Conformational landscapes in cryo-electron tomography data based on molecular dynamics simulations
Year: 2026
Type: review
Abstract: Cryo-electron tomography (cryo-ET) provides a unique window into molecular organization in cellular environments (in situ). However, the interpretation of molecular structural information is complicated by several intrinsic properties of cryo-ET data, such as noise, missing wedge, and continuous conformational variability of the molecules. These challenges shift the emphasis from high-resolution structure determination toward validation and interpretation of low-resolution density maps and analysis of conformational flexibility. Molecular dynamics (MD) simulations are particularly well suited to this task, as they provide a physically grounded way to explore continuous conformational transitions consistent with both experimental data and molecular energetics. This review focuses on the roles of MD simulations in cryo-ET, emphasizing their use in emerging methods for conformational landscape determination.
Author: Slavica Jonic
Language: English
Subfield: Structural Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,360
Awards: G1277351073, G3406617352, Obtaining complete macromolecular conformational landscapes at the atomic scale by combining cryo-EM image analysis, molecular dynamics simulations and deep learning, Deciphering chromatin rearrangements in response to UV irradiation using new deep learning tools for cryo-electron tomography data analysis, G7080048889
OpenAlex ID: W7212188163