Computational and Rational Stabilization of Toll-Like Receptors for the Development of Novel Tools

Abstract

Toll-like receptors (TLRs) are membrane-bound pattern recognition receptors essential for innate immune sensing, but their large, glycosylated extracellular domains and intrinsic instability make them notoriously difficult to express and purify recombinantly. These challenges have historically limited structural and functional studies, as well as the development of therapeutic reagents targeting TLRs. This dissertation presents a computational design framework for stabilizing and functionally interrogating TLRs, enabling the development of synthetic immunomodulatory tools. Using AI-guided design with ProteinMPNN, AlphaFold2 and RosettaFold Diffusion in addition to physics and rational approaches, we generated stabilized and expressible variants of TLR2 and TLR5, facilitating downstream applications including de novo minibinder generation and antibody development. Overall, this work highlights a generalizable approach to stabilizing immune receptors and advancing rational immunotherapy design.