Skip to main content

Structural Analysis of Protein Complexes by Cryo-Electron Microscopy

  • Protocol
  • First Online:
Bacterial Secretion Systems

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2715))

Abstract

Structural studies of bio-complexes using single particle cryo-Electron Microscopy (cryo-EM) is nowadays a well-established technique in structural biology and has become competitive with X-ray crystallography. Development of digital registration systems for electron microscopy images and algorithms for the fast and efficient processing of the recorded images and their following analysis has facilitated the determination of structures at near-atomic resolution. The latest advances in EM have enabled the determination of protein complex structures at 1.4–3 Å resolution for an extremely broad range of sizes (from ~100 kDa up to hundreds of MDa (Bartesaghi et al., Science 348(6239):1147–1151, 2015; Herzik et al., Nat Commun 10:1032, 2019; Wu et al., J Struct Biol X 4:100020, 2020; Zhang et al., Nat Commun 10:5511, 2019; Zhang et al., Cell Res 30(12):1136–1139, 2020; Yip et al., Nature 587(7832):157–161, 2020; https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year)). In 2022, nearly 1200 structures deposited to the EMDB database were at a resolution of better than 3 Å (https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year).

To date, the highest resolutions have been achieved for apoferritin, which comprises a homo-oligomer of high point group symmetry (O432) and has rigid organization together with high stability (Zhang et al., Cell Res 30(12):1136–1139, 2020; Yip et al., Nature 587(7832):157–161, 2020). It has been used as a test object for the assessments of modern cryo-microscopes and processing methods during the last 5 years. In contrast to apoferritin bacterial secretion systems are typical examples of multi protein complexes exhibiting high flexibility owing to their functions relating to the transportation of small molecules, proteins, and DNA into the extracellular space or target cells. This makes their structural characterization extremely challenging (Barlow, Methods Mol Biol 532:397–411, 2009; Costa et al., Nat Rev Microbiol 13:343–359, 2015). The most feasible approach to reveal their spatial organization and functional modification is cryo-electron microscopy (EM). During the last decade, structural cryo-EM has become broadly used for the analysis of the bio-complexes that comprise multiple components and are not amenable to crystallization (Lyumkis, J Biol Chem 294:5181–5197, 2019; Orlova and Saibil, Methods Enzymol 482:321–341, 2010; Orlova and Saibil, Chem Rev 111(12):7710–7748, 2011).

In this review, we will describe the basics of sample preparation for cryo-EM, the principles of digital data collection, and the logistics of image analysis focusing on the common steps required for reconstructions of both small and large biological complexes together with refinement of their structures to nearly atomic resolution. The workflow of processing will be illustrated by examples of EM analysis of Type IV Secretion System.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wu M, Lander GC (2020) Present and emerging methodologies in Cryo-EM single-particle analysis. Biophys J 119(7):1281–1289. https://doi.org/10.1016/j.bpj.2020.08.027

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Baßler J, Hurt E (2019) Eukaryotic ribosome assembly. Annu Rev Biochem 88:281–306. https://doi.org/10.1146/annurev-biochem-013118-110817

    Article  CAS  PubMed  Google Scholar 

  3. Watson ZL, Ward FR, Méheust R et al (2020) Structure of the bacterial ribosome at 2 Å resolution. eLife 9:e60482. https://doi.org/10.7554/eLife.60482

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Orlova EV, Saibil HR (2010) Methods for three-dimensional reconstruction of heterogeneous assemblies. Methods Enzymol 482:321–341. https://doi.org/10.1016/S0076-6879(10)82013-0

