Matching of EM Map Segments to Structurally-Relevant Bio-molecular Regions
- 26 Downloads
Electron microscopy is a technique used to determine the structure of bio-molecular machines via three-dimensional images (called maps). The state-of-the-art is able to determine structures at resolutions that allow us to identify up to secondary structural features, in some cases, but it is not widespread. Furthermore, because molecular interactions often require atomic-level details to be understood, it is still necessary to complement current maps with techniques that provide finer-grain structural details. We applied segmentation techniques to maps in the Electron Microscopy Data Bank (EMDB), the standard community repository for these data. We assessed the potential of these algorithms to match functionally relevant regions in their atomic-resolution image counterparts by comparing against three protein systems, each with multiple atomic-detailed domains. We found that at least 80% of amino acid residues in 7 out of 12 domains were assigned to single segments, suggesting there is potential to match the lower resolution segmented regions to the atomic counterparts. We also qualitatively analyzed the potential on other EMDB structures, as well as generating the raw segmentation information for the complete EMDB, for interested researchers to use. Results can be accessed online and the library developed is provided as part of an open-source project.
KeywordsComputational biology Computational protein structures Electron microscopy 3DEM Segmentation
Funded by the Vicerrrectoría de Investigación y Extensión at Instituto Tecnológico de Costa Rica.
- 21.Patwardhan, A., et al.: Building bridges between cellular and molecular structural biology. eLife 6 (2017). https://doi.org/10.7554/eLife.25835
- 22.Pintilie, G.D., Zhang, J., Goddard, T.D., Chiu, W., Gossard, D.C.: Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions. J. Struct. Biol. 170(3), 427–438 (2010). https://doi.org/10.1016/j.jsb.2010.03.007CrossRefGoogle Scholar
- 23.Raschka, S.: BioPandas: working with molecular structures in pandas dataframes. J. Open Source Softw. 2(14) (2017). https://doi.org/10.21105/joss.00279
- 25.Rougier, N.P.: Glumpy. In: EuroScipy (2015)Google Scholar
- 35.Witkin, A.P.: Scale-space filtering. In: Readings in Computer Vision, pp. 329–332. Elsevier (1987). https://doi.org/10.1016/B978-0-08-051581-6.50036-2. https://linkinghub.elsevier.com/retrieve/pii/B9780080515816500362