Using GANs for De Novo Protein Design Targeting Microglial IL-3R$\alpha$ to Inhibit Alzheimer's Progression

TL;DR

This study employs GANs and machine learning to design novel proteins targeting IL-3R, aiming to develop potential drugs that activate microglia and inhibit Alzheimer's progression by analyzing binding interactions.

Contribution

It introduces a de novo protein design approach using GANs to create novel IL-3 variants with potential therapeutic effects against Alzheimer's disease.

Findings

Designed proteins with similar secondary structures to IL-3 showed better binding in simulations.

Machine learning enabled the generation of novel protein structures for targeted binding.

Docking results identified promising candidates for further experimental validation.

Abstract

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.