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Activation mechanism of the human Smoothened receptor

  1. Prateek Bansal
  2. Soumajit Dutta
  3. Diwakar Shukla author has email address
  1. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
  2. Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
  3. Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
  4. Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801

Peer review process

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Consolidated peer review report (8 September 2022)

GENERAL ASSESSMENT

The rationale behind the study:

While the activation mechanisms of Class A and Class B GPCRs have been extensively studied, little emphasis has been placed on the study of the dynamics of class F receptors such as Smoothened (SMO). Hence it is still elusive which motifs may take part in the transition from inactive to active conformations. Understanding the underpinnings of receptor activation in terms of residue networks, and their modulation by allosteric modulators, could help rational drug design, such as for novel SMO antagonists for cancer treatment.

Key findings and major conclusions:

Bansal, Dutta and Shukla perform extensive molecular dynamics (MD) simulations in conjunction with Markov State Model theory for a range of conformational starting points (apo, agonist, and antagonist bound states) to elucidate a dynamic overview of SMO activation. This has mostly remained elusive despite the availability of inactive and active-state SMO structures. They reveal conserved motifs important for activation of class F receptors, which are distinct from other known activation motifs, including from class A or B GPCRs. The long-range MD simulations together with free energy calculations also identified three additional intermediate states between inactive and fully active SMO. Furthermore, they provide structural support for how the specific function of antagonists and agonists modulate the cholesterol tunnel and thereby modulate SMO’s activity. Finally, the authors present the dynamic allosteric pathway at atomistic resolution between the extracellular and intracellular side during activation and upon ligand modulation. Taken together, the authors provide a more detailed understanding of Class F GPCRs that could serve as the foundation for specific experimental validation studies.

The perceived strengths and weaknesses:

The new perspectives on the conformational changes during activation of SMO are based on well-described MD simulations. The Class F activation mechanism is not well understood; hence the authors’ conclusions advance the field by identifying states that are distinct from class A GPCRs and how cholesterol can modulate SMO’s activity, resulting in a map of allosteric pathways for this receptor type. However, the stated uniqueness or proposed Class F-specific observations would be more definitive with additional analyses.

RECOMMENDATIONS

Revisions essential for endorsement:

  1. The title seems too comprehensive for the present study. Please consider a title that more accurately summarizes the specific work in this manuscript.

  2. [Methods - Pre-Production MD, page 20]: The authors chose a more complex membrane composition to mimic physiological cerebellar membranes that requires additional attention during equilibration. If this has not been undertaken (no note in the method section), we do recommend carefully investigating the lipid distributions/clustering, including unusual curvature, that might influence the receptors behaviour throughout the simulations, in particular if modulations by ligands are interpreted.

  3. [Methods]: The authors should discuss convergence of the simulated clusters and energy landscape prior to conducting Markov State Modeling.

  4. DRY motif:

    • Please clarify the statements about generalisation in Class F in light of the missing outward kinking. As this proline is present in other Class F receptors, it suggests that this activation feature is likely unique to SMO. Also this molecular switch should be compared to e.g. Rhodopsin, which also signals through Gi (see e.g. Hofmann et al. doi: 10.1016/j.tibs.2009.07.005).

    • The authors should consider additional evidence or be more careful in their statement that the conserved motif (W3.50-G5.39-M6.30) acts as a microswitch in SMO signal transduction. The authors claimed that it is analogous to the DRY motif in class A GPCRs. However, the DRY motif has been shown to be involved in both inactive and active states, validated by experimental data. The R3.50 in DRY motif forms an ionic lock in the inactive state and this ionic interaction is broken during receptor activation. It is unclear what interactions are formed between the W-G-M motif in SMO.

    • Similar to W-G-M, the D-R-E motif has also been claimed as an important site for signal transduction. We recommend caution in this conclusion. The authors mentioned there is an H-bond interaction between D473 and E518. Is there a water molecule between these two residues? The two residues have very low pKa for the carboxylate group and probably are devoid of hydrogens in physiological conditions. Figure 2C should include the R400.

  5. The statement “SAG acts as an agonist by allosterically expanding the tunnel at the cholesterol interaction site” (line 252) may be incorrect according to at least two lines of evidence: 1) elongation of the 4-aminomethyl group of SAG converts it to an antagonist; 2) SMO variants containing mutations at the cholesterol binding site don’t respond to SAG as described in Deshpande et al (Nature 571, 284–288 (2019)). The agonist activity of SAG is most likely due to blocking cholesterol in the 7-TMs. The authors may want to change the statement and conclusion or provide strong evidence to support it.

Additional suggestions for the authors to consider:

  1. [Fig S.19]: Validity of the MSM on 5 macrostates via the Chapman-Kolmogorov test: the predictions and estimates look identical. Please add a 95% confidence interval and provide scripts used for the calculation and plotting.

  2. Did the authors try to simulate SMO with cholesterol bound to the cysteine rich domain (CRD)? The reorientation of CRD revealed by xSMO crystal structures is controversial in the field because this movement may be a result of crystal packing. It will be very interesting to test whether CRD can undergo this reorientation after cholesterol binding by MD simulation.

  3. [Methods, page 19 line 332]: It is not entirely clear what preparation was done to the SANT1-SMO structure. Please rephrase the sentences to ensure reproducibility

  4. [Methods]: General structure preparation: Where the structures solvated prior insertion into the membrane to avoid collapsing of cavities?

  5. [Methods - MD, page 20]: The authors do not mention the used force-field. Please add.

  6. [Figure S6]: For clarification and comparison (in context of uniqueness), please show the changes in the residues involved in the microswitch for b2AR (6.30, 5.58, 5.66 - also shown for Rhodopsin and others).

  7. [Figure 3]: the authors compared the CRD-TMD junction between inactive and active states. How is the conformation of these residues compared to the determined structures of SMO?

  8. [Results]: It is very interesting that three intermediate states have been determined between inactive and active states. It is unclear how these states (I1., I2, I3) are defined, besides the energy barrier. Are there any signature residues or motifs that can represent each intermediate state?

  9. Cholesterol interactions, distributions and modulations could give valuable insight into their influence on the activation mechanism. As cholesterol is present in the simulations, this data could be easily screened for cholesterol-receptor interactions throughout the activation pathway.

  10. Experimental structures have already revealed the conformational changes between inactive and active SMO, in particular, the shift of TM6 and the movement of W535. This should be clarified in the text and interpreted in light of the new results.

  11. While the authors calculated the mean first passage times during the apo simulation, they did not correlate this to the presence and absence of agonists. This could give further insight into how those modulators are influencing the activation pathway.

  12. [Methods]: The used analysis scripts could be deposited/made available (e.g. how the Chapman-Kolmogorov test was implemented).

  13. The study would have a greater influence on the field by further investigations on the agreement between simulations and experiments.

REVIEWING TEAM

Reviewed by:

Tao Che, Assistant Professor, Washington University in St. Louis, USA: atomic-level understanding of the activation mechanisms of pain-related GPCRs

Xiaofeng Qi, Postdoctoral Researcher, UT Southwestern Medical Center, USA: structural biology of SMO receptors and Hh/Wnt signaling

Johanna Tiemann, Postdoctoral Researcher, University of Copenhagen, Denmark: MD simulations of activation mechanisms in Class A GPCRs

Curated by:

Alexander S. Hauser, Associate Professor, University of Copenhagen, Denmark

(This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Minor corrections and presentational issues have been omitted for brevity.)