WikiSysop: Created page with "== Citation == M. A. Gilles and A. Singer, “Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression,” Proc. Natl. Academy of Sciences, vol. 122, no. 9, p. e2419140122, 2025. == 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 id..."

2025-07-08T07:13:24Z

Created page with "== Citation == M. A. Gilles and A. Singer, “Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression,” Proc. Natl. Academy of Sciences, vol. 122, no. 9, p. e2419140122, 2025. == 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 id..."

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== Citation ==

M. A. Gilles and A. Singer, “Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression,” Proc. Natl. Academy of Sciences, vol. 122, no. 9, p. e2419140122, 2025.

== 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.

== Keywords ==

== Links ==

https://www.pnas.org/doi/abs/10.1073/pnas.2419140122

== Related software ==

== Related methods ==

== Comments ==

2025Gilles Covariance - Revision history