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At least 613 records · Page 34Linked to original sources
PubMed2003

Proteome analysis using isoelectric focusing in immobilized pH gradient gels followed by mass spectrometry.

Over the past several years, a large effort has been focused on improvements of two-dimensional (2-D) gel electrophoresis-based proteomics technology, and on development of novel approaches for proteome analysis. Here, we describe the application of an alternative strategy for the analysis of complex proteomes. The strategy combines isoelectric focusing in immobilized pH gradient strips (in-gel IEF), mass spectrometry (MS), and bioinformatics. A protein mixture is separated by in-gel IEF, and the entire strip is cut into a set of gel sections. Proteins in each gel section are digested with trypsin, and the tryptic peptides are subjected to liquid chromatography-nanoelectrospray-quadrupole ion-trap tandem mass spectrometry (LC-ESI-MS/MS). The LC-ESI-MS/MS data are used to identify the proteins through searches of a protein sequence database. Using this in-gel IEF-LC-MS/MS strategy, we have identified 127 proteins from a human pituitary. This study demonstrates the potential of the in-gel IEF-LC-MS/MS approach for analyses of complex mammalian proteomes.

Computational Biology↗
PubMed2004

Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS.

Tandem mass spectrometry has been used increasingly for high-throughput analysis of complex protein samples. A major challenge lies in the consistent, objective and transparent analysis of the large amounts of data generated by such experiments and in their dissemination and publication. Here, we review currently available computational tools and discuss the need for statistical criteria in the analysis of large proteomics datasets.

Amino Acid Sequence↗
PubMed2006

A quantitative analysis of secondary RNA structure using domination based parameters on trees.

BACKGROUND: It has become increasingly apparent that a comprehensive database of RNA motifs is essential in order to achieve new goals in genomic and proteomic research. Secondary RNA structures have frequently been represented by various modeling methods as graph-theoretic trees. Using graph theory as a modeling tool allows the vast resources of graphical invariants to be utilized to numerically identify secondary RNA motifs. The domination number of a graph is a graphical invariant that is sensitive to even a slight change in the structure of a tree. The invariants selected in this study are variations of the domination number of a graph. These graphical invariants are partitioned into two classes, and we define two parameters based on each of these classes. These parameters are calculated for all small order trees and a statistical analysis of the resulting data is conducted to determine if the values of these parameters can be utilized to identify which trees of orders seven and eight are RNA-like in structure. RESULTS: The statistical analysis shows that the domination based parameters correctly distinguish between the trees that represent native structures and those that are not likely candidates to represent RNA. Some of the trees previously identified as candidate structures are found to be "very" RNA like, while others are not, thereby refining the space of structures likely to be found as representing secondary RNA structure. CONCLUSION: Search algorithms are available that mine nucleotide sequence databases. However, the number of motifs identified can be quite large, making a further search for similar motif computationally difficult. Much of the work in the bioinformatics arena is toward the development of better algorithms to address the computational problem. This work, on the other hand, uses mathematical descriptors to more clearly characterize the RNA motifs and thereby reduce the corresponding search space. These preliminary findings demonstrate that graph-theoretic quantifiers utilized in fields such as computer network design hold significant promise as an added tool for genomics and proteomics.

Algorithms↗
PubMed2005

The wheat (Triticum aestivum L.) leaf proteome.

The wheat leaf proteome was mapped and partially characterized to function as a comparative template for future wheat research. In total, 404 proteins were visualized, and 277 of these were selected for analysis based on reproducibility and relative quantity. Using a combination of protein and expressed sequence tag database searching, 142 proteins were putatively identified with an identification success rate of 51%. The identified proteins were grouped according to their functional annotations with the majority (40%) being involved in energy production, primary, or secondary metabolism. Only 8% of the protein identifications lacked ascertainable functional annotation. The 51% ratio of successful identification and the 8% unclear functional annotation rate are major improvements over most previous plant proteomic studies. This clearly indicates the advancement of the plant protein and nucleic acid sequence and annotation data available in the databases, and shows the enhanced feasibility of future wheat leaf proteome research.

Computational Biology↗
PubMed2004

Systematic comparison of a two-dimensional ion trap and a three-dimensional ion trap mass spectrometer in proteomics.

