WikiSysop: Created page with "== Citation == Hoff, Samuel E. / Thomasen, F. Emil / Lindorff-Larsen, Kresten / Bonomi, Massimiliano. Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference. 2024. PLOS Computational Biology, Vol. 20, No. 7, p. e1012180 == Abstract == Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced mo..."

2024-08-21T11:45:17Z

Created page with "== Citation == Hoff, Samuel E. / Thomasen, F. Emil / Lindorff-Larsen, Kresten / Bonomi, Massimiliano. Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference. 2024. PLOS Computational Biology, Vol. 20, No. 7, p. e1012180 == Abstract == Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced mo..."

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== Citation ==

Hoff, Samuel E. / Thomasen, F. Emil / Lindorff-Larsen, Kresten / Bonomi, Massimiliano. Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference. 2024. PLOS Computational Biology, Vol. 20, No. 7, p. e1012180

== Abstract ==

Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a
challenging problem of outstanding importance. Current refinement methods often generate
unbalanced models in which physico-chemical quality is sacrificed for excellent fit to the
data. Furthermore, these techniques struggle to represent the conformational heterogeneity
averaged out in low-resolution regions of density maps. Here we introduce EMMIVox, a
Bayesian inference approach to determine single-structure models as well as structural
ensembles from cryo-EM maps. EMMIVox automatically balances experimental information
with accurate physico-chemical models of the system and the surrounding environment,
including waters, lipids, and ions. Explicit treatment of data correlation and noise as well as
inference of accurate B-factors enable determination of structural models and ensembles
with both excellent fit to the data and high stereochemical quality, thus outperforming stateof-
the-art refinement techniques. EMMIVox represents a flexible approach to determine
high-quality structural models that will contribute to advancing our understanding of the
molecular mechanisms underlying biological functions.

== Keywords ==

== Links ==

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012180

== Related software ==

== Related methods ==

== Comments ==

2024Hoff EMMIVox - Revision history