    Article  CAS  PubMed  Google Scholar 

  5. Ludtke SJ (2016) Single-particle refinement and variability analysis in EMAN2.1. Methods Enzymol 579:159–189. https://doi.org/10.1016/bs.mie.2016.05.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen M, Ludtke SJ (2021) Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM. Nat Methods 18(8):930–936. https://doi.org/10.1038/s41592-021-01220-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rabuck-Gibbons JN, Lyumkis D et al (2022) Quantitative mining of compositional heterogeneity in cryo-EM datasets of ribosome assembly intermediates. Structure 30(4):498–509.e4. https://doi.org/10.1016/j.str.2021.12.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhong ED, Bepler T, Berger B et al (2021) CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Nat Methods 18:176–185. https://doi.org/10.1038/s41592-020-01049-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Scheres SH (2012) RELION: implementation of a Bayesian approach to cryo-EM structure determination. J Struct Biol 180:519–530. https://doi.org/10.1016/j.jsb.2012.09.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. de la Rosa-Trevin JM, Oton J, Marabini R et al (2013) Xmipp 3.0: an improved software suite for image processing in electron microscopy. J Struct Biol 184(2):321–328. https://doi.org/10.1016/j.jsb.2013.09.015

    Article  PubMed  Google Scholar 

  11. Punjani A, Rubinstein J, Fleet D et al (2017) CryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat Methods 14:290–296. https://doi.org/10.1038/nmeth.4169

    Article  CAS  PubMed  Google Scholar 

  12. Scheres SH (2016) Processing of structurally heterogeneous Cryo-EM data in RELION. Methods Enzymol 579:125–157. https://doi.org/10.1016/bs.mie.2016.04.012

    Article  CAS  PubMed  Google Scholar 

  13. Punjani A, Fleet DJ (2021) 3D variability analysis: resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM. J Struct Biol 213(2):107702. https://doi.org/10.1016/j.jsb.2021.107702

    Article  CAS  PubMed  Google Scholar 

  14. Bartesaghi A, Merk A, Banerjee S et al (2015) 2.2 A resolution cryo-EM structure of beta-galactosidase in complex with a cell-permeant inhibitor. Science 348(6239):1147–1151. https://doi.org/10.1126/science.aab1576

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Herzik MA, Wu M, Lander GC (2019) High-resolution structure determination of sub-100 kDa complexes using conventional cryo-EM. Nat Commun 10:1032. https://doi.org/10.1038/s41467-019-08991-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wu M, Lander GC, Herzik MA (2020) Sub-2 Angstrom resolution structure determination using single-particle cryo-EM at 200 keV. J Struct Biol X 4:100020. https://doi.org/10.1016/j.yjsbx.2020.100020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhang K, Li S, Chiu W et al (2019) Cryo-EM structure of a 40 kDa SAM-IV riboswitch RNA at 3.7 Å resolution. Nat Commun 10:5511. https://doi.org/10.1038/s41467-019-13494-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhang K, Pintilie GD, Li S, Schmid MF, Chiu W (2020) Resolving individual atoms of protein complex by cryo-electron microscopy. Cell Res 30(12):1136–1139. https://doi.org/10.1038/s41422-020-00432-2

    Article  PubMed  Google Scholar 

  19. Yip KM, Fischer N, Paknia E, Chari A, Stark H (2020) Atomic-resolution protein structure determination by cryo-EM. Nature 587(7832):157–161. https://doi.org/10.1038/s41586-020-2833-4

    Article  CAS  PubMed  Google Scholar 

  20. https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year

  21. Barlow M (2009) What antimicrobial resistance has taught us about horizontal gene transfer. Methods Mol Biol 532:397–411. https://doi.org/10.1007/978-1-60327-853-9_23

    Article  CAS  PubMed  Google Scholar 

  22. Costa T, Felisberto-Rodrigues C, Meir A et al (2015) Secretion systems in Gram-negative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13:343–359. https://doi.org/10.1038/nrmicro3456

