bioRxivBiophysics
Computationally designed GPCR quaternary structures bias signaling pathway activation
J. S. Paradis, X. Feng, B. Murat, R. E. Jefferson, M. E. Szpakowska, M. Hogue, N. D. Bergkamp, F. M. Heidenreich, M. J. Smit, A. Chevigne, M. Bouvier, P. Barth
Abstract
Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can now be readily characterized, receptor associations into quaternary structures are very challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting membrane receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed stabilities, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only a few specific dimer structures also recruited {beta}-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a conformational switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs {beta}-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.
- organism: human
- assay: proteomics
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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