Amyloidogenic peptides of yeast cell wall glucantransferase Bgl2p as a model for the investigation of its pH-dependent fibril formation

Lomonosov Moscow State University
Prion (Impact Factor: 2.24). 12/2012; 7(2). DOI: 10.4161/pri.22992
Source: PubMed


The pH-dependence of the ability of Bgl2p to form fibrils was studied using synthetic peptides with potential amyloidogenic determinants (PADs) predicted in the Bgl2p sequence. Three PADs, FTIFVGV, SWNVLVA and NAFS, were selected on the basis of combination of computational algorithms. Peptides AEGFTIFVGV, VDSWNVLVAG and VMANAFSYWQ, containing these PADs, were synthesized. It was demonstrated that these peptides had an ability to fibrillate at pH values from 3.2 to 5.0. The PAD-containing peptides, except for VDSWNVLVAG, could fibrillate also at pH values from pH 5.0 to 7.6. We supposed that the ability of Bgl2p to form fibrils most likely depended on the coordination of fibrillation activity of the PAD-containing areas and Bgl2p could fibrillate at mild acid and neutral pH values and lose the ability to fibrillate with the increasing of pH values. It was demonstrated that Bgl2p was able to fibrillate at pH value 5.0, to form fibrils of various morphology at neutral pH values and lost the fibrillation ability at pH value 7.6. The results obtained allowed us to suggest a new simple approach for the isolation of Bgl2p from Saccharomyces cerevisiae cell wall.

8 Reads
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The tendency of amyloid beta (Aβ42) peptide to misfold and aggregate into insoluble amyloid fibrils in Alzheimer's disease (AD) has been well documented. Accumulation of Aβ42 fibrils has been correlated with abnormal apoptosis and unscheduled cell division which can also trigger the death of neuronal cells, while oligomers can also exhibit similar activities. While investigations using chemically-synthesised Aβ42 peptide have become common practice, there appear to be differences in outcomes from different preparations. In order to resolve this inconsistency, we report two separate methods of preparing chemically-synthesized Aβ42 and we examined their effects in yeast. Hexafluoroisopropanol pretreatment caused toxicity while, ammonium hydroxide treated Aβ42 induced cell proliferation in both C. glabrata and S. cerevisiae. The hexafluoroisopropanol prepared Aβ42 had greater tendency to form amyloid on yeast cells as determined by thioflavin T staining followed by flow cytometry and microscopy. Both quiescent and non-quiescent cells were analysed by these methods of peptide preparation. Non-quiescent cells were susceptible to the toxicity of Aβ42compared with quiescent cells (p < 0.005). These data explain the discrepancy in the previous publications about the effects of chemically-synthesized Aβ42 on yeast cells. The effect of Aβ42 on yeast cells was independent of the size of the peptide aggregates. However, the Aβ42 pretreatment determined whether the molecular conformation of peptide resulted in proliferation or toxicity in yeast based assays.
    Prion 12/2014; 8(6). DOI:10.4161/19336896.2014.992275 · 2.24 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This chapter describes computational approaches to study amyloid formation. The first part addresses identification of potential amyloidogenic regions in the amino acid sequences of proteins and peptides. Next, we discuss nucleation and aggregation sites in protein folding and misfolding. The last part describes up-to-date kinetic models of amyloid fibrils formation. Numerous studies show that protein misfolding is initiated by specific amino acid segments with high amyloid-forming propensity. The ability to identify and, ultimately, block such segments is very important. To this end, many prediction algorithms have been developed which vary greatly in their effectiveness. We compared the predictions for 30 proteins by using different methods and found that, at best, only 50 % of residues in amyloidogenic segments were predicted correctly. The best results were obtained by using the meta-servers that combine several independent approaches, and by the method PASTA2. Thus, correct prediction of amyloidogenic segments remains a difficult task. Additional data and new algorithms that are becoming available are expected to improve the accuracy of the prediction methods, particularly if they use 3D structural information on the target proteins. At the same time, our understanding of the kinetics of fibril formation is more advanced. The current kinetic models outlined in this chapter adequately describe the key features of amyloid nucleation and growth. However, the underlying structural details are less clear, not least because of the apparently different mechanisms of amyloid fibril formation which are discussed. Ultimately, the detailed understanding of the structural basis for amyloidogenesis should help develop rational therapies to block this pathogenic process.
    Advances in Experimental Medicine and Biology 07/2015; 855:213-239. DOI:10.1007/978-3-319-17344-3_9 · 1.96 Impact Factor