Document Type
Article
Publication Date
2022
DOI
10.3390/biom12081022
Publication Title
Biomolecules
Volume
12
Issue
8
Pages
1022 (1-16)
Abstract
Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86–0.95) under experimental noise conditions.
Original Publication Citation
Sazzed, S., Scheible, P., He, J., & Wriggers, W. (2022). Spaghetti Tracer: A framework for tracing semiregular filamentous densities in 3D tomograms. Biomolecules, 12(8), 1-16, Article 1022. https://doi.org/10.3390/biom12081022
Repository Citation
Sazzed, S., Scheible, P., He, J., & Wriggers, W. (2022). Spaghetti Tracer: A framework for tracing semiregular filamentous densities in 3D tomograms. Biomolecules, 12(8), 1-16, Article 1022. https://doi.org/10.3390/biom12081022
ORCID
0000-0002-8552-5337 (Sazzed), 0000-0001-5326-3152 (Wriggers)
Comments
© 2022 by the authors.
This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.