Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This method is based on the combination of physico-chemical, shape and cross correlation features between each of the domains and the EM map. This work is not part of a stand-alone article as of this writing.
- 2.
Note that for 1010 there are 8 missing residues, observed in the C-\(\alpha \) trace but not the PDB with all atomic details. They are ommitted for analysis purposes.
- 3.
The production version of EM-SURFER is hosted at http://kiharalab.org/em-surfer. An example result from our alpha release of the latest version, that includes segmentation results, can be accessed at is available at http://emsurfer.tecdatalab.org/result/0185.
References
Ahmed, A., Whitford, P.C., Sanbonmatsu, K.Y., Tama, F.: Consensus among flexible fitting approaches improves the interpretation of cryo-EM data. J. Struct. Biol. 177(2), 561–570 (2012). https://doi.org/10.1016/j.jsb.2011.10.002
Baker, M.L., Baker, M.R., Hryc, C.F., Ju, T., Chiu, W.: Gorgon and pathwalking: macromolecular modeling tools for subnanometer resolution density maps. Biopolymers 97(9), 655–668 (2012). https://doi.org/10.1002/bip.22065
Baker, M.L., Ju, T., Chiu, W.: Identification of secondary structure elements in intermediate-resolution density maps. Structure 15(1), 7–19 (2007). https://doi.org/10.1016/j.str.2006.11.008
Baker, M.L., Yu, Z., Chiu, W., Bajaj, C.: Automated segmentation of molecular subunits in electron cryomicroscopy density maps. J. Struct. Biol. 156(3), 432–441 (2006). https://doi.org/10.1016/j.jsb.2006.05.013
Beck, F., et al.: Near-atomic resolution structural model of the yeast 26S proteasome. Proc. Natl. Acad. Sci. U.S.A. 109(37), 14870–14875 (2012). https://doi.org/10.1073/pnas.1213333109
Beck, M., et al.: Exploring the spatial and temporal organization of a cell’s proteome. J. Struct. Biol. 173(3), 483–496 (2011). https://doi.org/10.1016/j.jsb.2010.11.011
Burley, S.K., et al.: Protein data bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47(D1), D520–D528 (2019). https://doi.org/10.1093/nar/gky949
Dou, H., Burrows, D.W., Baker, M.L., Ju, T.: Flexible fitting of atomic models into cryo-EM density maps guided by helix correspondences. Biophys. J. 112(12), 2479–2493 (2017). https://doi.org/10.1016/j.bpj.2017.04.054
Esquivel-Rodríguez, J., Xiong, Y., Han, X., Guang, S., Christoffer, C., Kihara, D.: Navigating 3D electron microscopy maps with EM-SURFER. BMC Bioinform. 16, 181 (2015). https://doi.org/10.1186/s12859-015-0580-6
Fabiola, F., Chapman, M.S.: Fitting of high-resolution structures into electron microscopy reconstruction images. Structure 13(3), 389–400 (2005). https://doi.org/10.1016/j.str.2005.01.007
Hryc, C.F., et al.: Accurate model annotation of a near-atomic resolution cryo-EM map. Proc. Natl. Acad. Sci. 114(12), 3103–3108 (2017). https://doi.org/10.1073/PNAS.1621152114
Jiang, W., Baker, M.L., Ludtke, S.J., Chiu, W.: Bridging the information gap: computational tools for intermediate resolution structure interpretation. J. Mol. Biol. 308(5), 1033–1044 (2001). https://doi.org/10.1006/jmbi.2001.4633
Kong, Y., Ma, J.: A structural-informatics approach for mining beta-sheets: locating sheets in intermediate-resolution density maps. J. Mol. Biol. 332(2), 399–413 (2003)
Kong, Y., Zhang, X., Baker, T.S., Ma, J.: A structural-informatics approach for tracing beta-sheets: building pseudo-C(alpha) traces for beta-strands in intermediate-resolution density maps. J. Mol. Biol. 339(1), 117–130 (2004). https://doi.org/10.1016/j.jmb.2004.03.038
Kostyuchenko, V.A., et al.: Three-dimensional structure of bacteriophage T4 baseplate. Nat. Struct. Biol. 10(9), 688–693 (2003). https://doi.org/10.1038/nsb970
Lawson, C.L., et al.: EMDataBank unified data resource for 3DEM. Nucleic Acids Res. 44(D1), D396–D403 (2016). https://doi.org/10.1093/nar/gkv1126
