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Discrimination between cyclic nucleotides in a cyclic nucleotide-gated ion channel

Nature Structural & Molecular Biology volume 30, pages 512–520 (2023)Cite this article

Subjects

Abstract

Cyclic nucleotide-gated ion channels are crucial in many physiological processes such as vision and pacemaking in the heart. SthK is a prokaryotic homolog with high sequence and structure similarities to hyperpolarization-activated and cyclic nucleotide-modulated and cyclic nucleotide-gated channels, especially at the level of the cyclic nucleotide binding domains (CNBDs). Functional measurements showed that cyclic adenosine monophosphate (cAMP) is a channel activator while cyclic guanosine monophosphate (cGMP) barely leads to pore opening. Here, using atomic force microscopy single-molecule force spectroscopy and force probe molecular dynamics simulations, we unravel quantitatively and at the atomic level how CNBDs discriminate between cyclic nucleotides. We find that cAMP binds to the SthK CNBD slightly stronger than cGMP and accesses a deep-bound state that a cGMP-bound CNBD cannot reach. We propose that the deep binding of cAMP is the discriminatory state that is essential for cAMP-dependent channel activation.

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Fig. 1: 2D crystallization of SthK CL-CNBD.
Fig. 2: Schematic of the experiment to probe the cN–CNBD interaction using AFM-SMFS.
Fig. 3: Binding kinetics of cAMP–CNBD and cGMP–CNBD.
Fig. 4: H-bond interactions between CNBD and cNs.
Fig. 5: A deep cAMP binding site after extended cAMP–CNBD bond formation.
Fig. 6: Kinetic model of cAMP and cGMP binding to the SthK CNBD.

Data availability

The source data files contain all data (force distribution histograms, H-bond distributions) necessary to interpret, verify and extend the presented work. In the absence of dedicated data repositories for raw data AFM force curves and MDS trajectories, and in light of the instructions needed to open these files in proprietary software (in the case of the AFM force curves) and the additional information (parameters and conditions) needed to understand and use the data, raw data AFM force curves and MDS trajectories can be received from S.S. (sis2019@med.cornell.edu) and H.G. (hgrubmu@gwdg.de), respectively, upon reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank M. Rangl for initial experiments. Work in the Scheuring laboratory was supported by grants from the National Institute of Health, National Center for Complementary and Integrative Health DP1AT010874 and National Institute of Neurological Disorders and Stroke R01NS110790, and by the Kavli Institute at Cornell. Work in the Nimigean laboratory was supported by the National Institute of Health (GM124451 to C.M.N.) and the American Heart Association (18POST33960309 to P.A.M.S.). Work in the Grubmüller laboratory was funded by the Max Planck Society and by the German Science Foundation (DFG), Excellence Strategy Grant MBExC 2067/1-390729940; computer time was provided by the Max Planck Computing and Data Facility. A.C.V. was additionally supported by the European Union’s Horizon 2020 Framework Programme for Research and Innovation, Specific Grant Agreement No. 945539 (Human Brain Project SGA3).

Author information

Author notes

  1. Andrea C. Vaiana

    Present address: Institute of Biophysics, National Research Council, Palermo, Italy

Authors and Affiliations

  1. Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA

    Yangang Pan, Philipp A. M. Schmidpeter, Crina M. Nimigean & Simon Scheuring

  2. Max Planck Institute for Multidisciplinary Sciences, Theoretical and Computational Biophysics Department, Goettingen, Germany

    Emmi Pohjolainen, Andrea C. Vaiana & Helmut Grubmüller

  3. Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA

    Crina M. Nimigean & Simon Scheuring

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Contributions

Y.P., C.M.N. and S.S. designed the experiments. P.A.M.S. expressed and purified the protein. Y.P. performed HS-AFM and AFM-SMFS experiments. Y.P. did the HS-AFM and AFM-SMFS data analysis. E.P., A.C.V. and H.G. performed and analyzed force probe MDS. Y.P., E.P., H.G. and S.S. wrote the paper. All authors edited the paper.

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Correspondence to Simon Scheuring.

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The authors declare no competing interests.

