Data reduction methods for application of fluorescence correlation spectroscopy to pharmaceutical drug discovery.

Fluorescence methods are commonly used in pharmaceutical drug discovery to assay the binding of drug-like compounds to signaling proteins and other bio-particles. For binding studies of non-fluorescent compounds, a competitive format may be used in which the binding of the compound results in displacement of another fluorescently labeled ligand. Highly-sensitive measurements within nano-liter sized open probe volumes can be accomplished using a confocal epi-illumination geometry and thus key tools for such drug-binding studies include fluorescence correlation spectroscopy (FCS) and its related techniques. This paper reviews the general protocol for application of FCS to biomolecular compound-binding assays and it focuses on methods for the reduction of experimental photon count data to obtain the normalized autocorrelation function (ACF), on theoretical models of the ACF, and on statistical and systematic errors in the experimental ACF. Results from a detailed Monte Carlo simulation of FCS, which are useful for testing theoretical models and validating short-duration assay capabilities, are discussed. An illustrative example is presented on the use of FCS to assay binding of Alexa-488-labeled Bak peptide with Bcl-x(L), which is an intracellular protein that acts to protect against programmed cell death.