    Article  CAS  PubMed  Google Scholar 

  23. Fronzes R, Schafer E, Wang L et al (2009) Structure of a type IV secretion system core complex. Science 323(5911):266–268. https://doi.org/10.1126/science.1166101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rivera-Calzada A, Fronzes R, Savva CG et al (2013) Structure of a bacterial type IV secretion core complex at subnanometre resolution. EMBO J 32(8):1195–1204. https://doi.org/10.1038/emboj.2013.58

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Low HH, Gubellini F, Rivera-Calzada A et al (2014) Structure of a type IV secretion system. Nature 508(7497):550–553. https://doi.org/10.1038/nature13081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Macé K, Vadakkepat AK, Redzej A et al (2022) Cryo-EM structure of a type IV secretion system. Nature 607(7917):191–196. https://doi.org/10.1038/s41586-022-04859-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ilangovan A, Connery S, Waksman G (2015) Structural biology of the Gram-negative bacterial conjugation systems. Trends Microbiol 23(5):301–310. https://doi.org/10.1016/j.tim.2015.02.012

    Article  CAS  PubMed  Google Scholar 

  28. Orlova EV, Saibil HR (2011) Structural analysis of macromolecular assemblies by electron microscopy. Chem Rev 111(12):7710–7748. https://doi.org/10.1021/cr100353t

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Frank J (2006) Three dimensional electron microscopy of macromolecular assemblies: visualization of biological molecules in their native state, 2nd edn. Oxford University Press, USA. https://doi.org/10.1093/acprof:oso/9780195182187.001.0001

    Book  Google Scholar 

  30. Reimer L (1997) Transmission electron microscopy, Springer Series in Optical Sciences, 2nd edn. Springer-Verlag, New York

    Book  Google Scholar 

  31. Spence JCH (2003) High resolution microscopy, 3rd edn. OUP Oxford, 2013

    Google Scholar 

  32. Dubochet J, Adrian M, Chang JJ et al (1988) Cryo-electron microscopy of vitrified specimens. Q Rev Biophys 21(2):129–228. https://doi.org/10.1017/s0033583500004297

    Article  CAS  PubMed  Google Scholar 

  33. Adrian M, Dubochet J, Lepault J et al (1984) Cryo-electron microscopy of viruses. Nature 308(5954):32–36

    Article  CAS  PubMed  Google Scholar 

  34. Lepault J, Dubochet J (1986) Electron microscopy of frozen hydrated specimens: preparation and characteristics. Methods Enzymol 127:719–730. https://doi.org/10.1016/0076-6879(86)27056-1

    Article  CAS  PubMed  Google Scholar 

  35. Vos MR, Bomans PH, Frederik PM et al (2008) The development of a glove-box/Vitrobot combination: air-water interface events visualized by cryo-TEM. Ultramicroscopy 108(11):1478–1483. https://doi.org/10.1016/j.ultramic.2008.03.01

    Article  CAS  PubMed  Google Scholar 

  36. Jaffe JS, Glaeser RM (1987) Difference Fourier analysis of “surface features” of bacteriorhodopsin using glucose-embedded and frozen-hydrated purple membrane. Ultramicroscopy 23(1):17–28. https://doi.org/10.1016/0304-3991(87)90223-3

    Article  CAS  PubMed  Google Scholar 

  37. Grassucci RA, Taylor DJ, Frank J (2007) Preparation of macromolecular complexes for cryo-electron microscopy. Nat Protoc 2(12):3239–3246. https://doi.org/10.1038/nprot.2007.452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ayache J., Beaunier L., Boumendil J et al (2010) Sample preparation handbook for transmission electron microscopy. https://doi.org/10.1007/978-1-4419-5975-1

  39. Cabra V, Samso M (2015) Do’s and don’ts of cryo-electron microscopy: a primer on sample preparation and high quality data collection for macromolecular 3D reconstruction. J Vis Exp 95:52311. https://doi.org/10.3791/52311

    Article  CAS  Google Scholar 

  40. Carragher B, Cheng Y, Frost A et al (2019) Current outcomes when optimizing ‘standard’ sample preparation for single-particle cryo-EM. J Microsc 276(1):39–45. https://doi.org/10.1111/jmi.12834

