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At least 739 records · Page 41Linked to original sources
PubMed2006

Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm.

BACKGROUND: Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. RESULTS: High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. CONCLUSION: More than 1.6 million molecular formulae in the range 0-500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.

Algorithms↗
PubMed2006

A direct comparison of protein interaction confidence assignment schemes.

BACKGROUND: Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing large protein interaction networks for various species at an ever-growing pace. However, common technologies like yeast two-hybrid may experience high rates of false positive detection. To combat false positive discoveries, a number of different methods have been recently developed that associate confidence scores with protein interactions. Here, we perform a rigorous comparative analysis and performance assessment among these different methods. RESULTS: We measure the extent to which each set of confidence scores correlates with similarity of the interacting proteins in terms of function, expression, pattern of sequence conservation, and homology to interacting proteins in other species. We also employ a new metric, the Signal-to-Noise Ratio of protein complexes embedded in each network, to assess the power of the different methods. Seven confidence assignment schemes, including those of Bader et al., Deane et al., Deng et al., Sharan et al., and Qi et al., are compared in this work. CONCLUSION: Although the performance of each assignment scheme varies depending on the particular metric used for assessment, we observe that Deng et al. yields the best performance overall (in three out of four viable measures). Importantly, we also find that utilizing any of the probability assignment schemes is always more beneficial than assuming all observed interactions to be true or equally likely.

Caenorhabditis elegans Proteins↗
PubMed2006

A methodology for the structural and functional analysis of signaling and regulatory networks.

BACKGROUND: Structural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functional capabilities. While the structural analysis of metabolic networks is a well-established field, similar methodologies have been scarcely developed and applied to signaling and regulatory networks. RESULTS: We propose formalisms and methods, relying on adapted and partially newly introduced approaches, which facilitate a structural analysis of signaling and regulatory networks with focus on functional aspects. We use two different formalisms to represent and analyze interaction networks: interaction graphs and (logical) interaction hypergraphs. We show that, in interaction graphs, the determination of feedback cycles and of all the signaling paths between any pair of species is equivalent to the computation of elementary modes known from metabolic networks. Knowledge on the set of signaling paths and feedback loops facilitates the computation of intervention strategies and the classification of compounds into activators, inhibitors, ambivalent factors, and non-affecting factors with respect to a certain species. In some cases, qualitative effects induced by perturbations can be unambiguously predicted from the network scheme. Interaction graphs however, are not able to capture AND relationships which do frequently occur in interaction networks. The consequent logical concatenation of all the arcs pointing into a species leads to Boolean networks. For a Boolean representation of cellular interaction networks we propose a formalism based on logical (or signed) interaction hypergraphs, which facilitates in particular a logical steady state analysis (LSSA). LSSA enables studies on the logical processing of signals and the identification of optimal intervention points (targets) in cellular networks. LSSA also reveals network regions whose parametrization and initial states are crucial for the dynamic behavior. We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer) and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical) signal processing upon different stimuli. CONCLUSION: The methods and formalisms we propose herein are another step towards the comprehensive functional analysis of cellular interaction networks. Their potential, shown on a realistic T-cell signaling model, makes them a promising tool.

Animals↗
PubMed2005

Improved tagging strategy for protein identification in mammalian cells.

BACKGROUND: The tagging strategy enables full-length endogenous proteins in mammalian cells to be expressed as green fluorescent fusion proteins from their authentic promoters. RESULTS: We describe improved genetic tools to facilitate protein tagging in mammalian cells based on a mobile genetic element that harbors an artificial exon encoding a protein tag. Insertion of the artificial exon within introns of cellular genes results in expression of hybrid proteins consisting of the tag sequence fused in-frame to sequences of a cellular protein. We have used lentiviral vectors to stably introduce enhanced green fluorescent protein (EGFP) tags into expressed genes in target cells. The data obtained indicate that this strategy leads to bona fide tripartite fusion proteins and that the EGFP tag did not affect the subcellular localization of such proteins. CONCLUSION: The tools presented here have the potential for protein discovery, and subsequent investigation of their subcellular distribution and role(s) under defined physiological conditions, as well as for protein purification and protein-protein interaction studies.

Animals↗
PubMed2005

Effect of dataset selection on the topological interpretation of protein interaction networks.

