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Structure determination of high-energy states in a dynamic protein ensemble

Nature volume 603, pages 528–535 (2022)Cite this article

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Abstract

Macromolecular function frequently requires that proteins change conformation into high-energy states1,2,3,4. However, methods for solving the structures of these functionally essential, lowly populated states are lacking. Here we develop a method for high-resolution structure determination of minorly populated states by coupling NMR spectroscopy-derived pseudocontact shifts5 (PCSs) with Carr–Purcell–Meiboom–Gill (CPMG) relaxation dispersion6 (PCS–CPMG). Our approach additionally defines the corresponding kinetics and thermodynamics of high-energy excursions, thereby characterizing the entire free-energy landscape. Using a large set of simulated data for adenylate kinase (Adk), calmodulin and Src kinase, we find that high-energy PCSs accurately determine high-energy structures (with a root mean squared deviation of less than 3.5 angström). Applying our methodology to Adk during catalysis, we find that the high-energy excursion involves surprisingly small openings of the AMP and ATP lids. This previously unresolved high-energy structure solves a longstanding controversy about conformational interconversions that are rate-limiting for catalysis. Primed for either substrate binding or product release, the high-energy structure of Adk suggests a two-step mechanism combining conformational selection to this state, followed by an induced-fit step into a fully closed state for catalysis of the phosphoryl-transfer reaction. Unlike other methods for resolving high-energy states, such as cryo-electron microscopy and X-ray crystallography, our solution PCS–CPMG approach excels in cases involving domain rearrangements of smaller systems (less than 60 kDa) and populations as low as 0.5%, and enables the simultaneous determination of protein structure, kinetics and thermodynamics while proteins perform their function.

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Fig. 1: Paramagnetic enhanced NMR for structure determination of a high-energy state during catalysis.
Fig. 2: Maximum-likelihood classification method for high-energy structure determination.
Fig. 3: The high-energy state of Adk during enzyme catalysis.
Fig. 4: General applicability of PCS–CPMG methodology.

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Data availability

The NMR assignments of G. stearothermophilus adenylate kinase in its Zn2+- and Co2+-bound states have been deposited in the BioMagResBank74 under accession codes 51232 and 51233, respectively. Peak lists for the tagged ubiquitin variants can be obtained from https://github.com/kernlab-brandeis/PCS-CPMG. All relevant data are available from the corresponding author upon request.

Code availability

Any relevant code is available by request from the corresponding author. A general script for the Expectation Maximization algorithm and test datasets are made available at https://github.com/kernlab-brandeis/PCS-CPMG.

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Acknowledgements

We thank I. Hertel-Hering and M. Rogowski for expression of the K6C and S20C ubiquitin constructs, respectively; and S. Hiller for the trigger factor expression plasmid. This work was supported by the Howard Hughes Medical Institute (HHMI) to D.K., the NIH (R01GM121384 and R01GM132499) to D.L.T., and the Department of Chemistry at University of Basel to D.H. Computational resources were provided by NSF XSEDE computing resources. We acknowledge computational support from the Brandeis HPCC which is partially supported by the NSF through DMR-MRSEC 2011846 and OAC-1920147.

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Authors and Affiliations

  1. Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, MA, USA

    John B. Stiller, Renee Otten & Dorothee Kern

  2. Department of Chemistry, University of Basel, Basel, Switzerland

    Daniel Häussinger & Pascal S. Rieder

  3. Department of Biochemistry, Brandeis University, Waltham, MA, USA

    Douglas L. Theobald

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  1. John B. Stiller
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Contributions

D.K. conceived the project idea. D.K. and J.B.S. developed the research plan and experimental strategy. J.B.S. purified Adk, performed NMR experiments, and analysed results. D.H. and P.S.R. synthesized lanthanide-binding tags and prepared tagged ubiquitin samples. R.O. purified PPD–SBD proteins and prepared lanthanide-bound samples. R.O. performed the NMR experiments for ubiquitin and PPD–SBD trigger factor. R.O., J.B.S., D.K., P.S.R. and D.H. analysed results from the NMR experiments. D.L.T. designed the expectation maximization algorithm. J.B.S. implemented the algorithm into the XPLOR-NIH software and performed calculations on simulated and real data. All authors discussed results leading to overall scientific findings. J.B.S. and D.K. wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Dorothee Kern.