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Tivol WF, Briegel A, Jensen GJ (2008) An improved cryogen for plunge freezing. Microsc Microanal 14(5):375–379. https://doi.org/10.1017/S1431927608080781

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Levin BDA (2021) Direct detectors and their applications in electron microscopy for materials science. J Phys Mater 4:042005. https://doi.org/10.1088/2515-7639/ac0ff9

    Article  CAS  Google Scholar 

  43. Faruqi AR, Henderson R (2007) Electronic detectors for electron microscopy. Curr Opin Struct Biol 17(5):549–555. https://doi.org/10.1016/j.sbi.2007.08.014

    Article  CAS  PubMed  Google Scholar 

  44. McMullan G, Chen S, Henderson R et al (2009) Detective quantum efficiency of electron area detectors in electron microscopy. Ultramicroscopy 109(9):1126–1143. https://doi.org/10.1016/j.ultramic.2009.04.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Milazzo AC, Moldovan G, Lanman J et al (2010) Characterization of a direct detection device imaging camera for transmission electron microscopy. Ultramicroscopy 110(7):744–747. https://doi.org/10.1016/j.ultramic.2010.03.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bammes BE, Rochat RH, Jakana J et al (2012) Direct electron detection yields cryo-EM reconstructions at resolutions beyond 3/4 Nyquist frequency. J Struct Biol 177(3):589–601. https://doi.org/10.1016/j.jsb.2012.01.008

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ruskin RS, Yu Z, Grigorieff N (2013) Quantitative characterization of electron detectors for transmission electron microscopy. J Struct Biol 184(3):385–393. https://doi.org/10.1016/j.jsb.2013.10.016

    Article  CAS  PubMed  Google Scholar 

  48. Li X, Mooney P, Zheng S et al (2013) Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM. Nat Methods 10(6):584–590. https://doi.org/10.1038/nmeth.2472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. McMullan G, Faruqi AR, Clare D et al (2014) Comparison of optimal performance at 300keV of three direct electron detectors for use in low dose electron microscopy. Ultramicroscopy 147:156–163. https://doi.org/10.1016/j.ultramic.2014.08.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Campbell MG, Cheng A, Brilot AF et al (2012) Movies of ice-embedded particles enhance resolution in electron cryo-microscopy. Structure 20(11):1823–1828. https://doi.org/10.1016/j.str.2012.08.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Abrishami V, Vargas J, Li X et al (2015) Alignment of direct detection device micrographs using a robust optical flow approach. J Struct Biol 189(3):163–176. https://doi.org/10.1016/j.jsb.2015.02.00156

    Article  PubMed  Google Scholar 

  52. Scheres SHW (2014) Beam-induced motion correction for sub-megadalton cryo-EM particles. eLife 3:e03665. https://doi.org/10.7554/eLife.03665

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zheng S, Palovcak E, Armache JP et al (2017) MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat Methods 14:331–332. https://doi.org/10.1038/nmeth.4193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zivanov J, Nakane T, Scheres SHW (2019) A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis. IUCrJ 6(Pt 1):5–17. https://doi.org/10.1107/S205225251801463X

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Glaeser RM (1971) Limitations to significant information in biological electron microscopy as a result of radiation damage. J Ultrastruct Res 36(3):466–482. https://doi.org/10.1016/S0022-5320(71)80118-1

    Article  CAS  PubMed  Google Scholar 

  56. Taylor KA, Glaeser RM (1976) Electron microscopy of frozen hydrated biological specimens. J Ultrastruct Res 55(3):448–456. https://doi.org/10.1016/S0022-5320(76)80099-8

    Article  CAS  PubMed  Google Scholar 

  57. Knapek E, Dubochet J (1980) Beam damage to organic material is considerably reduced in cryo-electron microscopy. J Mol Biol 141(2):147–161. https://doi.org/10.1016/0022-2836(80)90382-4

