Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Computational Structural Biology Unit, Paris, France.
Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
PLoS Comput Biol. 2024 Jul 15;20(7):e1012180. doi: 10.1371/journal.pcbi.1012180. eCollection 2024 Jul.
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.
将低温电子显微镜（cryo-EM）数据转换为高质量的结构模型是一个具有突出重要性的挑战性问题。目前的精修方法通常会产生不平衡的模型，这些模型为了与数据的良好拟合而牺牲了物理化学质量。此外，这些技术难以表示在密度图的低分辨率区域中平均化的构象异质性。在这里，我们引入了 EMMIVox，这是一种贝叶斯推断方法，可用于从 cryo-EM 图谱中确定单结构模型以及结构集合。EMMIVox 自动平衡实验信息与系统和周围环境（包括水、脂质和离子）的精确物理化学模型。对数据相关性和噪声的显式处理以及准确 B 因子的推断，使我们能够确定具有出色数据拟合和高立体化学质量的结构模型和集合，从而超越了最先进的精修技术。EMMIVox 代表了一种灵活的方法，可以确定高质量的结构模型，这将有助于我们深入了解生物功能背后的分子机制。
