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Integrating energy calculations with functional assays to decipher the specificity of G protein–RGS protein interactions

Nature Structural & Molecular Biology volume 18, pages 846–853 (2011)Cite this article

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Abstract

The diverse Regulator of G protein Signaling (RGS) family sets the timing of G protein signaling. To understand how the structure of RGS proteins determines their common ability to inactivate G proteins and their selective G protein recognition, we combined structure-based energy calculations with biochemical measurements of RGS activity. We found a previously unidentified group of variable 'Modulatory' residues that reside at the periphery of the RGS domain–G protein interface and fine-tune G protein recognition. Mutations of Modulatory residues in high-activity RGS proteins impaired RGS function, whereas redesign of low-activity RGS proteins in critical Modulatory positions yielded complete gain of function. Therefore, RGS proteins combine a conserved core interface with peripheral Modulatory residues to selectively optimize G protein recognition and inactivation. Finally, we show that our approach can be extended to analyze interaction specificity across other large protein families.

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Figure 1: The GAP activities of ten representative RGS domains are not correlated with their subfamily classification.
Figure 2: Positions of Significant & Conserved and Modulatory residues in multiple RGS proteins.
Figure 3: Mutations in Modulatory positions impair the GAP activities of RGS4 and RGS16 in an additive manner.
Figure 4: Redesign of RGS17 gain-of-function mutants.
Figure 5: Redesign of RGS18 gain-of-function mutants.
Figure 6: Residues contributing substantially to colicin E7–immunity protein interactions.
Figure 7: Positions of Significant & Conserved and Modulatory residues in the Gα subunits interacting with RGS domains.

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Acknowledgements

This research was supported by US National Institutes of Health grants EY012859 (V.Y.A.) and GM082892 (D.P.S.), by a core grant for vision research to Duke University (EY5722), by the US National Science Foundation through TeraGrid resources (TG-MCB080085T; M.K.) and by a long-term postdoctoral fellowship from the Human Frontier Science Program (M.K.). We thank the Duke Shared Cluster Resource and the San Diego Supercomputer Center for computational resources, S.A. Baker, S. Farsiu, N.P. Skiba, E.S. Lobanova and D. Reichmann for helpful suggestions, B. Honig for insightful guidance (M.K.) and F. Sheinerman, R. Rohs, S. Fleishman and E. Alexov for helpful discussions.

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Authors and Affiliations

  1. Duke Eye Center, Duke University Medical Center, Durham, North Carolina, USA

    Mickey Kosloff, Amanda M Travis & Vadim Y Arshavsky

  2. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Dustin E Bosch & David P Siderovski

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  1. Mickey Kosloff
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M.K. designed and carried out computational analysis and biochemical experiments, analyzed data and prepared the manuscript, A.M.T. carried out experiments and prepared the manuscript, D.E.B. carried out experiments and prepared the manuscript, D.P.S. supervised the project and prepared the manuscript and V.Y.A. supervised the project and analysis and prepared the manuscript.

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Correspondence to Vadim Y Arshavsky.

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Kosloff, M., Travis, A., Bosch, D. et al. Integrating energy calculations with functional assays to decipher the specificity of G protein–RGS protein interactions. Nat Struct Mol Biol 18, 846–853 (2011). https://doi.org/10.1038/nsmb.2068

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