cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM
cryoEM; protein structure; Deep Learning; Machine Learning; generative modelling
Yes
No ethics review needed.
Can the authors elaborate how the density maps (volumes) are reconstructed in cryoSPHERE (like in Fig. 6)? I understand how backprojection in cryo-EM works, but still find it difficult to understand how this is performed exactly.
Instead of finding the deformation for each residue, cryoSPHERE learns a segmentation of the amino acid or nucleotide chains and deforms the segment to fit the heterogenous cryo-EM data. This is indeed a valid assumption in many cases especially for large complexes  like the spliceosome (EMPIAR-10180).
The authors introduced a new method, cryoSPHERE, that resolves the continuous heterogeneity problem in cryo-EM reconstruction. Similar to some other methods, cryoSPHERE uses an atomic model as the input, makes segmentations, and uses the results as the regularization in finding the flexible movement in the cryo-EM dataset.
Major:
1. CryoSPHERE uses an atomic model as the reference and formulate the conformational heterogeneity in cryo-EM dataset as the deformation of the segments of the reference model. In finding the optimal segments, the approach is similar to what used in e2gmm, where a GMM with N_{segm} components is fitted. This segmentation result is then used in a cryoSTAR/DynaMight-like setting, replacing the regularization losses and compute predicted projections to compare with the particle images. The author should also compare the result with other similar methods, including DynaMight, e2gmm, and maybe 3DFlex, and discuss the outcomes.

2. In cryo-EM, density maps are extremely important in evaluating the validity of the results. However, the authors only presented the density maps for one of the synthetic dataset. Especially in the case of under the hypothesis that the heterogeneity is modeled as the deformations of a canonical density, it is very important that the reconstructed canonical density is free from any obvious artifact.

3. The FSC comparison in Sec. 5.2 between cryoSPHERE and cryoDRGN and cryoSTAR may not be very fair, since cryoSPHERE's density is reconstructed under the assumption that there exist a canonical density, while cryoDRGN does not hold the same hypothesis, and cryoSTAR explicitly avoids this to reduce the unwanted bias from the reference model. A fair comparison would be made against DynaMight and 3DFlex.

4. Lack of ablation study. The authors should discuss how N_{segm}, which is a key hyperparameter that is related to the degree of freedom in the segmentation, affects the results in both synthetic and real data.

5. The authors compare cryoSPHERE to cryoDRGN and cryoSTAR, but from the experiments, it is unclear what the advantage that cryoSPHERE brings to the field. The only advantage seems to be the increase FSC resolution, but it is only shown in one synthetic dataset.

Minor:
1. The sequence of Fig.6 and Fig.7 in the paper is reversed.
2. In the related works, e2gmm should also be considered as a method using deep learning.
3. In the FSC comparison, if not comparing two half maps, the cutoff of 0.5 should be used and 0.143 is not meaningful. See [1] appendix for the reason (and also why 0.143 is the number widely used for half maps comprison). Therefore the discussion at the end of Sec. 5.2 about 0.5 and 0.143 cutoff improvement is not very correct.
4. The writing could be better polished.
5. I do not think the "Ethics Statement" content is proper as it lacks evidence.


[1] Rosenthal, Peter B., and Richard Henderson. "Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy." Journal of molecular biology 333.4 (2003): 721-745.
Yes
No ethics review needed.
Questions for clarification 
- In section 3.6 the authors say “Similar to Li et al. (2023), instead of using a mean squared error loss between the predicted and ground truth image, we use a correlation loss between the true and predicted image”. However, they do not motivate this choice. Would you please care to elaborate?
- In section 5.1 the authors write “Testing the segment decomposition, we then run cryoSPHERE by requesting division into Nsegm = 4. The program learnt a first and third segment with 0 residues, a second segment with 1353 residues and a fourth segment with 157 residues (Figure 5). Thus, cryoSPHERE learnt segments according to the ground truth.” Is there a reason why we should expect that the model prefers to learn two segments?

