Alzheimer's disease (AD) is the most common form of dementia and the sixth leading cause of death in the United States. Plaques composed of aggregated amyloid-beta protein (Aβ) accumulate between the neural cells in the brain and are associated with dementia and cellular death. Many strategies have been investigated to prevent Aβ self-assembly into disease-associated β-sheet amyloid aggregates; however, a promising therapeutic has not yet been identified. In this study, a peptoid-based mimic of the peptide KLVFF (residues 16-20 of Aβ) was tested for its ability to modulate Aβ aggregation. Peptoid JPT1 includes chiral, aromatic side chains to induce formation of a stable helical secondary structure that allows for greater interaction between the aromatic side chains and the cross β-sheet of Aβ. JPT1 was found to modulate Aβ40 aggregation, specifically decreasing lag time to β-sheet aggregate formation as well as the total number of fibrillar, β-sheet structured aggregates formed. These results suggest that peptoids may be able to limit the formation of Aβ aggregates that are associated with AD.
Peptoids are a versatile family of oligomeric synthetic molecules that can be customized for many applications. The submonomer solid-phase synthesis of peptoids allows for quick and inexpensive manufacturing and the selection of side chains is nearly limitless. In addition, peptoids that include chiral, aromatic side chains form stable helical secondary structure that leads to the potential for the formation of supramolecular assemblies. The effects of water solubility, helical content, charge placement, and side chain bulk on microsphere formation were studied for seven peptoid sequences. We found that secondary structure and partial water solubility were essential for microsphere formation. In addition, charge placement and side chain bulk affect both the ability to form microspheres and the diameter of the microspheres.
Enzyme-linked immunosorbent assay (ELISA) microarrays promise to be a powerful tool for the detection and validation of disease biomarkers. ELISA microarrays are capable of simultaneous detection of many proteins using a small sample volume. Although there are many potential pitfalls to the use of ELISA microarrays, these can be avoided by careful planning of experiments. In this chapter we describe a high-throughput protocol for processing ELISA microarrays that will result in reliable and reproducible data.
Peptoids that are helical and partially water soluble have been shown to self-assemble into microspheres when the peptoid solution is dried on a silicon substrate. Such microsphere coatings have great potential for use in biosensor technologies, specifically to increase the surface area for binding. However, in order to be useful, the peptoids must consistently form uniform coatings. In this study we investigated the effects of various coating protocol parameters on the uniformity of the resulting peptoid microsphere coatings, including (i) solvent, (ii) administration technique, and (iii) drying environment. In addition, we investigated the robustness of the coatings as well as the potential for using a glass substrate. These studies show that uniform, robust peptoid microsphere coatings can be formed using protic solvents, a full coverage administration technique, and drying in open air on silicon or glass substrates.
Our research group has been developing enzyme-linked immunosorbent assays (ELISA) microarray technology for the rapid and quantitative evaluation of biomarker panels. Studies using antibody microarrays are susceptible to systematic bias from the various steps in the experimental process, and these biases can mask biologically significant differences. For this reason, we have developed a calibration system that can identify and reduce systematic bias due to processing factors. Specifically, we developed a sandwich ELISA for green fluorescent protein (GFP) that is included on each chip. The GFP antigen is spiked into each biological sample or standard mixture and the resulting signal is used for calibration between chips. We developed ProMAT Calibrator, an open-source bioinformatics tool, for the rapid visualization and interpretation of the calibrator data and, if desired, data normalization. We demonstrate that data normalization using this system markedly reduces bias from processing factors. Equally useful, this calibrator system can help reveal the source of the bias, thereby facilitating the elimination of the underlying problem. ProMAT Calibrator can be downloaded at http://www.pnl.gov/statistics/ProMAT .
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