Enhanced Sampling of Intracellular Protein Interactions with G-Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are primary targets of about 1/3 of currently marketed drugs. GPCRs can couple with specific subtypes of heterotrimeric G proteins and β-arrestins, followed by dissociation of the intracellular proteins to regulate various downstream signaling pathways in the cell. GPCR–G protein/arrestin interactions can be modulated by ligands that bind allosteric sites with divergent sequences and conformations. Allosteric modulators provide advantages over traditional agonists that often cause off-target side effects due to binding to conserved “orthosteric” sites. Furthermore, biased agonists shift conformational ensemble of GPCRs to preferentially bind certain G proteins or β-arrestins and selectively activate a subset of the downstream signaling pathways. The allosteric modulators and biased agonists have thus emerged as promising candidates of selective GPCR drugs. Recent fluorescence experiments showed that the lifetime of GPCR-G protein complexes determine the efficiency and selectivity of G protein coupling to GPCRs. However, these experiments are expensive, time-consuming, and limited to a small number of GPCR-G protein systems. On the other hand, computational modeling, in particular Molecular Dynamics (MD), is a promising approach to explore protein interactions with GPCRs. We hypothesize that quantitative characterization of the lifetime and dissociation kinetics of GPCR–G protein/arrestin complexes will facilitate drug design of GPCRs. However, it is extremely challenging to predict protein dissociation kinetics using existing MD simulation methods, due to the large system size and gap between simulation and biological timescales. Based on successes of the Protein-Protein Interaction–Gaussian accelerated MD (PPI-GaMD) and related simulation methods the PI’s lab has developed with prior NIGMS R01 support, we will address the following challenges: (1) Develop a new Selective Deep Boosted MD (SDBMD) method that integrates powerful enhanced sampling and Deep Learning to predict the dissociation kinetics and lifetime of protein complexes more efficiently and accurately. (2) Benchmark new simulation methods on model GPCR-G protein complexes with published experimental kinetics data. (3) Predict changes in the dissociation kinetics and lifetime of adenosine and muscarinic GPCR-G protein complexes upon binding of GPCR allosteric modulators and biased agonists, and validate simulation predictions with fluorescence and cellular functional assays through collaborations with world-leading experimentalists. We will develop an innovative computational tool for predicting protein dissociation kinetics and obtain a quantitative picture of the dissociation kinetics and lifetime of GPCR-G protein/arrestin complexes. The research will greatly facilitate computer-aided design of selective GPCR drugs. Our long-term goals are (1) to develop robust computational methodologies to quantitatively characterize biomolecular recognition in disease-associated cellular signaling pathways and (2) to design effective drug molecules targeting important GPCRs. Project Number: 1R35GM163770-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Yinglong Miao | Institution: UNIV OF NORTH CAROLINA CHAPEL HILL, CHAPEL HILL, NC | Award Amount: $470,963 | Activity Code: R35 | Study Section: Maximizing Investigators' Research Award B Study Section[MRAB] View on NIH RePORTER: https://reporter.nih.gov/project-details/11331430