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Extended data figures and tables

Extended Data Fig. 1 Diamagnetic and paramagnetic samples were assigned by triple resonance experiments.

Backbone assignment of Zn2+ (left) and Co2+ (right) Adk proteins under saturating nucleotide conditions plotted onto Ap5A bound crystal structure, PDB 4QBH26. Orange spheres depict residues where no assignment was determined. Prolines are shown as gray sticks. All other amides are assigned. For the cobalt species, many residues surrounding the metal binding site are lost due to either Curie relaxation or exchange on the intermediate timescale.

Extended Data Fig. 2 PCSs in different ligand states reflect the conformational state of Adk.

Fits of PCSs extracted from [1H-15N]-HSQC spectra in either 20 mM Mg2+ADP (a), 20 mM ADP (b), and apo (c) to the closed crystal structure (PDB 4QBH26). Notably, the Mg2+/ADP data fits reasonably well (Q = 11.6%), but reports on PCSs from both the closed crystal structure and the minor state (i.e., population averaged). In the absence of magnesium, the open/closing exchange rate is in the slow time regime, leading to PCSs that better fit the closed state (Q = 7.6%). The apo PCSs report on a more open state, and, therefore, fit significantly worse (Q = 47.9%) to the closed state. (d) Calculating tensors using either paramagnetic-induced RDCs (left) or PCSs (right) provide similar tensor values, indicating little ps-ns motion of the paramagnet.

Extended Data Fig. 3 In the absence of magnesium, the open/closed rate exchange rate is in the slow exchange regime, in agreement with earlier reports25.

(a) [1H, 15N]-HSQC spectra for Zn2+ and Co2+ samples with either 20 mM ADP or 20 mM Mg2+ADP. Residues that were broadened in the Mg2+ADP sample show reduced linewidths in the ADP sample, indicating a shift in exchange timescales from intermediate to slow exchange. Black lines trace the PCSs between diamagnetic and paramagnetic samples. For residue 38, the corresponding diamagnetic peaks are at 8.46 ppm (1H) and 123.5 ppm (15N) and are not shown for illustration purposes. (b) 1HN CPMG dispersion profiles for Co2+ Adk with 20 mM ADP and 0 mM Mg2+. Representative traces show slow exchange that is fit to the Tollinger equation75 (F-statistics were used to determine whether the description by a slow exchange model compared to a “no-exchange” model was justified at the 95% confidence interval; p>0.05). (c) Representative CPMG relaxation dispersion profiles for residues in the presence on 20 mM Mg2+ADP. Notably, the paramagnetic chemical shift differences in the absence and presence of magnesium are similar, whereas the timescale is significantly altered (kopen, ADP = 2.6 ± 0.3 s−1 vs kopen, ADP = 180 ± 36 s−1). Uncertainties (s.d.) in R2,eff are determined from the rmsd in the intensities of duplicate points (n = 3) according to the definition of pooled relative standard deviation. Uncertainties (s.d.) in chemical shift differences were calculated from the covariance matrix.

Extended Data Fig. 4 Comparison of PCS values determined during apo and turnover conditions and correlation of PCSs during turnover conditions with the open structure of Adk.

