ArticleNature communications2025
Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- CryoJAX: a cryo-electron microscopy image-simulation library in JAX.Acta crystallographica. Section D, Structural biology · 2026Article
- Strategic advances for cryo-EM structural studies of small (<100 kDa) GPCRs.Communications biology · 2026Review
- The Open Conformation of CCT: How to Achieve High Resolution Using CryoEM.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Cryo-electron microscopy ensemble optimization using individual particles and physical constraints.bioRxiv : the preprint server for biology · 2025Article
- The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge.bioRxiv : the preprint server for biology · 2025Article
- cryoJAX: A Cryo-electron Microscopy Image Simulation Library In JAX.bioRxiv : the preprint server for biology · 2025Article
- Merging conformational landscapes in a single consensus space with FlexConsensus algorithm.Nature methods · 2025Article
- Exploring the Intrinsic Structural Plasticity and Conformational Dynamics of Human Beta Coronavirus Spike Glycoproteins.Journal of chemical information and modeling · 2025Article
- Automated model-free analysis of cryo-EM volume ensembles with SIREn.Structure (London, England : 1993) · 2025Article
- Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN.Nature communications · 2025Article
- InstaMap: instant-NGP for cryo-EM density maps.Acta crystallographica. Section D, Structural biology · 2025Article
- Automated model-free analysis of cryo-EM volume ensembles with SIREn.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformations of macromolecules. Traditional methods assume discrete conformations, while newer algorithms estimate conformational landscapes representing the different structural states a biomolecule explores. This work presents HetSIREN, a deep learning-based method that can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. HetSIREN is defined as an accurate space-based method that allows spatially focused analysis and the introduction of sinusoidal hypernetworks with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the images' pose while conditioning the network with additional constraints to yield cleaner high-quality volumes, as well as addressing one of the most confusing issues in heterogeneity analysis, as it is the fact that structural heterogeneity estimations are entangled with pose estimation (and to a lesser extent with CTF estimation) thanks to its decoupling architecture.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.