Additional feedback
- It reads as if equation 7 is the only loss that is being used. I assume that the VAE latent distribution is also trainable? It would benefit the readability if the authors clarify whether the loss is just the decoder loss or the whole loss.“
- In section 5.3 the authors write “The first two principal components explain more than 96 percent of the variance.” In general, I would be very reluctant to make any claims about PCA in the latent space as you could come up with a transformation that scrambles the latent space and still gives the same decoding, but covers much less variance. I can imagine that Figure 7 is a nice illustration, but I would recommend to leave it with that.
The work is of high quality and overall very well-written. I’d like to highlight the following strong points of the paper:
- The authors seem very well aware of the difficulties atomistic models have in terms of the optimisation landscape and address the problem adequately by regularising while keeping sequential information intact
- The authors give useful biological remarks such as that their methods seems to recover domains, which is useful to know as a practitioner
- The authors show that their method works on real data, which is actually the first time I’ve read this for atomistic models
- The authors propose a VAE and GMM-based approach to atomistic heterogeneous Cryo-EM reconstruction
- The GMM part learns how to divide the amino acid chan in segments
- The VAE learns how to encode images into latent variables and decode these variables along with the GMM output into segment-wise conformational changes
- The authors claim that their method overcomes current challenges that atomistic models face, while also achieving improved interpretability by learning these segments (that ideally correspond to domains in the biological sense)
There is one main weak point in the paper in my regard:
- The authors do not address limitations of their method very thoroughly:
    - whether or not their method is able to overcome poor initialisation of the initial structure S_0. This is one of the major risks of working with (pseudo)-atomistic models as shown in works such as DynaMight (Schwab et al. 2024) 
    - Whether or not local physical information such as bond lengths between atoms at the boundaries of the segments is preserved. This might be challenging
Yes
No concerns
No ethics review needed.
Could you include additional comparisons with methods like CryoSTAR, or Dynamight (e.g., computational cost, novelty, etc)
1. The approach of learning to decompose the protein’s amino acid chain into segments to represent various conformations is novel.
2. The interpretability of moving part is highly beneficial for practitioners, offering a clearer understanding of conformational dynamics.
This paper introduces cryoSPHERE, a deep learning model that uses nominal protein structures (e.g., from AlphaFold) to break proteins into segments and fit these segments to various conformations in cryo-EM data.
1. No meaningful improvements were observed over CryoStar
    - Figures 3, 12, 13: It’s unclear what meaningful differences exist compared to CryoStar.
    - Figures 14, 21: There doesn’t appear to be a significant difference between CryoStar and CryoSphere (CryoStar may even appear better).
    - Figure 29: It would be great to compare CryoStar with CryoSphere rather than with CryoDRGN.
2. Should change Figure 6:
    - Instead of showing only CryoDRGN results and G.T, a comparison with CryoSTAR or Dynamight would be more appropriate (perhaps include Figure 32 and the CryoSphere result, as there are no CryoSphere results with SNR 0.001).
@inproceedings{
anonymous2024cryosphere,
title={cryo{SPHERE}: Single-Particle {HE}terogeneous {RE}construction from cryo {EM}},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=n8O0trhost},
note={under review}
}
The three-dimensional structure of proteins plays a crucial role in determining their function. Protein structure prediction methods, like AlphaFold, offer rapid access to a protein’s structure. However, large protein complexes cannot be reliably predicted, and proteins are dynamic, making it important to resolve their full conformational distribution. Single-particle cryo-electron microscopy (cryo-EM) is a powerful tool for determining the structures of large protein complexes. Importantly, the numerous images of a given protein contain underutilized information about conformational heterogeneity. These images are very noisy projections of the protein, and traditional methods for cryo-EM reconstruction are limited to recovering only one or a few consensus conformations.

In this paper, we introduce cryoSPHERE, which is a deep learning method that uses a nominal protein structure (e.g., from AlphaFold) as input, learns how to divide it into segments, and moves these segments as approximately rigid bodies to fit the different conformations present in the cryo-EM dataset. This approach provides enough constraints to enable meaningful reconstructions of single protein structural ensembles. We demonstrate this with two synthetic datasets featuring varying levels of noise, as well as one real dataset. We show that cryoSPHERE is very resilient to the high levels of noise typically encountered in experiments, where we see consistent improvements over the current state-of-the-art for heterogeneous reconstruction.
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cryoEM; protein structure; Deep Learning; Machine Learning; generative modelling
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cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM
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