Using GANs for De Novo Protein Design Targeting Microglial IL-3R$\alpha$ to Inhibit Alzheimer's Progression
<|reference_start|>Using GANs for De Novo Protein Design Targeting Microglial IL-3R$\alpha$ to Inhibit Alzheimer's Progression: 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.<|reference_end|>
@article{swaroop2024using,
    title={Using GANs for De Novo Protein Design Targeting Microglial IL-3R$\alpha$
  to Inhibit Alzheimer's Progression},
    author={Arnav Swaroop},
    journal={arXiv preprint arXiv:2405.06651},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06651},
    primaryClass={q-bio.BM cs.LG q-bio.NC}
}