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- Habchi, Johnny, et al.
(författare)
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An anticancer drug suppresses the primary nucleation reaction that initiates the production of the toxic Aβ42 aggregates linked with Alzheimer's disease.
- 2016
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Ingår i: Science Advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 2:2
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Tidskriftsartikel (refereegranskat)abstract
- The conversion of the β-amyloid (Aβ) peptide into pathogenic aggregates is linked to the onset and progression of Alzheimer's disease. Although this observation has prompted an extensive search for therapeutic agents to modulate the concentration of Aβ or inhibit its aggregation, all clinical trials with these objectives have so far failed, at least in part because of a lack of understanding of the molecular mechanisms underlying the process of aggregation and its inhibition. To address this problem, we describe a chemical kinetics approach for rational drug discovery, in which the effects of small molecules on the rates of specific microscopic steps in the self-assembly of Aβ42, the most aggregation-prone variant of Aβ, are analyzed quantitatively. By applying this approach, we report that bexarotene, an anticancer drug approved by the U.S. Food and Drug Administration, selectively targets the primary nucleation step in Aβ42 aggregation, delays the formation of toxic species in neuroblastoma cells, and completely suppresses Aβ42 deposition and its consequences in a Caenorhabditis elegans model of Aβ42-mediated toxicity. These results suggest that the prevention of the primary nucleation of Aβ42 by compounds such as bexarotene could potentially reduce the risk of onset of Alzheimer's disease and, more generally, that our strategy provides a general framework for the rational identification of a range of candidate drugs directed against neurodegenerative disorders.
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| 2. |
- Meisl, Georg, et al.
(författare)
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Molecular mechanisms of protein aggregation from global fitting of kinetic models.
- 2016
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Ingår i: Nature Protocols. - : Springer Science and Business Media LLC. - 1750-2799 .- 1754-2189. ; 11:2, s. 252-272
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Tidskriftsartikel (refereegranskat)abstract
- The elucidation of the molecular mechanisms by which soluble proteins convert into their amyloid forms is a fundamental prerequisite for understanding and controlling disorders that are linked to protein aggregation, such as Alzheimer's and Parkinson's diseases. However, because of the complexity associated with aggregation reaction networks, the analysis of kinetic data of protein aggregation to obtain the underlying mechanisms represents a complex task. Here we describe a framework, using quantitative kinetic assays and global fitting, to determine and to verify a molecular mechanism for aggregation reactions that is compatible with experimental kinetic data. We implement this approach in a web-based software, AmyloFit. Our procedure starts from the results of kinetic experiments that measure the concentration of aggregate mass as a function of time. We illustrate the approach with results from the aggregation of the β-amyloid (Aβ) peptides measured using thioflavin T, but the method is suitable for data from any similar kinetic experiment measuring the accumulation of aggregate mass as a function of time; the input data are in the form of a tab-separated text file. We also outline general experimental strategies and practical considerations for obtaining kinetic data of sufficient quality to draw detailed mechanistic conclusions, and the procedure starts with instructions for extensive data quality control. For the core part of the analysis, we provide an online platform (http://www.amylofit.ch.cam.ac.uk) that enables robust global analysis of kinetic data without the need for extensive programming or detailed mathematical knowledge. The software automates repetitive tasks and guides users through the key steps of kinetic analysis: determination of constraints to be placed on the aggregation mechanism based on the concentration dependence of the aggregation reaction, choosing from several fundamental models describing assembly into linear aggregates and fitting the chosen models using an advanced minimization algorithm to yield the reaction orders and rate constants. Finally, we outline how to use this approach to investigate which targets potential inhibitors of amyloid formation bind to and where in the reaction mechanism they act. The protocol, from processing data to determining mechanisms, can be completed in <1 d.
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