximately 5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability--a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu.

Algorithms↗
PubMed2005

Proteomic profiling of recombinant Escherichia coli in high-cell-density fermentations for improved production of an antibody fragment biopharmaceutical.

By using two-dimensional polyacrylamide gel electrophoresis, a proteomic analysis over time was conducted with high-cell-density, industrial, phosphate-limited Escherichia coli fermentations at the 10-liter scale. During production, a recombinant, humanized antibody fragment was secreted and assembled in a soluble form in the periplasm. E. coli protein changes associated with culture conditions were distinguished from protein changes associated with heterologous protein expression. Protein spots were monitored quantitatively and qualitatively. Differentially expressed proteins were quantitatively assessed by using a t-test method with a 1% false discovery rate as a significance criterion. As determined by this criterion, 81 protein spots changed significantly between 14 and 72 h (final time) of the control fermentations (vector only). Qualitative (on-off) comparisons indicated that 20 more protein spots were present only at 14 or 72 h in the control fermentations. These changes reflected physiological responses to the culture conditions. In control and production fermentations at 72 h, 25 protein spots were significantly differentially expressed. In addition, 19 protein spots were present only in control or production fermentations at this time. The quantitative and qualitative changes were attributable to overexpression of recombinant protein. The physiological changes observed during the fermentations included the up-regulation of phosphate starvation proteins and the down-regulation of ribosomal proteins and nucleotide biosynthesis proteins. Synthesis of the stress protein phage shock protein A (PspA) was strongly correlated with synthesis of a recombinant product. This suggested that manipulation of PspA levels might improve the soluble recombinant protein yield in the periplasm for this bioprocess. Indeed, controlled coexpression of PspA during production led to a moderate, but statistically significant, improvement in the yield.

Bacterial Proteins↗
PubMed2005

Multidimensional protein identification technology: current status and future prospects.

Protein profiling using high-throughput tandem mass spectrometry has become a powerful method for analyzing changes in global protein expression patterns in cells and tissues as a function of developmental, physiologic and disease processes. This review summarizes the utility and practical application of multidimensional protein identification technology as a platform for comprehensive proteomic profiling of complex biologic samples. The strengths and potential problems and limitations associated with this powerful technology are discussed, with an emphasis placed on one of the biggest challenges currently facing large-scale expression profiling projects -- namely, data analysis. Complementary bioinformatic computational data mining strategies, such as clustering, functional annotation and statistical inference, are also discussed as these are increasingly necessary for interpreting the results of global proteomic profiling studies.

Animals↗
PubMed2003

JVirGel: Calculation of virtual two-dimensional protein gels.

We developed JVirGel, a collection of tools for the simulation and analysis of proteomics data. The software creates and visualizes virtual two-dimensional (2D) protein gels based on the migration behaviour of proteins in dependence of their theoretical molecular weights in combination with their calculated isoelectric points. The utilization of all proteins of an organism of interest deduced from genes of the corresponding genome project in combination with the elimination of obvious membrane proteins permits the creation of an optimized calculated proteome map. The electrophoretic separation behaviour of single proteins is accessible interactively in a Java(TM) applet (small application in a web browser) by selecting a pI/MW range and an electrophoretic timescale of interest. The calculated pattern of protein spots helps to identify unknown proteins and to localize known proteins during experimental proteomics approaches. Differences between the experimentally observed and the calculated migration behaviour of certain proteins provide first indications for potential protein modification events. When possible, the protein spots are directly linked via a mouse click to the public databases SWISS-PROT and PRODORIC. Additionally, we provide tools for the serial calculation and visualization of specific protein properties like pH dependent charge curves and hydrophobicity profiles. These values are helpful for the rational establishment of protein purification procedures. The proteomics tools are available on the World Wide Web at http://prodoric.tu-bs.de/proteomics.php.

Computer Graphics↗
PubMed2006

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology.

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.

Animals↗
PubMed2005

Identification of differentially expressed proteins of gamma-ray irradiated rat intestinal epithelial IEC-6 cells by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionisation-time of flight mass spectrometry.

To identify proteins involved in the processes of cellular and molecular response to radiation damage repair in intestinal epithelial IEC-6 cells, we comparatively analyzed the proteome of irradiated IEC-6 cells with that of normal cells. A series of methods were used, including two-dimensional gel electrophoresis (Z-DE), PDQuest software analysis of 2-DE gels, peptide mass fingerprinting based on matrix-assisted laser desorption/ionisation-time of flight-mass spectrometry (MALDI-TOF-MS), and Swiss-Prot database searching, to separate and identify differentially expressed proteins. Western blotting and reverse transcriptase polymerase chain reaction (RT-PCR) were used to validate the differentially expressed proteins. Image analysis revealed that averages of 608 +/- 39 and 595 +/- 31 protein spots were detected in normal and irradiated IEC-6 cells, respectively. Sixteen differential protein spots were isolated from gels, and measured with MALDI-TOF-MS. A total of 14 spots yielded good spectra, and 11 spots matched with known proteins after database searching. These proteins were mainly involved in anti-oxidation, metabolism, and protein post-translational processes. Western blotting confirmed that stress-70 protein was down-regulated by gamma-irradiation. Up-regulation of ERP29 was confirmed by RT-PCR, indicating that it is involved in ionizing radiation. The clues provided by the comparative proteome strategy utilized here will shed light on molecular mechanisms of radiation damage repair in intestinal epithelial cells.

