Sensitive Spectroscopic Detection of Large and Denatured Protein Aggregates in Solution by Use of the Fluorescent Dye Nile Red

The fluorescent dye Nile red was used as a probe for the sensitive detection of large, denatured aggregates of the model protein β-galactosidase (E. coli) in solution. Aggregates were formed by irreversible heat denaturation of β-galactosidase below and above the protein’s unfolding temperature of 57.4°C, and the presence of aggregates in heated solutions was confirmed by static light scattering. Interaction of Nile red with β-galactosidase aggregates led to a shift of the emission maximum (λmax) from 660 to 611 nm, and to an increase of fluorescence intensity. Time-resolved fluorescence and fluorescence correlation spectroscopy (FCS) measurements showed that Nile red detected large aggregates with hydrodynamic radii around 130 nm. By steady-state fluorescence measurements, it was possible to detect 1 nM of denatured and aggregated β-galactosidase in solution. The comparison with size exclusion chromatography (SEC) showed that native β-galactosidase and small aggregates thereof had no substantial effect on the fluorescence of Nile red. Large aggregates were not detected by SEC, because they were excluded from the column. The results with β-galactosidase demonstrate the potential of Nile red for developing complementary analytical methods that overcome the size limitations of SEC, and can detect the formation of large protein aggregates at early stages

Fluorescence correlation spectroscopy of the binding of nucleotide excision repair protein XPC-hHr23B with DNA substrates

The interaction of the nucleotide excision repair (NER) protein dimeric complex XPC-hHR23B, which is implicated in the DNA damage recognition step, with three Cy3.5 labeled 90-bp double-stranded DNA substrates (unmodified, with a central unpaired region, and cholesterol modified) and a 90-mer single-strand DNA was investigated in solution by fluorescence correlation spectroscopy. Autocorrelation functions obtained in the presence of an excess of protein show larger diffusion times (τ d) than for free DNA, indicating the presence of DNA-protein bound complexes. The fraction of DNA bound (θ), as a way to describe the percentage of protein bound to DNA, was directly estimated from FCS data. A significantly stronger binding capability for the cholesterol modified substrate (78% DNA bound) than for other double-stranded DNA substrates was observed, while the lowest affinity was found for the single-stranded DNA (27%). This is in accordance with a damage recognition role of the XPC protein. The similar affinity of XPC for undamaged and 'bubble' DNA sub

Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo

Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated data sets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS data sets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely, excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions, physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in nonphysical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells, and in living organisms.National Science Foundation (U.S.). Physics of Living Systems ProgramNational Institute of Mental Health (U.S.) (Award U01MH106011

SIMULATION BASED EVALUATION OF THE WORKLOAD CONTROL CONCEPT FOR A COMPANY OF THE AUTOMOBILE INDUSTRY

This paper describes a simulation study conducted for a company of the German automobile supply industry facing the need to improve delivery reliability. The intention of the study was to evaluate whether Workload Control (WLC) is applicable as production control policy for this company and whether improvement can be expected. Therefore the regarded shop floor was modeled being organized as a WLC production system. Both, the structural and quantitative model components of the developed simulation model are explained in depth. Furthermore it is shown how inherent parameters of the WLC concept can be set using the simulation model in a practical environment. As due date compliance is the primary concern of the company, the performance of four simple priority dispatching rules is analyzed with regard to delivery reliability. As a result it is shown that WLC is applicable in the given situation and that performance enhancements can be expected.