Title: Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data
Venue: NeurIPS 2025
Accepted: yes
Keywords: Computational Biology and Bioinformatics

Generative Models
Abstract: Protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fail to capture the conformational diversity of dynamic biomolecular complexes, often requiring heuristic MSA subsampling approaches for generating alternative states. In parallel, cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for imaging near-native structural heterogeneity, but is challenged by arduous pipelines to transform raw experimental data into atomic models. Here, we bridge the gap between these modalities, combining cryo-EM density maps with the rich sequence and biophysical priors learned by protein structure prediction models. Our method, CryoBoltz, guides the sampling trajectory of a pretrained biomolecular structure prediction model using both global and local structural constraints derived from density maps, driving predictions towards conformational states consistent with the experimental data. We demonstrate that this flexible yet powerful inference-time approach allows us to build atomic models into heterogeneous cryo-EM maps across a variety of dynamic biomolecular systems including transporters and antibodies.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a guided diffusion procedure for sampling from AlphaFold3 while conditioning on density maps obtained from Cryo-EM experiments. The authors propose a two-stage, global-to-local sampling scheme. In the global stage, the diffusion model is guided towards the course structure of the protein conditioning on sequence alone, and in the local stage, Cryo-EM information is incorporated through the likelihood of the forward model. Since the required guidance term is intractable, a previously proposed approximation to it is used.\n\nThe authors evaluate their method on several protein structures, including some simulated and some experimental Cryo-EM maps.\n\nSTRENGTHS AND WEAKNESSES:\nQuality\n\nThe quality of the paper is good. It is well written and figures are well-presented.\n\nClarity\n\nThe clarity of the exposition is above average, and I enjoyed reading the paper.\n\nSignificance\n\nI would judge the significance of this work as moderate-to-high. The field being tackled is extremely active, and many researchers in structural biology are targeting this problem. As time goes on, Cryo-EM microscopy will become more and more common. A lack of multi-conformational accuracy is one of the few remaining gaps in AF3’s performance, and this model appears to do a decent job of addressing that. \n\nOriginality\n\nOriginality seems moderate. I do believe that this idea was “in the air” somewhat, in that I expect other groups to publish similar work. Guidance of diffusion models for proteins is not new, and it’s only a matter of time before it was successfully applied to Cryo-EM. However, the exact implementation, going from global to local, seems like an original advance, and appears to be very important for getting the method to work well in practice.\n\nQUESTIONS:\n-\tHave the authors considered the possibility of test set contamination? I.e., to what extent are the experimental results attributable to model generalization vs. memorization of structures in the train set? One standard way to measure this would be to measure the sequence (or structure) similarity of each of the test proteins to the nearest sequence/structures in the AF3 train set.\n-\tCould the authors explain why ModelAngelo performs so poorly in Figure 5? Is it simply because it cannot handle proteins with multiple states well?\n\nLIMITATIONS:\n-\tThe number of proteins demonstrated is very small – only three. It is hard to avoid the possibility of cherry-picking or a lucky selection. In that sense I think the experimental section is lacking and not statistically significant.\n-\tAs mentioned above, I do not see any effort to measure the possibility of test-set contamination.\n-\tTwo of the three systems involve synthetic rather than experimental Cryo-EM maps. I would like to see more actual Cryo-EM maps used. Actual maps are noisy and potentially pose a much more difficult problem.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents CryoBoltz, a method that bridges protein structure prediction models like AlphaFold3 with cryo-EM data via multiscale guidance, driving sampling towards conformational states consistent with experimental observations. By integrating global and local structural constraints from density maps, CryoBoltz overcomes the single-structure bias of existing models, enabling accurate atomic modeling of heterogeneous cryo-EM maps for dynamic systems like transporters and antibodies.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- Utilizes AlphaFold3 as a prior to enhance the accuracy of heterogeneous analysis, effectively integrating rich sequence and biophysical priors with cryo-EM data to drive predictions toward experimental consistency.  \n- Implements a smart design of global and local guidance mechanisms: global guidance targets large-scale conformational adjustments using low-resolution point cloud representations, while local guidance refines high-resolution details based on physics-based forward models. \n\n\n\nWeaknesses\n\n- There is a vulnerability in relying on AlphaFold3 as a prior. If AlphaFold3 fails to accurately predict the protein structure, the entire heterogeniety analysis accuracy relying on it may be severely compromised, potentially leading to incorrect interpretations of the data.\n- The potential biases introduced by AlphaFold3 pose an issue. For instance, if it mispredicts a loop region as a helix while getting the global structure correct, it's unclear whether the cryo - EM data can effectively correct such local mispredictions. There is also a lack of clarity on how to detect such biases and whether there are failure cases presented to alert users and prevent them from being misled by the model.\n\nQUESTIONS:\n- What if AlphaFold3 fails to accurately predict the protein structure? I suppose that if the AF-predicted structures can fit the density up to some rotation and translation of some part of the structure, the error can be corrected. But if there are some larger errors, can the density correct the AF-3 structures?\n- Is it possible to incorporate the cryo-EM data into the training process, like CryoSTAR? Maybe adding a learable pair representation at the template module to finetune the Boltz-1 is enough.\n- I think incorporating some validation metrics (to avoid the prior from AF3)  will potentially boost the effectiveness of this study.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper propose a method for guiding generative models for protein stuctures (from sequence data) based on observed experimental densities. The guiding is done using two approaches: local (density based) and global (geometry/structure based, derived form the densities). The method builds on the idea of posterior sampling, where the AF3 type models act as priors and the observed densities define the likelihoods. The paper shows that this approach leads to more accurate structures than the unconditional/unguided sampling methodds (AF3 and Boltz-1)\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n1.