Animals↗
PubMed2006

INTEGRATOR: interactive graphical search of large protein interactomes over the Web.

BACKGROUND: The rapid growth of protein interactome data has elevated the necessity and importance of network analysis tools. However, unlike pure text data, network search spaces are of exponential complexity. This poses special challenges for storing, searching, and navigating this data efficiently. Moreover, development of effective web interfaces has been difficult. RESULTS: We present Integrator, a web-integrated graphical search tool for protein-protein interaction networks across 50+ genomes. CONCLUSION: Integrator provides single and multiple protein searches of the Bioverse database containing experimentally-derived and predicted protein-protein interactions. The interface provides animated local network views, rapid subgraph manipulation, and cross-referencing of functional annotations. Integrator is available at http://bioverse.compbio.washington.edu/integrator.

Algorithms↗
PubMed2006

Computational prediction of cancer-gene function.

Most cancer genes remain functionally uncharacterized in the physiological context of disease development. High-throughput molecular profiling and interaction studies are increasingly being used to identify clusters of functionally linked gene products related to neoplastic cell processes. However, in vivo determination of cancer-gene function is laborious and inefficient, so accurately predicting cancer-gene function is a significant challenge for oncologists and computational biologists alike. How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets? We explore plausible solutions to this important challenge.

Computational Biology↗
PubMed2002

A model of random mass-matching and its use for automated significance testing in mass spectrometric proteome analysis.

A rapid and accurate method for testing the significance of protein identities determined by mass spectrometric analysis of protein digests and genome database searching is presented. The method is based on direct computation using a statistical model of the random matching of measured and theoretical proteolytic peptide masses. Protein identification algorithms typically rank the proteins of a genome database according to a score based on the number of matches between the masses obtained by mass spectrometry analysis and the theoretical proteolytic peptide masses of a database protein. The random matching of experimental and theoretical masses can cause false results. A result is significant only if the score characterizing the result deviates significantly from the score expected from a false result. A distribution of the score (number of matches) for random (false) results is computed directly from our model of the random matching, which allows significance testing under any experimental and database search constraints. In order to mimic protein identification data quality in large-scale proteome projects, low-to-high quality proteolytic peptide mass data were generated in silico and subsequently submitted to a database search program designed to include significance testing based on direct computation. This simulation procedure demonstrates the usefulness of direct significance testing for automatically screening for samples that must be subjected to peptide sequence analysis by e.g. tandem mass spectrometry in order to determine the protein identity.

Algorithms↗
PubMed2006

Modification-specific proteomics of plasma membrane proteins: identification and characterization of glycosylphosphatidylinositol-anchored proteins released upon phospholipase D treatment.

Plasma membrane proteins are displayed through diverse mechanisms, including anchoring in the extracellular leaflet via glycosylphosphatidylinositol (GPI) molecules. GPI-anchored membrane proteins (GPI-APs) are a functionally and structurally diverse protein family, and their importance is well-recognized as they are candidate cell surface biomarker molecules with potential diagnostic and therapeutic applications in molecular medicine. GPI-APs have also attracted interest in plant biotechnology because of their role in root development and cell remodeling. Using a shave-and-conquer concept, we demonstrate that phospholipase D (PLD) treatment of human and plant plasma membrane fractions leads to the release of GPI-anchored proteins that were identified and characterized by capillary liquid chromatography and tandem mass spectrometry. In contrast to phospholipase C, the PLD enzyme is not affected by structural heterogeneity of the GPI moiety, making PLD a generally useful reagent for proteomic investigations of GPI-anchored proteins in a variety of cells, tissues, and organisms. A total of 11 human GPI-APs and 35 Arabidopsis thaliana GPI-APs were identified, representing a significant addition to the number of experimentally detected GPI-APs in both species. Computational GPI-AP sequence analysis tools were investigated for the characterization of the identified GPI-APs, and these demonstrated that there is some discrepancy in their efficiency in classification of GPI-APs and the exact assignment of omega-sites. This study highlights the efficiency of an integrative proteomics approach that combines experimental and computational methods to provide the selectivity, specificity, and sensitivity required for characterization of post-translationally modified membrane proteins.

Amino Acid Sequence↗
PubMed2003

Peptide libraries: at the crossroads of proteomics and bioinformatics.

Peptide libraries offer a valuable means for providing functional information regarding protein-modifying enzymes and protein interaction domains. Library approaches have become increasingly useful as high-throughput strategies for the analysis of large numbers of new proteins identified as a result of genome-sequencing efforts. Recent developments in the field have produced faster methods with broadened applicability. Crucially, new computational and biochemical tools have emerged that facilitate identification of interaction partners and substrates for proteins on the basis of their peptide selectivity profiles. Such combinations of proteomics-scale experimental approaches with bioinformatics tools hold great promise for the elucidation of protein interaction networks and signal transduction pathways in living cells.

Computational Biology↗