Lewiner, T., Lopes, H., Vieira, A.W., Tavares, G.: Efficient implementation of marching cubes’ cases with topological guarantees. J. Graph.Tools 8(2), 1–15 (2003). https://doi.org/10.1080/10867651.2003.10487582
Lindert, S., Stewart, P.L., Meiler, J.: Hybrid approaches: applying computational methods in cryo-electron microscopy. Curr. Opin. Struct. Biol. 19(2), 218–225 (2009). https://doi.org/10.1016/j.sbi.2009.02.010
Ludtke, S.J., Chen, D.H., Song, J.L., Chuang, D.T., Chiu, W.: Seeing GroEL at 6 A resolution by single particle electron cryomicroscopy. Structure 12(7), 1129–1136 (2004). https://doi.org/10.1016/j.str.2004.05.006
Mitra, K., et al.: Structure of the E. Coli protein-conducting channel bound to a translating ribosome. Nature 438(7066), 318–324 (2005). https://doi.org/10.1038/nature04133
Patwardhan, A., et al.: Building bridges between cellular and molecular structural biology. eLife 6 (2017). https://doi.org/10.7554/eLife.25835
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.2010.03.007
Raschka, S.: BioPandas: working with molecular structures in pandas dataframes. J. Open Source Softw. 2(14) (2017). https://doi.org/10.21105/joss.00279
Roh, S.H., et al.: The 3.5-Å CryoEM structure of nanodisc-reconstituted yeast vacuolar ATPase Vo proton channel. Mol. Cell 69(6), 993.e3–1004.e3 (2018). https://doi.org/10.1016/j.molcel.2018.02.006
Rougier, N.P.: Glumpy. In: EuroScipy (2015)
Terashi, G., Kihara, D.: De novo main-chain modeling with MAINMAST in 2015/2016 EM model challenge. J. Struct. Biol. 204(2), 351–359 (2018). https://doi.org/10.1016/J.JSB.2018.07.013
Terwilliger, T.C., Adams, P.D., Afonine, P.V., Sobolev, O.V.: A fully automatic method yielding initial models from high-resolution cryo-electron microscopy maps. Nat. Methods 15(11), 905–908 (2018). https://doi.org/10.1038/s41592-018-0173-1
Topf, M., Baker, M.L., John, B., Chiu, W., Sali, A.: Structural characterization of components of protein assemblies by comparative modeling and electron cryo-microscopy. J. Struct. Biol. 149(2), 191–203 (2005). https://doi.org/10.1016/j.jsb.2004.11.004
Unverdorben, P., et al.: Deep classification of a large cryo-EM dataset defines the conformational landscape of the 26S proteasome. Proc. Natl. Acad. Sci. U.S.A. 111(15), 5544–5549 (2014). https://doi.org/10.1073/pnas.1403409111
van der Walt, S., Colbert, S.C., Varoquaux, G.: The numpy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22–30 (2011). https://doi.org/10.1109/MCSE.2011.37
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991). https://doi.org/10.1109/34.87344
Volkmann, N., Hanein, D., Ouyang, G., Trybus, K.M., DeRosier, D.J., Lowey, S.: Evidence for cleft closure in actomyosin upon ADP release. Nat. Struct. Biol. 7(12), 1147–1155 (2000). https://doi.org/10.1038/82008
Volkmann, N.: A novel three-dimensional variant of the watershed transform for segmentation of electron density maps. J. Struct. Biol. 138(1–2), 123–129 (2002). https://doi.org/10.1016/S1047-8477(02)00009-6
Van der Walt, S., et al.: Scikit-image: image processing in python. PeerJ 2, e453 (2014)
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
Acknowledgements
Funded by the Vicerrrectoría de Investigación y Extensión at Instituto Tecnológico de Costa Rica.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zumbado-Corrales, M., Castillo-Valverde, L., Salas-Bonilla, J., Víquez-Murillo, J., Kihara, D., Esquivel-Rodríguez, J. (2020). Matching of EM Map Segments to Structurally-Relevant Bio-molecular Regions. In: Crespo-Mariño, J., Meneses-Rojas, E. (eds) High Performance Computing. CARLA 2019. Communications in Computer and Information Science, vol 1087. Springer, Cham. https://doi.org/10.1007/978-3-030-41005-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-41005-6_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41004-9
Online ISBN: 978-3-030-41005-6
eBook Packages: Computer ScienceComputer Science (R0)