The utility and advantages of the recently introduced two-dimensional quadrupole ion trap mass spectrometer in proteomics over the traditional three-dimensional ion trap mass spectrometer have not been systematically characterized. Here we rigorously compared the performance of these two platforms by using over 100,000 tandem mass spectra acquired with identical complex peptide mixtures and acquisition parameters. Specifically we compared four factors that are critical for a successful proteomic study: 1) the number of proteins identified, 2) sequence coverage or the number of peptides identified for every protein, 3) the data base matching SEQUEST X(corr) and S(p) score, and 4) the quality of the fragment ion series of peptides. We found a 4-6-fold increase in the number of peptides and proteins identified on the two-dimensional ion trap mass spectrometer as a direct result of improvement in all the other parameters examined. Interestingly more than 70% of the doubly and triply charged peptides, but not the singly charged peptides, showed better quality of fragmentation spectra on the two-dimensional ion trap. These results highlight specific advantages of the two-dimensional ion trap over the conventional three-dimensional ion traps for protein identification in proteomic experiments.

Computational Biology↗
PubMed2004

Bioinformatics as a critical prerequisite to transcriptome and proteome studies.

Large-scale genomic studies rely strongly on annotations available in databases to design experimental supports such as arrays or to explain results in term of biological meaning. Most of this information originates from bioinformatic predictions. Their accuracy as well as their relevance to existing biological data are critical in avoiding the misinterpretation of experimental results.

Computational Biology↗
PubMed2003

High throughput protein production for functional proteomics.

A major impact of genome projects on human health will be their contribution to the understanding of protein function. Proteins are the engines of biological systems, nearly all pharmaceuticals act on proteins and increasingly proteins themselves are used therapeutically. As biology enters the post-genomic era, researchers have begun to embrace the exciting opportunity of investigating proteins in high throughput (HT) experiments. The study of proteins includes a vast array of techniques ranging from enzyme catalysis assays to interaction and structural studies. Many of these methods depend on purified proteins. The discovery of thousands of novel protein-coding sequences and the increased availability of large cDNA collections provide the opportunity to investigate protein function in a systematic manner and at an unprecedented scale. This opportunity highlights the need for development of HT methods for protein isolation. This article describes the challenges faced and the approaches taken to develop proteome-scale protein expression systems.

Cell-Free System↗
PubMed2001

Computational cell biologists snowed in at Cranwell.

Cell biology is being inundated by an avalanche of data from the genomics and proteomics enterprises. The complexity and sheer volume of information threaten to overwhelm the ability of traditional cell biologists to grasp its implications and develop experimentally testable hypotheses. For this reason, some have begun to explore computational approaches towards organizing complex data into quantitative models. This requires communication and collaboration between the biological science community and and the physical and mathematical sciences communities. A recent meeting [The First International Symposium on Computational Cell Biology, Cranwell Resort, Lenox, MA, USA; 4-6 March 2001. Organizers: J.H. Carson, A. Cowan, and L.M. Loew (www.nrcam.uchc.edu/conference).] made a first attempt to bring these two communities together. Three feet of new snow fell during the meeting, but the 125 attendees, an unusual mixture of cell biologists, computer scientists, mathematicians, physicists, and engineers, were having too much fun defining the new field of computational cell biology to notice that they were literally snowed in.

Animals↗
PubMed2005

The utility of accurate mass and LC elution time information in the analysis of complex proteomes.

The combination of mass and normalized elution time (NET) of a peptide identified by liquid chromatography-mass spectrometry (LC-MS) measurements can serve as a unique signature for that peptide. However, the specificity of an LC-MS measurement depends upon the complexity of the proteome (i.e., the number of possible peptides) and the accuracy of the LC-MS measurements. In this work, theoretical tryptic digests of all predicted proteins from the genomes of three organisms of varying complexity were evaluated for specificity. Accuracy of the LC-MS measurement of mass-NET pairs (on a 0 to 1.0 NET scale) was described by bivariate normal sampling distributions centered on the peptide signatures. Measurement accuracy (i.e., mass and NET standard deviations of +/-0.1, 1, 5, and 10 ppm, and +/-0.01 and 0.05, respectively) was varied to evaluate improvements in process quality. The spatially localized confidence score, a conditional probability of peptide uniqueness, formed the basis for the peptide identification. Application of this approach to organisms with comparatively small proteomes, such as Deinococcus radiodurans, shows that modest mass and elution time accuracies are generally adequate for confidently identifying most peptides. For more complex proteomes, more accurate measurements are required. However, the study suggests that the majority of proteins for even the human proteome should be identifiable with reasonable confidence by using LC-MS measurements with mass accuracies within +/-1 ppm and high efficiency separations having elution time measurements within +/-0.01 NET.

