Rigid-Body Fitting of Atomic Models on 3D Density Maps of Electron Microscopy

  • Takeshi KawabataEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1105)


Cryo electron microscopy has revolutionarily evolved for the determination of the 3D structure of macromolecular complexes. The modeling procedures on the 3D density maps of electron microscopy are roughly classified into three categories: fitting, de novo modeling and refinement. The registered atomic models from the maps have mostly been hand-built and auto-refined. Several programs aiming at automatic modeling have also been developed using various kinds of molecular representations. Among these three classes of the modeling procedures, the rigid body fitting is reviewed here, because it is the most basic modeling process applied before the other steps. The fitting problems are classified as the fittings of single subunit or multiple subunits, and the fittings on global or local parts of maps. A higher resolution map enables more local fitting. Various molecular representations have been employed in the fitting programs. A point and digital image models are generally used to represent molecules, but new representations, such as the Gaussian mixture model, have been applied recently.


Electron microscopy Gaussian mixture model EM algorithm 



This work was partially supported by JSPS KAKENHI Grants-in-Aid for Scientific Research (C), Grant Number JP26440078 and 17K07364, and the Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from Japan Agency for Medical Research and Development (AMED).


  1. Adams PD, Afonine PV, Bunkóczi G, Chen VB, Davis IW, Echols N, Headd JJ, Hung LW, Kapral GJ, Grosse-Kunstleve RW, McCoy AJ, Moriarty NW, Oeffner R, Read RJ, Richardson DC, Richardson JS, Terwilliger TC, Zwart PH (2010) PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr D Biol Crystallogr 266: 213–221CrossRefGoogle Scholar
  2. Alber F, Förster F, Korkin D, Topf M, Sali A (2008) Integrating diverse data for structure determination of macromolecular assemblies. Annu Rev Biochem 77:443–477CrossRefGoogle Scholar
  3. Bai XC, McMullan G, Scheres SH (2015) How cryo-EM is revolutionizing structural biology. Trends Biochem Sci 40:49–57CrossRefGoogle Scholar
  4. Brunger AT (2007) Version 1.2 of the crystallography and NMR system. Nat Protoc 2:2728–2733CrossRefGoogle Scholar
  5. Burley SK, Kurisu G, Markley JL, Nakamura H, Velankar S, Berman HM, Sali A, Schwede T, Trewhella J (2017) PDB-Dev: a prototype system for depositing integrative/hybrid structural models. Structure 25:1317–1318CrossRefGoogle Scholar
  6. Cassidy CK, Himes BA, Luthey-Schulten Z, Zhang P (2017) CryoEM-based hybrid modeling approaches for structure determination. Curr Opin Microbiol 43:14–23CrossRefGoogle Scholar
  7. Chacón P, Wriggers W (2002) Multi-resolution contour-based fitting of macromolecular structures. J Mol Biol 317:375–384CrossRefGoogle Scholar
  8. DiMaio F, Chiu W (2016) Tools for model building and optimization into near-atomic resolution electron cryo-microscopy density maps. Methods Enzymol 579:255–276CrossRefGoogle Scholar
  9. DiMaio F, Song Y, Li X, Brunner MJ, Xu C, Conticello V, Egelman E, Marlovits T, Cheng Y, Baker D (2015) Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement. Nat Methods 12:361–365CrossRefGoogle Scholar
  10. Emsley P, Lohkamp B, Scott WG, Cowtan K (2010) Features and development of Coot. ActaCrystallogr D BiolCrystallogr66:486–501CrossRefGoogle Scholar
  11. Goddard TD, Huang CC, Ferrin TE (2007) Visualizing density maps with UCSF chimera. J Struct Biol 157:281–287CrossRefGoogle Scholar
  12. Goddard TD, Huang CC, Meng EC, Pettersen EF, Couch GS, Morris JH, Ferrin TE (2018) UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci 27:14–25CrossRefGoogle Scholar
  13. Jones TA (2004) Interactive electron-density map interpretation: from INTER to O. Acta Crystallogr D Biol Crystallogr 60:2115–2125CrossRefGoogle Scholar
  14. Jonić S, Sorzano CÓS (2016) Coarse-graining of volumes for modeling of structure and dynamics in electron microscopy: algorithm to automatically control accuracy of approximation. IEEE J Select Top Sig Process 10:161–173CrossRefGoogle Scholar
  15. Kawabata T (2008) Multiple subunit fitting into a low-resolution density map of a macromolecular complex using a Gaussian mixture model. Biophys J 95:4643–4658CrossRefGoogle Scholar
  16. Kawabata T (2018) Gaussian-input Gaussian mixture model for representing density maps and atomic models. J Struct Biol 203:1–16CrossRefGoogle Scholar