    Article  CAS  PubMed  Google Scholar 

  58. Chiu W, Jeng TW (1982) Electron radiation sensitivity of protein crystals. Ultramicroscopy 10(1–2):63–69. https://doi.org/10.1016/0304-3991(82)90188-7

    Article  CAS  PubMed  Google Scholar 

  59. Chiu W (1986) Electron microscopy of frozen, hydrated biological specimens. Annu Rev Biophys Biophys Chem 15:237–257. https://doi.org/10.1146/annurev.bb.15.060186.001321

    Article  CAS  PubMed  Google Scholar 

  60. Burmeister WP (2000) Structural changes in a cryo-cooled protein crystal owing to radiation damage. Acta Crystallogr D Biol Crystallogr 56(Pt 3):328–341. https://doi.org/10.1107/s0907444999016261

    Article  CAS  PubMed  Google Scholar 

  61. Egerton RF, Li P, Malac M (2004) Radiation damage in the TEM and SEM. Micron 35(6):399–409. https://doi.org/10.1016/j.micron.2004.02.003

    Article  CAS  PubMed  Google Scholar 

  62. Bartesaghi A, Matthies D, Banerjee S et al (2014) Structure of beta-galactosidase at 3.2-A resolution obtained by cryo-electron microscopy. Proc Natl Acad Sci U S A 111(32):11709–11714. https://doi.org/10.1073/pnas.1402809111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Carlson DB, Evans JE (2012) Low-dose imaging techniques for transmission electron microscopy. The transmission electron microscope. InTech, China. https://doi.org/10.5772/36614

    Book  Google Scholar 

  64. Thon F (1966) Zur Defokussierungsabh ä ngigkeit des Phasenkontrastes bei der elektronenmikroskopischen Abbildung. Naturforschg 21a:476–478

    Article  Google Scholar 

  65. Wade RH (1992) A brief look at imaging and contrast transfer. Ultramicroscopy 46:145–156. https://doi.org/10.1016/0304-3991(92)90011-8

    Article  CAS  Google Scholar 

  66. Rohou A, Grigorieff N (2015) CTFFIND4: fast and accurate defocus estimation from electron micrographs. J Struct Biol 192(2):216–221. https://doi.org/10.1016/j.jsb.2015.08.00867

    Article  PubMed  PubMed Central  Google Scholar 

  67. Grant T, Rohou A, Grigorieff N et al (2018) cisTEM, user-friendly software for single-particle image processing. eLife 7:e35383. https://doi.org/10.7554/eLife.35383

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ludtke SJ (2016) Single-particle refinement and variability analysis in EMAN2.1. Methods Enzymol 579:159–189. Elsevier, United States. https://doi.org/10.1016/bs.mie.2016.05.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. van Heel M, Gowen B, Matadeen R et al (2000) Single-particle electron cryo-microscopy: towards atomic resolution. Q Rev Biophys 33:307–369. https://doi.org/10.1017/S0033583500003644

    Article  PubMed  Google Scholar 

  70. Wagner T, Merino F, Stabrin M et al (2019) SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun Biol 2:218. https://doi.org/10.1038/s42003-019-0437-z

    Article  PubMed  PubMed Central  Google Scholar 

  71. Wiener N (1964) Extrapolation, interpolation, and smoothing of stationary time series. Wiley, New York

    Google Scholar 

  72. Mancini EJ, Fuller SD (2000) Supplanting crystallography or supplementing microscopy? A combined approach to the study of an enveloped virus. Acta Crystallogr D Biol Crystallogr 56:1278–1287. https://doi.org/10.1107/S0907444900010817

    Article  CAS  PubMed  Google Scholar 

  73. Smith JM (1999) Ximdisp--A visualization tool to aid structure determination from electron microscope images. J Struct Biol 125(2–3, 223):–228. https://doi.org/10.1006/jsbi.1998.4073