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Nature Structural & Molecular Biology thanks Baron Chanda and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editors: Florian Ullrich and Katarzyna Ciazynska, in collaboration with the Nature Structural & Molecular Biology team.

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Extended data

Extended Data Fig. 1 cNs-CNBD binding studies using microscale thermophoresis (MST).

a) and c) Microscale thermophoresis (MST) traces of cAMP-CNBD (a) and cGMP-CNBD (c) interactions, respectively. b) Binding curve of labeled CNBD with cAMP. Two binding phases were detected. The first had a KD of 0.4 ± 0.5 µM, and the second had a KD of 1.6 ± 1.1 µM. d) Binding curve of labeled CNBD with cGMP. The KD value of cGMP-CNBD was 3.3 ± 1.8 µM. The concentration of labeled His6-C-linker-CNBD was kept constant (50 nM), and the concentration of cNs was varied from 0.00305 µM to 100 µM.

Source data

Extended Data Fig. 2 Distributions of rupture forces of cAMP-CNBD or cGMP-CNBD bonds under various conditions.

a) Force distribution of cAMP-CNBD unbinding following 0.02 s bond formation at varying pulling velocity (top right in each graph). b) Force distribution of cAMP-CNBD unbinding following 1.00 s bond formation at varying pulling velocities (top right in each graph). The force distributions are fitted with bimodal Gaussian fits to extract the most probable rupture forces for binding state 1 (first peak) and binding state 2 (second peak), which are then used for Bell–Evans model fitting. c) Force distribution of cGMP-CNBD unbinding following 0.02 s bond formation at varying pulling velocities (top right in each graph). d) Force distribution of cGMP-CNBD unbinding following 1.00 s bond formation at varying pulling velocities (top right in each graph). e) and f) Distributions of rupture forces of cGMP-CNBD (e) and cAMP-CNBD (f) unbinding following different bond formation times and at 0.4 µm/s pulling velocity). Bimodal Gaussian fits are used to extract the number of events for each bond state, which is then used to calculate the probability of occurrence of binding state 2.

Source data

Extended Data Fig. 3 Deviations of rupture force distributions.

Theoretical, simulation and experimental unbinding force distributions (normalized by loading rates) of cAMP (left) and cGMP (right). The experimental unbinding force distributions are the distributions after 0.02 s bond formation time.

Source data

Extended Data Fig. 4 Number of bond ruptures as a function of bond-formation time for cAMP-CNBD (left) and cGMP-CNBD (right).

With increasing cN contact time, the frequency of unsuccessful (0 bond) force-distance cycles decreases, while the number of successful (1 bond) force-distance cycles increases. Concomitantly, a fraction of force-distance cycles reported multiple (2 or 3) binding events.

Source data

Extended Data Fig. 5 Markovian sequence analysis for the force spectroscopy experiments of cAMP-CNBD at 1 s contact time.

Markovian model fitting for 1 (gray line) and 2 (dashed line) bonds. koff and xβ values were derived from the Bell–Evans model fit to the canonical binding mode (see Fig. 5b). The most probable rupture force (Gaussian peak) and error (full width at half maximum of the Gaussian peak) at each loading rate was determined through Gaussian fitting of the corresponding histogram (total data points for all histograms, N = 1898, Extended Data Fig. 2b).

Extended Data Fig. 6 Comparison of SthK with CNG and HCN channels, with a focus on their cN binding pockets.

Top: cNs binding pocket of SthK (left, gray) and CNGA1 (right, orange) showing that the residues Y and F are swapped in CNGA1 channels compared with SthK. Bottom: Sequence alignment of SthK with CNG and HCN channels showing that Y357 and F365 identified by MDS to be crucial in cN discrimination in SthK are not conserved, whereas M369 is a conservative mutation and A370 is a semi-conservative mutation. cAMP interacts with Y357, M369 and A370, and cGMP interacts with F365.

Supplementary information

Source data

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Source Data Extended Data Fig. 1

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Pan, Y., Pohjolainen, E., Schmidpeter, P.A.M. et al. Discrimination between cyclic nucleotides in a cyclic nucleotide-gated ion channel. Nat Struct Mol Biol 30, 512–520 (2023). https://doi.org/10.1038/s41594-023-00955-3

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