Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference.
PLoS Comput Biol
; 20(7): e1012180, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-39008528
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 state-of-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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Moleculares
/
Teorema de Bayes
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Microscopia Crioeletrônica
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
França