  74. Scheres SH (2015) Semi-automated selection of cryo-EM particles in RELION-1.3. J Struct Biol 189(2):114–122. https://doi.org/10.1016/j.jsb.2014.11.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Zhang K (2017) Fully automatic accurate, convenient and extremely fast particle picking for EM. https://sbgrid.org/software/titles/gautomatch

  76. Langlois R, Pallesen J, Ash JT et al (2014) Automated particle picking for low-contrast macromolecules in cryo-electron microscopy. J Struct Biol 186(1):1–7. https://doi.org/10.1016/j.jsb.2014.03.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Heymann JB, Belnap DM (2007) Bsoft: image processing and molecular modeling for electron microscopy. J Struct Biol 157(1):3–18. https://doi.org/10.1016/j.jsb.2006.06.006

    Article  CAS  PubMed  Google Scholar 

  78. Roseman AM (2004) FindEM--a fast, efficient program for automatic selection of particles from electron micrographs. J Struct Biol 145(1–2):91–99

    Article  CAS  PubMed  Google Scholar 

  79. Voss NR, Yoshioka CK, Radermacher M et al (2009) DoG Picker and TiltPicker: software tools to facilitate particle selection in single particle electron microscopy. J Struct Biol 166(2):205–213. https://doi.org/10.1016/j.jsb.2009.01.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Bepler T, Morin A, Rapp M et al (2019) Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat Methods 16:1153–1160. https://doi.org/10.1038/s41592-019-0575-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Al-Azzawi A, Ouadou A, Max H et al (2020) DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM. BMC Bioinformatics 21:509. https://doi.org/10.1186/s12859-020-03809-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Tegunov D, Cramer P (2019) Real-time cryo-electron microscopy data preprocessing with warp. Nat Methods 16(11):1146–1152. https://doi.org/10.1038/s41592-019-0580-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Van Heel M, Portugal RV, Schatz M (2009) Multivariate statistical analysis in single particle (Cryo) electron microscopy. An electronic textbook: electron microscopy in Life Science. 3D-EM Network of Excellence

    Google Scholar 

  84. Sigworth FJ (1998) A maximum-likelihood approach to single-particle image refinement. J Struct Biol 122(3):328–339. https://doi.org/10.1006/jsbi.1998.4014

    Article  CAS  PubMed  Google Scholar 

  85. Sigworth FJ, Doerschuk PC, Carazo JM et al (2010) An introduction to maximum-likelihood methods in cryo-EM. Methods Enzymol 482:263–294. https://doi.org/10.1016/S0076-6879(10)82011-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Myung IJ (2003) Tutorial on maximum likelihood estimation. J Math Psyc 47(1):90–100. https://doi.org/10.1016/S0022-2496(02)00028-7

    Article  Google Scholar 

  87. Scheres SH (2010) Classification of structural heterogeneity by maximum-likelihood methods. Methods Enzymol 482:295–320. https://doi.org/10.1016/S0076-6879(10)82012-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. MacQueen (1967) J. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press; Berkeley, CA, USA Some methods for classification and analysis of multivariate observations,Volume 1 Statistics: 281–297

    Google Scholar 

  89. Hartigan JA, Wong MA (1979) Algorithm AS 136: a K-means clustering algorithm. J R Stat Soc Ser C (Appl Stat) 28:100. https://doi.org/10.2307/2346830

    Article  Google Scholar 

  90. Punjani A, Brubaker MA (2015) Microscopic advances with large-scale learning: stochastic optimization for cryo-EM. https://arxiv.org/pdf/1501.04656.pdf

  91. Al-Azzawi A, Ouadou A, Tanner JJ et al (2019) A super-clustering approach for fully automated single particle picking in Cryo-EM. Genes (Basel) 10(9):666. https://doi.org/10.3390/genes10090666