Animals↗
PubMed2004

The nonconserved wrapping of conserved protein folds reveals a trend toward increasing connectivity in proteomic networks.

Although protein folding domains are generally conserved for function across distant homologous sequences, one crucial structural feature is not conserved: the wrapping of backbone hydrogen bonds, that is, the extent to which they are intramolecularly desolvated and thereby protected from water attack. Extensive data on protein complex interfaces led us to postulate that insufficiently wrapped backbone hydrogen bonds in monomeric domains must be adhesive, and therefore determinants of interactivity, a result that has been experimentally confirmed. Here, we show that the wrapping of certain conserved folds becomes progressively poorer as species diverge in some lineages. This trend is thus concurrent with a progressive enhancement of the interactivity of individual domains sharing the conserved fold. Such increase in interactivity is predicted to impose an "evolutionary brake" on the overall speed of sequence divergence. This phenomenon follows when more and more residues become engaged in protein associations and thus become functionally indispensable. For complete proteomes for which statistically significant structural data are available, scale-free network statistics based solely on the distribution of folding domains, catalogued by their number of wrapping defects, best describe the proteomic connectivity. Thus, the intermolecular connectivity may be effectively used as a measure of species complexity. Our results might contribute to explaining how interactome complexity may be achieved without a dramatic increase in genome size.

Animals↗
PubMed2006

Geographical focus. Proteomics initiatives in Spain: ProteoRed.

The Spanish National Network of Proteomic Facilities--ProteoRed has been created as an initiative for the coordination, integration and development of the proteomics facilities and laboratories distributed throughout Spain. ProteoRed's main objective is to give support to the scientific community allowing them wide access to emerging proteomics technologies and thus encouraging the science of proteomics. In addition, standardization of protocols and robustness of workflows are addressed by multi-centric laboratory activities. Educational, training and dissemination issues are part of the core activities of ProteoRed. To reach these objectives, specific activities have been developed through six working groups (WG1-WG6) covering functional, technical, educational and scientific aspects of proteomics.

Computational Biology↗
PubMed2002

Evaluation of proteome reference maps for cross-species identification of proteins by peptide mass fingerprinting.

We tested whether proteome reference maps established for one species can be used for cross-species protein identification by comparing two-dimensional protein gel patterns and protein identification data of two closely related bacterial strains and four plant species. First, proteome profiles of two strains of the fully sequenced bacterium Sinorhizobium meliloti were compared as an example of close relatedness, high reproducibility and sequence availability. Secondly, the proteome profiles of three legumes (Medicago truncatula, Melilotus alba and Trifolium subterraneum), and the nonlegume rice (Oryza sativa) were analysed to test cross-species similarities. In general, we found stronger similarities in gel patterns of the arrayed proteins between the two bacterial strains and between the plant species than could be expected from the sequence similarities. However, protein identity could not be concluded from their gel position, not even when comparing strains of the same species. Surprisingly, in the bacterial strains peptide mass fingerprinting was more reliable for species-specific protein identification than N-terminal sequencing. While peptide masses were found to be unreliable for cross-species protein identification, we present useful criteria to determine confident matching against species-specific expressed sequence tag databases. In conclusion, we present evidence that cautions the use of proteome reference maps and peptide mass fingerprinting for cross-species protein identification.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2004

Serum proteomics in cancer diagnosis and management.

Mass spectrometry-based diagnostics has the potential to revolutionize molecular medicine. Using modern mass-spectrometer technologies, clinical tests can be developed that are practical, robust, accurate, and inexpensive. Serum proteomic pattern profiling couples mass spectrometry with adaptive artificial-intelligence-based bioinformatics, which can now be employed to detect pathological states reflected in the serum proteome. With this approach, rapid and cost-effective tests with exquisite clinical sensitivity and specificity are emerging. These tools may dramatically change how disease is detected, monitored, and managed.

