A time-resolved fluorescence resonance energy transfer assay suitable for high-throughput screening for inhibitors of immunoglobulin E-receptor interactions

The interaction of immunoglobulin E (IgE) antibodies with the high-affinity receptor, FcεRI, plays a central role in initiating most allergic reactions. The IgE-receptor interaction has been targeted for treatment of allergic diseases, and many high-affinity macromolecular inhibitors have been identified. Small molecule inhibitors would offer significant advantages over current anti-IgE treatment, but no candidate compounds have been identified and fully validated. Here, we report the development of a time-resolved fluorescence resonance energy transfer (TR-FRET) assay for monitoring the IgE-receptor interaction. The TR-FRET assay measures an increase in fluorescence intensity as a donor lanthanide fluorophore is recruited into complexes of site-specific Alexa Fluor 488-labeled IgE-Fc and His-tagged FcεRIα proteins. The assay can readily monitor classic competitive inhibitors that bind either IgE-Fc or FcεRIα in equilibrium competition binding experiments. Furthermore, the TR-FRET assay can also be used to follow the kinetics of IgE-Fc-FcεRIα dissociation and identify inhibitory ligands that accelerate the dissociation of preformed complexes, as demonstrated for an engineered DARPin (designed ankyrin repeat protein) inhibitor. The TR-FRET assay is suitable for high-throughput screening (HTS), as shown by performing a pilot screen of the National Institutes of Health (NIH) Clinical Collection Library in a 384-well plate format.

LIKELIHOOD ESTIMATION OF CONJUGACY RELATIONSHIPS IN LINEAR MODELS WITH APPLICATIONS TO HIGH-THROUGHPUT GENOMICS

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately capitalizing on the benefits of a well fitting fully-specified likelihood in the terms of gene ranking is difficult.

A HIDDEN MARKOV MODEL FOR JOINT ESTIMATION OF GENOTYPE AND COPY NUMBER IN HIGH-THROUGHPUT SNP CHIPS

Amplifications and deletions of chromosomal DNA, as well as copy-neutral loss of heterozygosity have been associated with diseases processes. High-throughput single nucleotide polymorphism (SNP) arrays are useful for making genome-wide estimates of copy number and genotype calls. Because neighboring SNPs in high throughput SNP arrays are likely to have dependent copy number and genotype due to the underlying haplotype structure and linkage disequilibrium, hidden Markov models (HMM) may be useful for improving genotype calls and copy number estimates that do not incorporate information from nearby SNPs. We improve previous approaches that utilize a HMM framework for inference in high throughput SNP arrays by integrating copy number, genotype calls, and the corresponding confidence scores when available. Using simulated data, we demonstrate how confidence scores control smoothing in a probabilistic framework. Software for fitting HMMs to SNP array data is available in the R package ICE.

Enzyme Assays: High-throughput Screening, Genetic Selection and Fingerprinting

Edited by one of the leading experts in the field, this book fills the need for a book presenting the most important methods for high-throughput screenings and functional characterization of enzymes. It adopts an interdisciplinary approach, making it indispensable for all those involved in this expanding field, and reflects the major advances made over the past few years. For biochemists, analytical, organic and catalytic chemists, and biotechnologists.