IL-3 is a hemopoietic growth factor that usually targets blood cell precursors; IL-3R is a cytokine receptor that binds to IL-3. However, IL-3 takes on a different role in the context of glial cells in the nervous system, where studies show that the protein IL-3 protects against Alzheimer's disease by activating microglia at their IL-3R receptors, causing the microglia to clear out the tangles caused by the build-up of misfolded Tau proteins. In this study, we seek to ascertain what role the secondary structure of IL-3 plays in its binding with the receptor. The motivation behind this study is to learn more about the mechanism and identify possible drugs that might be able to activate it, in hopes of inhibiting the spread of Alzheimer's Disease. From a preliminary analysis of complexes containing IL-3 and IL-3R, we hypothesized that the binding is largely due to the interactions of three alpha helix structures stretching towards the active site on the receptor. The original Il-3 protein serves as the control in this experiment; the other proteins being tested are generated through several types of computational de novo protein design, where machine learning allows for the production of entirely novel structures. The efficacy of the generated proteins is assessed through docking simulations with the IL-3R receptor, and the binding poses are also qualitatively examined to gain insight into the function of the binding. From the docking data and poses, the most successful proteins were those with similar secondary structure to IL-3.

通过活化微胶质细胞上的IL-3R受体，该研究表明IL-3能够保护免受阿尔茨海默病的侵害，清除由错误折叠的Tau蛋白引起的纠缠物。研究旨在确定IL-3的次级结构在其与受体的结合中的作用，以期寻找激活它的可能药物，并抑制阿尔茨海默病的传播。与IL-3R受体进行的对接模拟和结构分析显示，与IL-3具有类似次级结构的蛋白质具有最成功的结合效果。

利用生成对抗网络进行自发性蛋白质设计，针对微胶质细胞IL-3Rα以抑制阿尔茨海默病的进展