  17. Kinjo AR, Bekker GJ, Suzuki H, Tsuchiya Y, Kawabata T, Ikegawa Y, Nakamura H (2017) Protein Data Bank Japan (PDBj): updated user interfaces, resource description framework, analysis tools for large structures. Nucleic Acids Res 45(D1):D282–D288CrossRefGoogle Scholar
  18. Kinjo AR, Bekker GJ, Wako H, Endo S, Tsuchiya Y, Sato H, Nishi H, Kinoshita K, Suzuki H, Kawabata T, Yokochi M, Iwata T, Kobayashi N, Fujiwara T, Kurisu G, Nakamura H (2018) New tools and functions in data-out activities at Protein Data Bank Japan (PDBj). Protein Sci 27:95–102CrossRefGoogle Scholar
  19. Lasker K, Topf M, Sali A, Wolfson HJ (2009) Inferential optimization for simultaneous fitting of multiple components into a cryoEM map of their assembly. J Mol Biol 388:180–194CrossRefGoogle Scholar
  20. Lasker K, Sali A, Wolfson HJ. (2010) Determining macromolecular assembly structures by molecular docking and fitting into an electron density map. Proteins 278:3205–3211CrossRefGoogle Scholar
  21. Lawson CL, Patwardhan A, Baker ML, Hryc C, Garcia ES, Hudson BP, Lagerstedt I, Ludtke SJ, Pintilie G, Sala R, Westbrook JD, Berman HM, Kleywegt GJ, Chiu W (2016) EM Data Bank unified data resource for 3DEM. Nucleic Acids Res 44(D1):D396–D403CrossRefGoogle Scholar
  22. Liang YL, Khoshouei M, Radjainia M, Zhang Y, Glukhova A, Tarrasch J, Thal DM, Furness SGB, Christopoulos G, Coudrat T, Danev R, Baumeister W, Miller LJ, Christopoulos A, Kobilka BK, Wootten D, Skiniotis G, Sexton PM (2017) Phase-plate cryo-EM structure of a class B GPCR-G-protein complex. Nature 546:118–123CrossRefGoogle Scholar
  23. Murshudov GN, Skubák P, Lebedev AA, Pannu NS, Steiner RA, Nicholls RA, Winn MD, Long F, Vagin AA. (2011) REFMAC5 for the refinement of macromolecular crystal structures. ActaCrystallogr D BiolCrystallogr67:355–367CrossRefGoogle Scholar
  24. Nicholls RA, Long F, Murshudov GN (2012) Low-resolution refinement tools in REFMAC5. Acta Crystallogr D Biol Crystallogr 68:404–417CrossRefGoogle Scholar
  25. Pandurangan AP, Vasishtan D, Alber F, Topf M (2015) γ-TEMPy: simultaneous fitting of components in 3D-EM maps of their assembly using a genetic algorithm. Structure 23: 2365–2376CrossRefGoogle Scholar
  26. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera – a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  27. Pintilie GD, Zhang J, Goddard TD, Chiu W, Gossard DC (2010) 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 170:427–438CrossRefGoogle Scholar
  28. Robinson PJ, Trnka MJ, Pellarin R, Greenberg CH, Bushnell DA, Davis R, Burlingame AL, Sali A, Kornberg D. (2015) Molecular architecture of the yeast Mediator complex. Elife e08719Google Scholar
  29. Rossmann MG, Bernal R, Pletnev SV (2001) Combining electron microscopic with X ray crystallographic structures. J Struct Biol 136:190–200CrossRefGoogle Scholar
  30. Russel D, Lasker K, Webb B, Velázquez-Muriel J, Tjioe E, Schneidman-Duhovny D, Peterson B, Sali A (2012) Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies. PLoS Biol 10:e1001244CrossRefGoogle Scholar
  31. Siebert X, Navaza J (2009) UROX 2.0: an interactive tool for fitting atomic models into electron-microscopy reconstructions. Acta Crystallogr D Biol Crystallogr 65:651–658CrossRefGoogle Scholar
  32. Suzuki H, Kawabata T, Nakamura H (2016) Omokage search: shape similarity search service for biomolecular structures in both the PDB and EMDB. Bioinformatics 32:619–620CrossRefGoogle Scholar
  33. Topf M, Lasker K, Webb B, Wolfson H, Chiu W, Sali A (2008) Protein structure fitting and refinement guided by cryo-EM density. Structure 16:295–307CrossRefGoogle Scholar
  34. Trabuco LG, Villa E, Mitra K, Frank J, Schulten K (2008) Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 16:673–683CrossRefGoogle Scholar
  35. van Zundert GCP, Melquiond ASJ, Bonvin AMJJ (2015) Integrative modeling of biomolecular complexes: HADDOCKing with Cryo-Electron microscopy data. Structure 23:949–960CrossRefGoogle Scholar
  36. Webb B, Viswanath S, Bonomi M, Pellarin R, Greenberg CH, Saltzberg D, Sali A.(2018) Integrative structure modeling with the integrative modeling platform. Protein Sci 28:245–258CrossRefGoogle Scholar
  37. Wriggers W (2012) Conventions and workflows for using situs. Acta Crystallogr D Biol Crystallogr 68:344–351CrossRefGoogle Scholar
  38. Wriggers W, Milligan RA, Schulten K, McCammon JA (1998) Self-organizing neural networks bridge the biomolecular resolution gap. J Mol Biol 284:1247–1254CrossRefGoogle Scholar
  39. Zhang S, Vasishtan D, Xu M, Topf M, Alber F (2010) A fast mathematical programming procedure for simultaneous fitting of assembly components into cryoEM density maps. Bioinformatics 26:i261–i268CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Institute for Protein ResearchOsaka UniversitySuitaJapan

Personalised recommendations