From Sequence to Dynamics to Function: Computational Design of Allostery and Ligand Selectivity in G-Protein Coupled Receptors

The phenomenon of allostery, a general property in proteins that has been heralded as "the second secret of life" remains elusive to our understanding and even more challenging to incorporate into protein design. One example of allosteric proteins with great therapeutic potential are G-Protein coupled receptors (GPCRs). GPCRs play a crucial role in regulating numerous physiological reactions triggered by neurotransmitters, hormones, and various environmental stimuli. As a result, GPCRs are targets for nearly one-third of all licensed pharmaceutical drugs. In this thesis, we present a framework to elucidate allosteric signaling applied to GPCRs as model systems. The framework is based on analyzing dynamics in proteins modeled via molecular simulations to (1) extract potential allosteric pathways in the protein and (2) quantify protein response to a perturbation. We employ computational protein design coupled with the aforementioned dynamic analysis to explore the allosteric functions of GPCRs, unveiling mechanistic relationships between agonist ligand chemistry, receptor sequence, structure, dynamics, and allosteric signaling across the dopamine receptor family. The framework is also applied to designed signaling complexes between conformationally dynamic proteins and peptides in chemokine receptors to shed light on the change of allosteric pathways in response to the designs. This work is a step forward toward mechanistic understanding of sequence polymorphism on receptor function and pharmacology, providing valuable insights for selective drug design and rational receptor engineering for both fundamental research and therapeutic applications.