Methods in molecular biology
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Bioluminescence resonance energy transfer (BRET) is a prominent biophysical technology for monitoring molecular interactions, and has been widely used to study protein-protein interactions (PPI) in live cells. This technology requires proteins of interest to be associated with an energy donor (i.e., luciferase) and an acceptor (e.g., fluorescent protein) molecule. Upon interaction of the proteins of interest, the donor and acceptor will be brought into close proximity and energy transfer of chemical reaction-induced luminescence to its corresponding acceptor will result in an increased emission at an acceptor-defined wavelength, generating the BRET signal. ⋯ We term this NLuc-based BRET platform "BRET(n)". BRET(n) has been demonstrated to have significantly improved assay performance, compared to previous BRET technologies, in terms of sensitivity and scalability. This chapter describes a step-by-step practical protocol for developing a BRET(n) assay in a multi-well plate format to detect PPIs in live mammalian cells.
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In recent years, mass spectrometry-based phosphoproteomics has propelled our knowledge about the regulation of cellular pathways. Nevertheless, typically applied bottom-up strategies have several limitations. Trypsin, the preferentially used proteolytic enzyme shows impaired cleavage efficiency in the vicinity of phosphorylation sites. ⋯ To overcome these limitations, we introduce an alternative and simple approach based on the usage of the nonspecific serine protease subtilisin, which enables a fast and reproducible digestion and provides access to "hidden" areas of the proteome. Thus, in a single LC-MS experiment >1800 phosphopeptides were confidently identified and localized from 125 μg of HeLa digest, compared to >2100 sites after tryptic digestion. While the overlap was less than 20 %, subtilisin allowed the identification of many phosphorylation sites that are theoretically not accessible via tryptic digestion, thus considerably increasing the coverage of the phosphoproteome.
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This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple data set, then proceeds to explain how one might approach a bioinformatics problem. Finally, it includes a brief summary of machine learning algorithms for other types of data mining problems, and provides suggestions about where to find additional information.
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β-D-glucan (Fungitell) and polymerase chain reaction-based (T2Candida) assays of blood samples are FDA-approved adjuncts to cultures for diagnosing candidemia and other types of invasive candidiasis, but their clinical roles are unclear. In this chapter, we describe laboratory protocols for performing Fungitell and T2Candida assays. We then discuss step-by-step methods for interpreting test results at the bedside using a Bayesian framework, and for incorporating assays into rational patient management strategies. ⋯ Using the thresholds, it is possible to assign windows of pretest likelihood for invasive candidiasis (and corresponding patient populations) in which tests are most likely to valuable. At the individual patient level, tests may be useful outside of the windows proposed for screening populations. The interpretive and clinical decision-making processes we discuss will be applicable to other diagnostic assays as they enter the clinic, and to existing assays as more data emerge from various populations.
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Isobaric tagging reagents have become an invaluable tool for multiplexed quantitative proteomic analysis. These reagents can label multiple, distinct peptide samples from virtually any source material (e.g., tissue, cell line, purified proteins), allowing users the opportunity to assess changes in peptide abundances across many different time points or experimental conditions. Here, we describe the application of isobaric peptide labeling, specifically 8plex isobaric tags for relative and absolute quantitation (8plex iTRAQ), for quantitative phosphoproteomic analysis of cultured cells or tissue suspensions. For this particular protocol, labeled samples are pooled, fractionated by strong cation exchange chromatography, enriched for phosphopeptides, and analyzed by tandem mass spectrometry (LC-MS/MS) for both peptide identification and quantitation.