WikiSysop: Created page with "== Citation == Luo, D., Alsuwaykit, Z., Khan, D., Strnad, O., Isenberg, T. and Viola, I. 2025. DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map. IEEE Trans. on Visualization and Computer Graphics. 31, 1 (2025), 558–568. == Abstract == We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, t..."

2025-11-02T21:10:05Z

Created page with "== Citation == Luo, D., Alsuwaykit, Z., Khan, D., Strnad, O., Isenberg, T. and Viola, I. 2025. DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map. IEEE Trans. on Visualization and Computer Graphics. 31, 1 (2025), 558–568. == Abstract == We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, t..."

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== Citation ==

Luo, D., Alsuwaykit, Z., Khan, D., Strnad, O., Isenberg, T. and Viola, I. 2025. DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map. IEEE Trans. on Visualization and Computer Graphics. 31, 1 (2025), 558–568.

== Abstract ==

We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed
Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite
large mesoscale models of complex protein assemblies and complete cellular structures that are based on measured cryo-EM data.
The current approaches require manual fitting in three dimensions to start, resulting in approximately aligned structures followed by an
automated fine-tuning of the alignment. The DiffFit approach enables domain scientists to fit new structures automatically and visualize
the results for inspection and interactive revision. The fitting begins with differentiable three-dimensional (3D) rigid transformations of
the protein atom coordinates followed by sampling the density values at the atom coordinates from the target cryo-EM volume. To
ensure a meaningful correlation between the sampled densities and the protein structure, we proposed a novel loss function based on
a multi-resolution volume-array approach and the exploitation of the negative space. This loss function serves as a critical metric for
assessing the fitting quality, ensuring the fitting accuracy and an improved visualization of the results. We assessed the placement
quality of DiffFit with several large, realistic datasets and found it to be superior to that of previous methods. We further evaluated
our method in two use cases: automating the integration of known composite structures into larger protein complexes and facilitating
the fitting of predicted protein domains into volume densities to aid researchers in identifying unknown p roteins. We implemented
our algorithm as an open-source plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization software in the field. All
supplemental materials are available at osf.io/5tx4q.

== Keywords ==

== Links ==

https://ieeexplore.ieee.org/abstract/document/10672541

== Related software ==

== Related methods ==

== Comments ==

2025Luo DiffFit - Revision history