(a) Overlay of PCS values obtained for apo and Mg2+ADP conditions. Values were determined from [1H-15N]-HSQC comparison in Zn2+ and Co2+ states. Note the sizable loss of PCS in the apo state compared to the closed state, indicating a more open structure in the absence of ligand. (b) Difference in PCS values for apo and turnover conditions. Large absolute differences of > 0.1 ppm are observed for many residues. (c) Zoom in of [1H-15N]-HSQC spectra in either 20 mM Mg2+ADP or apo conditions of Co2+ Adk. Noticeable line broadening is observed for apo conditions. (d) Fit of open state (4AKE68) to observed PCS shift data during catalytic turnover. (e) Best-fit tensor for PCS to open state structure. (f) Calculated PCSs for open state structure when fit with observed PCSs. A poor fit is found as the observed PCSs do not report on the open state structure. (g) The PCS difference expected between the open and closed state structures. Differences of |0.5 ppm| or greater would be expected for residues in AMP lid and core domain near the ATP lid.

Extended Data Fig. 5 Expectation-maximization during simulated annealing leads to correct PCS identifications for nearly all residues.

All 12 structures are shown with incorrectly chosen PCS as gray spheres. Mistakes usually occur near the end of secondary structure units, where local differences between crystal structures are most prevalent. For each structure, the core domain, ATP-lid, and AMP-lid are colored in wheat, red, and blue, respectively. The cobalt metal is shown as a green sphere.

Extended Data Fig. 6 Starting from an open or closed starting structure of Adk in the PCS-CPMG maximum-likelihood calculations results in equivalent final Adk structures.

(a) Open (lighter colors) and closed (darker colors) crystal structures, PDBs 4AKE68 and 4QBH26, respectively. (b) RMSD, Co2+-Core distance, and AMP-lid angle results for four calculated structures, all started from the open state (PDB 4AKE68). Similar to starting from the closed state (Fig. 2f), starting from an open state result in excellent collective variables, and excellent agreement with the experimental structures (shown as black stars). (c) Alignment of inferred structures starting from a closed state (blue) and open state (red) compared to the target structure (gray), highlighting that the new method results in converged and accurate structures independent of the starting model.

Extended Data Fig. 7 Plots of likelihood versus RMSD to the target structures for the first simulated annealing run for all 12 simulated Adk structures.

Structures which possess relatively low likelihood also have higher RMSD.

Extended Data Fig. 8 Eight PCS choices possible in the case where CPMG is performed in the presence of one diamagnetic and two paramagnetic metals.

As the diamagnetic sign is shared between the two paramagnetic dispersions, choices 1/3 and 2/4 are linked. This reduces the possible PCS choice for a specific residue.

Extended Data Fig. 9 Expectation-maximization of ambiguous PCSs are solved during structural calculations for calmodulin and src kinase.

PCSs of the final structures for calmodulin (a) and src kinase (b) have low Q values for both metals as well as accurate PCS identifications (blue stars for the correct PCS identification, red spheres are incorrect PCS identifications, dark blue line indicates the calculated PCS).

Extended Data Fig. 10 Ubiquitin and the chaperone trigger factor support lanthanide-binding tag coordination and produce substantial paramagnetic tensors.

(a) [1H-15N]-HSQC spectra of ubiquitin mutants S20C (left) and K6C (middle, right) bound with either the DOTA-M7PyThiazole (left, middle) or DOTA-M8-(4R4S)-SSPy(right) lanthanide-binding tags. Each spectrum shows large PCSs induced by Tm3+ bound tags. (b) Correlation plots between each ubiquitin variant’s PCSs and the calculated PCSs. (c) [1H-15N]-HSQC spectra of WT PPD-SBD, V270C PPD-SBD, M8-Lu-V270C PPD-SBD, and M8-Tm-V270C PPD-SBD. (d) Zoom-in of center section of the [1H-15N]-HSQC spectra of (c) showing nearly identical spectra for each sample. (e) Correlation plots between each M8-Tm-V270C PPD-SBD PCSs and the calculated PCSs.

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Stiller, J.B., Otten, R., Häussinger, D. et al. Structure determination of high-energy states in a dynamic protein ensemble. Nature 603, 528–535 (2022). https://doi.org/10.1038/s41586-022-04468-9

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