3DFlex is a generative neural network method that determines the structure and motion of flexible biomolecules from cryo-EM images. Central to 3DFlex is the assumption that conformations of a dynamic protein are related to each other through deformation of a single 3D structure. Specifically, a fl...
We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and...
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seems to have a weak effect on the spheroids, and even the highest SW energy appears to undermine more the integrity of the 3D structure, rather than the cell viability of the spheroids (see pictures in the first raw and right col- umn in Fig. 7C and Figure S9 of the S.I.). ...
coordinates to the observed coordinate frame of a given particle image. Thus, 3DFlex conserves density, as do conventional, rigid reconstruction methods. Further, central to the optimization is a regularizer that encourages locally smooth and rigid motion in regions of the canonical map with high ...