ArticleProceedings of the National Academy of Sciences of the United States of America2025
Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- The CAHRA Challenge: A Community-Wide Assessment of Cryo-EM Heterogeneous Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026Article
- Cryo-EM: the revolution continues.IUCrJ · 2026Review
- Cryo-EM analysis of cooperative conformational changes in the SARS-CoV-2 spike protein trimer.IUCrJ · 2026Article
- Digital twin of biomacromolecular thermodynamics in cryo-EM data.Communications chemistry · 2026Article
- Article
- Heterogeneous reconstruction algorithms for cryoEM achieve limited particle classification accuracy on real benchmark datasets.bioRxiv : the preprint server for biology · 2026Article
- Engineered Cas9 complexes establish an experimentally grounded benchmark for heterogeneous cryoEM reconstruction methods.bioRxiv : the preprint server for biology · 2026Article
- Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis.Acta crystallographica. Section D, Structural biology · 2026Article
- Counting particles in cryo-electron microscopy may result in incorrect population estimates.Communications biology · 2026Article
- CryoJAX: a cryo-electron microscopy image-simulation library in JAX.Acta crystallographica. Section D, Structural biology · 2026Article
- Article
- Comparing cryo-EM methods and molecular dynamics simulation to investigate heterogeneity in ligand-bound TRPV1.Nature communications · 2025Article
- 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
- Bayesian Perspective for Orientation Determination in Cryo-EM with Application to Structural Heterogeneity Analysis.bioRxiv : the preprint server for biology · 2025Article
- How many (distinguishable) classes can we identify in single-particle analysis?Acta crystallographica. Section D, Structural biology · 2025Article
- Merging conformational landscapes in a single consensus space with FlexConsensus algorithm.Nature methods · 2025Article
- Deciphering conformational dynamics in AFM data using fast nonlinear NMA and FFT-based search with AFMFit.Communications biology · 2025Article
- Dimerization and dynamics of human angiotensin-I converting enzyme revealed by cryo-EM and MD simulations.eLife · 2025Article
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Abstract
Proteins and the complexes they form are central to nearly all cellular processes. Their flexibility, expressed through a continuum of states, provides a window into their biological functions. Cryogenic electron microscopy (cryo-EM) is an ideal tool to study these dynamic states as it captures specimens in noncrystalline conditions and enables high-resolution reconstructions. However, analyzing the heterogeneous distributions of conformations from cryo-EM data is challenging. We present RECOVAR, a method for analyzing these distributions based on principal component analysis (PCA) computed using a REgularized COVARiance estimator. RECOVAR is fast, robust, interpretable, expressive, and competitive with state-of-the-art neural network methods on heterogeneous cryo-EM datasets. The regularized covariance method efficiently computes a large number of high-resolution principal components that can encode rich heterogeneous distributions of conformations and does so robustly thanks to an automatic regularization scheme. The reconstruction method based on adaptive kernel regression resolves conformational states to a higher resolution than all other tested methods on extensive independent benchmarks while remaining highly interpretable. Additionally, we exploit favorable properties of the PCA embedding to estimate the conformational density accurately. This density allows for better interpretability of the latent space by identifying stable states and low free-energy motions. Finally, we present a scheme to navigate the high-dimensional latent space by automatically identifying these low free-energy trajectories. We make the code freely available at https://github.com/ma-gilles/recovar.
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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.