BACKGROUND: Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. RESULTS: We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. CONCLUSION: When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected.

Computational Biology↗
PubMed2002

Back to basics.

A report on the annual Association of Biomolecular Resource Facilities (ABRF) meeting, Austin, Texas, 9-12 March 2002.

Animals↗
PubMed2006

The interaction map of yeast: terra incognita?

A systematic curation of the literature on Saccharomyces cerevisiae has yielded a comprehensive collection of experimentally observed interactions. This new resource augments current views of the topological structure of yeast's physical and genetic networks, but also reveals that existing studies cover only a fraction of the cell.

Computational Biology↗
PubMed2004

Proceedings: the Applications of Bioinformatics in Cancer Detection Workshop.

The Division of Cancer Prevention of the National Cancer Institute sponsored and organized the Applications of Bioinformatics in Cancer Detection Workshop on August 6-7, 2002. The goal of the workshop was to evaluate the state of the science of bioinformatics and determine how it may be used to assist early cancer detection, risk identification, risk assessment, and risk reduction. This paper summarizes the proceedings of this conference and points out future directions for research.

Computational Biology↗
PubMed2005

Identification of components of protein complexes.

Protocols are given for a variety of techniques used in protein identification of complexes, including identification of in-gel separated proteins and LC-MS/MS. Gels, staining procedures, and peptide extraction protocols that are compatible with mass spectrometry are described. The detection limits of the various staining procedures and their compatibility with mass spectrometry are discussed. The various mass spectrometric techniques used (MALDI-MS, MALDI-MS/MS, nanospray, and ESI/LC-MS/MS) are also described, along with an indication of the advantages and disadvantages of each, and when they would most appropriately be used. Common pitfalls associated with database searching are also discussed.

Animals↗
PubMed2006

MALDI-MS data analysis for disease biomarker discovery.

In this chapter, we address the issue of matrix-assisted laser desorption/ionization mass spectrometry (MS) data analysis for disease biomarker discovery. We first give a general framework of MS data analysis, then focus on several key steps. After that, we show some application examples using an ovarian sera cancer dataset. Finally, we discuss the limitations of current approaches and possible future research directions.

Animals↗
PubMed2002

Protein interaction-targeted drug discovery: evaluating critical issues.

Employment of the decision strategies outlined in this general discussion should help to pinpoint mode of activity in drug development and validation. Overall, as a paradigm for drug development, a search for small molecules that can interfere with PPIs would seem to have significant long term potential. At present, the level of structural knowledge in databases is not sufficient to predict in toto the protein binding properties of a modeled drug, but as databases improve, this may become generally feasible. A major point that remains to be determined is how much specificity of protein binding can be incorporated into molecules of generally less than 500 Da. Finally, integration of PPI-targeting strategies with other approaches towards drug design will enhance the number of signaling pathways that can effectively be targeted. These points will be particularly pertinent as technologies permit a systematic identification of encoded protein interactions that govern the proteornic complement of cells.

Chemistry, Pharmaceutical↗
PubMed2004

Quantitative beta-galactosidase assay suitable for high-throughput applications in the yeast two-hybrid system.

Measurement of beta-galactosidase (beta-gal) activity is an important step in every yeast two-hybrid assay, yet many commonly used methods have distinct disadvantages, such as being only qualitative, time-consuming, and cumbersome when processing large numbers of samples. To overcome these drawbacks, we have implemented a novel technique, termed pellet X-gal assay, that allows simultaneous quantitative measurements from large numbers of samples with a minimum of hands-on time. The method was tested using five different, previously described protein-protein interactions and compared to two standard methods, the colony filter lift and the liquid ONPG assay. Our assay allows accurate quantitative measurements of protein-protein interactions and covers a greater dynamic range than the classic ONPG assay. The novel assay is robust and requires very little handling, making it suitable for applications in which several hundreds of individual protein interaction pairs need to be measured simultaneously.

Enzyme Activation↗
PubMed2004

Recent advances in peptide-based microarray technologies.

Peptide array is a rapidly growing tool that provides both large-scale and high-throughput capabilities for protein detection and activity studies. Materials presented in this review will examine the recent advances in the field of peptide microarray with special emphasis on the generation and applications of high-density arrays of peptides on glass slides.

Enzymes↗