Simulation of Spontaneous G Protein Activation Reveals a New Intermediate Driving GDP Unbinding

Sukrit Singh,Xianqiang Sun,Kendall J Blumer,Gregory Bowman

doi:10.1016/j.bpj.2019.11.1798

Sukrit Singh, Xianqiang Sun + Show 2 more

Open Access

https://doi.org/10.1016/j.bpj.2019.11.1798

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Journal: Biophysical Journal	Publication Date: Feb 1, 2020
Citations: 1	License type: publisher-specific-oa

Affiliation: Washington University in St. Louis

Abstract

Activation of heterotrimeric G proteins is a key step in many signaling cascades. However, a complete mechanism for this process, which requires allosteric communication between binding sites that are ∼30 Å apart, remains elusive. We construct an atomically detailed model of G protein activation by combining three powerful computational methods: metadynamics, Markov state models (MSMs), and CARDS analysis of correlated motions. We uncover a mechanism that is consistent with a wide variety of structural and biochemical data. Surprisingly, the rate-limiting step for activation, GDP dissociation, correlates with tilting rather than translation of the GPCRbinding helix 5. β-Strands 1-3 and helix 1 emerge as hubs in the allosteric network that links conformational changes in the GPCR-binding site to disordering of the distal nucleotidebinding site and consequent GDP release. Our approach and insights provide foundations for understanding disease-implicated G protein mutants, illuminating slow events in allosteric networks, and examining unbinding processes with slow off-rates. Published in: Sun, X.∗, & Singh, S.∗, et al. Simulation of spontaneous G protein activation reveals a new intermediate driving GDP unbinding. eLife, 7, (2018).

Highlights

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Summary

Introduction

1566-Plat Simulation of Spontaneous G Protein Activation Reveals a New Intermediate Driving GDP Unbinding Sukrit Singh1, Xianqiang Sun1, Kendall J. We construct an atomically detailed model of G protein activation by combining three powerful computational methods: metadynamics, Markov state models (MSMs), and CARDS analysis of correlated motions. Our approach and insights provide foundations for understanding disease-implicated G protein mutants, illuminating slow events in allosteric networks, and examining unbinding processes with slow off-rates.

Results

Conclusion