    Article  CAS  PubMed  Google Scholar 

  92. Chung JM, Durie CL, Lee J (2022) Artificial intelligence in cryo-electron microscopy. Life (Basel) 12:1267. https://doi.org/10.3390/life12081267

    Article  CAS  PubMed  Google Scholar 

  93. Botifoll M, Pinto-Huguet I, Arbiol J (2022) Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook. Nanoscale Horiz 7:1427–1477. https://doi.org/10.1039/D2NH00377E

    Article  CAS  PubMed  Google Scholar 

  94. Skalidis I, Kyrilis FL, Tüting C et al (2022) Cryo-EM and artificial intelligence visualize endogenous protein community members. Structure 30(4):575–589.e6. https://doi.org/10.1016/j.str.2022.01.001

    Article  CAS  PubMed  Google Scholar 

  95. Orlov SS (1976) Theory of three dimensional reconstruction – conditions of a complete set of projections. Sov Phys Crystallogr 20:312–314

    Google Scholar 

  96. Crowther RA, DeRosier DJ, Klug A (1970) The reconstruction of a three-dimensional structure from projections and its application to electron microscopy. Proc Roy Soc A 317:319–340. https://doi.org/10.1098/rspa.1970.0119

    Article  Google Scholar 

  97. Crowther RA (1971) Procedures for three-dimensional reconstruction of spherical viruses by Fourier synthesis from electron micrographs. Philos Trans R Soc Lond Ser B Biol Sci 261(837):221–230. https://doi.org/10.1098/rstb.1971.0054

    Article  CAS  Google Scholar 

  98. van Heel M, Orlova EV, Harauz G et al (1997) Angular reconstitution in three-dimentional electron microscopy: historical and theoretical aspects. Scanning Microsc 11:195–210

    Google Scholar 

  99. Fuller SD (1987) The T=4 envelope of Sindbis virus is organized by interactions with a complementary T=3 capsid. Cell 48(6):923–934. https://doi.org/10.1016/0092-8674(87)90701-x

    Article  CAS  PubMed  Google Scholar 

  100. De Rosier DJ, Klug A (1968) Reconstruction of three dimensional structures from electron micrographs. Nature 217(5124):130–134. https://doi.org/10.1038/217130a0

    Article  PubMed  Google Scholar 

  101. Harauz G, van Heel M (1986) Exact filters for general geometry three-dimensional reconstruction. Optik 73:146–156

    Google Scholar 

  102. Herman GT (1980) Image reconstruction from projections: the fundamentals of computerized tomography. Academic Press, New York. https://doi.org/10.1002/zamm.19830630227

    Book  Google Scholar 

  103. Penczek PA (2010) Fundamentals of three-dimensional reconstruction from projections. Methods Enzymol 482:1–33. https://doi.org/10.1016/S0076-6879(10)82001-4

    Article  PubMed  PubMed Central  Google Scholar 

  104. DeRosier DJ, Moore PB (1970) Reconstruction of three-dimensional images from electron micrographs of structures with helical symmetry. J Mol Biol 52(2):355–369. https://doi.org/10.1016/0022-2836(70)90036-7

    Article  CAS  PubMed  Google Scholar 

  105. Haselbach D, Schrader J, Lambrecht F et al (2017) Long-range allosteric regulation of the human 26S proteasome by 20S proteasome-targeting cancer drugs. Nat Commun 8:15578. https://doi.org/10.1038/ncomms15578

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Haselbach D, Komarov I, Agafonov DE et al (2018) Structure and conformational dynamics of human spliceosome B act complex. Cell 172:454–464. https://doi.org/10.1016/j.cell.2018.01.010

    Article  CAS  PubMed  Google Scholar 

  107. Glaeser RM, Downing KH, DeRosier DJ et al (2007) Electron crystallography of biological macromolecules. Oxford University Press, New York