Blood Proteins↗
PubMed2006

Design and implementation of a tool for translating SBML into the biochemical stochastic pi-calculus.

MOTIVATION: SBML is becoming a standard 'de-facto' to represent and store biological models. Although SBML is very useful in defining ways of exchanging and storing biological information, it is not formal enough to allow direct translation into non ambiguous formal representation languages to perform analysis and simulation of models. We here suggest to map SBML models into process calculi representations. RESULTS: We implemented and validated a tool that translates SBML descriptions into stochastic pi-calculus specifications. AVAILABILITY: Source code is freely available for academic use by contacting the authors.

Algorithms↗
PubMed2003

A metabolic network analysis & NMR experiment design tool with user interface-driven model construction for depth-first search analysis.

A Windows program for metabolic engineering analysis and experimental design has been developed. A graphical user interface enables the pictorial, "on-screen" construction of a metabolic network. Once a model is composed, balance equations are automatically generated. Model construction, modification and information exchange between different users is thus considerably simplified. For a given model, the program can then be used to predict all the extreme point flux distributions that optimize an objective function while satisfying balances and constraints by using a depth-first search strategy. One can also find the minimum reaction set that satisfies different conditions. Based on the identified flux distributions or linear combinations, the user can simulate the NMR and GC/MS spectra of selected signal molecules. Alternately, spectra vectorization allows for the automated optimization of labeling experiments that are intended to distinguish between different, yet plausible flux extreme point distributions. The example provided entails predicting the flux distributions associated with deleting pyruvate kinase and designing 13C NMR experiments that can maximally discriminate between the flux distributions.

Computer Simulation↗
PubMed2005

DNA microarray stochastic model.

A stochastic model of the DNA microarray image pixels is presented. The model includes spot pixel intensity distribution, interpixel correlations and the intensity distribution of background noise. The data is indicative of a small exponential additive noise process and a larger Gaussian fluctuation that scales with spot intensity. Correlations are observed among pixels in the spot and between test and control images. The correlated fluctuations may be attributed to variations across each spot in the amount of DNA placed on the spot during the array fabrication process. The model may be used in gene expression estimation algorithm development, both to test new algorithms through simulation and to develop optimum algorithms. The model should also be easily adapted to new array based technologies in proteomics.

Computer Simulation↗
PubMed2006

A versatile method for deciphering plant membrane proteomes.

Proteomics is a very powerful approach to link the information contained in sequenced genomes, like that of Arabidopsis, to the functional knowledge provided by studies of plant cell compartments. This article summarizes the different steps of a versatile strategy that has been developed to decipher plant membrane proteomes. Initiated with envelope membranes from spinach chloroplasts, this strategy has been adapted to thylakoids, and further extended to a series of membranes from the model plant Arabidopsis: chloroplast envelope membranes, plasma membrane, and mitochondrial membranes. The first step is the preparation of highly purified membrane fractions from plant tissues. The second step in the strategy is the fractionation of membrane proteins on the basis of their physico-chemical properties. Chloroform/methanol extraction and washing of membranes with NaOH, NaCl or any other agent led to the simplification of the protein content of the fraction to be analysed. The next step is the genuine proteomic step, i.e. the separation of proteins by 1D-gel electrophoresis followed by in-gel proteolytic digestion of the polypeptides, analysis of the proteolytic peptides using mass spectrometry, and protein identification by searching through databases. The last step is the validation of the procedure by checking the subcellular location. The results obtained by using this strategy demonstrate that a combination of different proteomics approaches, together with bioinformatics, indeed provide a better understanding of the biochemical machinery of the different plant membranes at the molecular level.

Alkalies↗
PubMed2006

4(th) HUPO Brain Proteome Project Workshop in Munich, Germany.

More than 70 interested colleagues attended the 4(th) Workshop of HUPO's Brain Proteome Project. The project was presented within nine talks mainly focusing on two running pilot studies as well as on data re-processing. A bioinformatics jamboree in Hinxton, UK, and the 5th Workshop taking place in Dublin next February were announced.

Brain↗