In the near future, this study can be continued from two aspects. First, it will be important to confirm that Ab aggregates conformational change induced by piceatannol can alter the cytotoxicity of Ab aggregates. To examine this possibility, a caspase-3/7 assay will be employed to assess the toxicity of Ab aggregates formed in the presence or absence of piceatannol. Caspase-3/7 assay measures caspase-3/7 activities, which are indicative of the level of cell apoptosis. In addition, a satisfactory explanation for the binding propensity differences between control Ab aggregates and four types of lipid bilayers observed in Chapter 5 is needed. Specifically, we will examine the hypothesis of a phase transition occurring for lipid bilayers with 70 % POPC and 30 % DPPC at 40 °C. Here, binding experiments will be performed at a lower temperature, where lipid bilayers with higher fluidities (POPC/DPPC, 90/10 and 80/20) are expected to undergo a similar phase transition and thus exhibit a higher binding propensity.
The desire to discover biological agents that can inhibit or interfere with Aβ aggregation, the pathological process of AD, has driven extensive research in evaluating such effects of both natural and synthetic compounds, for over two decades. The numerous inhibitors that have been examined to date range from small molecules, peptides and peptideomimetics to nanoparticles. These past endeavors have accumulated a great amount
of valuable experimental data relating compounds to their capabilities of binding, interfering and inhibiting diverse Aβ aggregated species. However, each individual study could only assess a few compounds and deduce structure-activity relationships accordingly, since performing such investigations requires intensive laboratory work, high cost for materials and equipment, as well as, a long experimental time frame. Efforts have been made to manually summarize results from different studies in order to find common chemical structures that are important for inhibiting aggregation [103,104] and aid the design of effective inhibitors, but the result is far from satisfactory. It is probably because the number of parameters to describe the properties of compounds and Aβ aggregates is too large for humans to recognize underlying patterns and understand the causative mechanisms directly. Based on the fact that we have access to a large number of valuable experimental data for both positive inhibitors and negative inhibitors, in the future, it is feasible to develop a machine learning pipeline that, given a new compound, outputs its predicted possibility of being a positive inhibitor and the predicted effectiveness. The design of this pipeline would include data collection and clean, feature extraction and transformation, classification model training, model performance evaluation and finally prediction. This pipeline would help researchers to quickly screen a large number of novel compounds and narrow the scope of experimental evaluation to the compounds that are most likely to be positive inhibitors. As a result, it will increase the successful rates for identifying effective inhibitors and save a great amount of time and money.
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