\tThe paper is very well written\n2.\tThe formulation of the problem (posterior inference) is elegant and the derivation is sound.\n3.\tThe results are good.\n\n# Weaknesses\n\n1.\tThe validation seems a bit biased by design since Table 1 is made using synthetic maps. This means that the maps are rendered from a know structure, so we know the to-be-recovered structure is indeed contained in this rendered density maps. Wouldn’t the guidance necessarily lead to the optimal structures? What kind of noise is used in the rendering and how realistic is it? Should this table then be considered more as a sanity check? I do appreciate that this table at confirms the benefit of including observational data in the generative process.\n2.\tThe validation seems limited considering 2/3 are based on synthetic density maps. The 3rd problem is on experimental density maps. Although the it confirms the applicability on experimental data, the validation may be too limited to draw general conclusions. \n\n# Minor\n1.\tThe paper is applied and no new general principles are presented that generalize outside of the domain of structure prediction. The new guiding term (global) seems a bit ad-hoc but effective. The general principles used are known in the broader context of inverse modeling. However, within the specific application domain the paper seems relevant.\n2.\tThe paper explains the contribution as “multi-scale”. However I do not see why one is called global and the other local. The local is based on pixel-wise errors and in that sense can be considered local. However, all pixels are used, and the signal is in that sense global. The named global term is based on based distances between point clouds.  Wouldn’t a more informative naming be density vs geometry guidance or something? The main difference is imo not local vs global, but the fact that the “global” term directly gives a supervisory signal on the actual geometry. E.g. a small shift of the structure could lead to a big change in density error, whereas a small shift remains a small distance in the geometric loss. Since the problem is geometric, the “global” terms seems to be the most suitable guiding term, and I don’t understand why we would need the local term at all. In fact, the experiments show that global term is the superior one.\n\nQUESTIONS:\nThe validation was done on one structure for which there was real experimental data. To what extend can we believe the method will generalize to other structures? \n\nTo what extend are the reduction in RMSD important? The structures obtained by guidance are naturally going to be closer to \"ground truth\" resolved structures, which are probably resolved using cryoEM data? But the unguided structures may very well be accurate too, it's just that they have not been observed in such states in cryoEM. Now, I may be talking nonsense here as I don't know the field too well, but my point is, I expect a bias of some sort but can't quite put my finger on it. Perhaps the authors could either alleviate my concern, or transparently lay down the limitations of this validation.\n\nLIMITATIONS:\nOverall limitations and impact have been adequately addressed. The scientific impact could be more deeply discussed though (cf the questions field)", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nCryoBoltz is an inference-time framework that enhances diffusion-based protein structure predictors by integrating information form heterogeneous cryo-electron. It adds data-driven guidance to a pretrained score model without retraining. Using a multiscale strategy over four stages it drives models toward conformations consistent with experimental data. The stages include initial unguided sampling, coarse global alignment to a point-cloud representation of the density map, fine local fitting against the full map, and final unguided relaxation. CryoBoltz works atop any diffusion-based predictor namely Boltz-1, and overcomes their static bias. Evaluations on synthetic maps for a transporter and an antibody and on real cryo-EM maps of P-glycoprotein demonstrate its ability to recover diverse states with high accuracy. This approach bridges sequence-based models with experimental observables.\n\nSTRENGTHS AND WEAKNESSES:\n-\tThe authors clearly outline their proposed method.\n-\tThe figures are instructive, clear and of high quality. \n-\tThe authors show their multi step guidance is required through various ablations\n-\tThe authors illustrate a failure of the models to reproduce cryo-EM data without any guidance\n-\tOverall, the complexity of the approach is minimal. Comparison to other approaches and other guidance methods could be extended.\n-\tThe approach of generating synthetic data is interesting but could be leveraged more to investigate the performance as a function of resolution in greater detail.\n-\tThe experimental comparisons are valuable, but the authors only compare on a single protein (P-glycoprotein) with 4 different structures. Could the authors use a larger benchmark dataset to compare their method to show the results aren’t cherry picked? \n- Overall the a larger benchmark dataset would be helpful to asses the trust worthiness of the method. If its very hard to obtain cryo-em data you could extend your synthetic generation approach to a relevant subset of the pdb? Or all new PDB structures post Boltz-1 training? \n-\tThe reviewer does not have the domain expertise to judge the significance of this work for the broader bio community.\n\nQUESTIONS:\n•\tCould the authors provide more quantitative comparisons to alternative methods, namely ADP-3D? \n\n•\tI appreciate the synthetic dataset generation for th STP10 and CH67 antibodies. Could you show for one of them how the \naccuracy of your model changes with the resolution of your synthetic density maps. It would be interesting to see a line plot of resolution vs rmsd for all the compared methods. It would be nice to understand on a single molecule where the method works best and potential where it fails with respect to the other methods?\n\n•\tCould the authors test the method on a new pdb structure that is not included in the Boltz-1 training dataset? Is this an area where your inference-time methods shine even more? \n\n•\tCould the authors compare their approach on a larger experimental dataset. Its currently hard to judge whether the 4 proteins (section 4.4) are cherry picked or whether the outperformance generalises. \n\n•\tLow priority: Does using Boltz-2 further improve your method?\n\nLIMITATIONS:\nA more detailed experimental section would help in understanding the true limitations of the method.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}