    Book  Google Scholar 

  108. van Heel M, Schatz M (2005) Fourier shell correlation threshold criteria. J Struct Biol 151(3):250–262. https://doi.org/10.1016/j.jsb.2005.05.009

    Article  CAS  PubMed  Google Scholar 

  109. Rosenthal PB, Henderson R (2003) Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J Mol Biol 333(4):721–745. https://doi.org/10.1016/j.jmb.2003.07.013

    Article  CAS  PubMed  Google Scholar 

  110. Scheres SH, Chen S (2012) Prevention of overfitting in cryo-EM structure determination. Nat Methods 9(9):853–854. https://doi.org/10.1038/nmeth.2115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Chen S, McMullan G, Faruqi AR et al (2013) High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy. Ultramicroscopy 135:24–35. https://doi.org/10.1016/j.ultramic.2013.06.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Kucukelbir A, Sigworth FJ, Tagare HD (2014) Quantifying the local resolution of cryo-EM density maps. Nat Methods 11(1):63–65. https://doi.org/10.1038/nmeth.2727

    Article  CAS  PubMed  Google Scholar 

  113. Villa E, Lasker K (2014) Finding the right fit: chiseling structures out of cryo-electron microscopy maps. Curr Opin Struct Biol 25:118–125. https://doi.org/10.1016/j.sbi.2014.04.001

    Article  CAS  PubMed  Google Scholar 

  114. Topf M, Lasker K, Webb B et al (2008) Protein structure fitting and refinement guided by cryo-EM density. Structure 16(2):295–307. https://doi.org/10.1016/j.str.2007.11.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Brown A, Long F, Nicholls RA et al (2015) Tools for macromolecular model building and refinement into electron cryo-microscopy reconstructions. Acta Crystallogr D Biol Crystallogr 7(Pt 1):136–153. https://doi.org/10.1107/S1399004714021683

    Article  CAS  Google Scholar 

  116. Kelley LA, Mezulis S, Yates CM et al (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858. https://doi.org/10.1038/nprot.2015.053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Yang J, Yan R, Roy A, Xu D et al (2015) The I-TASSER suite: protein structure and function prediction. Nat Methods 12(1):7–8. https://doi.org/10.1038/nmeth.3213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Baek M, DiMaio F, Anishchenko I et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373(6557):871–876. https://doi.org/10.1126/science.abj8754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612. https://doi.org/10.1002/jcc.20084

    Article  CAS  PubMed  Google Scholar 

  121. Emsley P, Cowtan K (2004) Coot: model-building tools for molecular graphics. Acta Crystallogr D Biol Crystallogr 60(Pt12Pt1):2126. https://doi.org/10.1107/s0907444904019158

    Article  PubMed  Google Scholar 

  122. Lopéz-Blanco JR, Chacón P (2013) iMODFIT: efficient and robust flexible fitting based on vibrational analysis in internal coordinates. J Struct Biol 184(2):261–270. https://doi.org/10.1016/j.jsb.2013.08.010

    Article  PubMed  Google Scholar 

  123. Adams PD, Afonine PV, Bunkoczi G et al (2010) PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr D Biol Crystallogr 66(Pt2):213–221. https://doi.org/10.1107/S0907444909052925

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32:W526–W531. https://doi.org/10.1093/nar/gkh468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Zhu J, Cheng L, Fang Q et al (2010) Building and refining protein models within cryo-electron microscopy density maps based on homology modeling and multiscale structure refinement. J Mol Biol 397(3):835–851. https://doi.org/10.1016/j.jmb.2010.01.041

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Kovalevskiy O, Nicholls RA, Long F et al (2018) Overview of refinement procedures within REFMAC5: utilizing data from different sources. Acta Crystallogr D Struct Biol 74(Pt 3):215–227. https://doi.org/10.1107/S2059798318000979

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Williams CJ, Headd JJ, Moriarty NW et al (2018) MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci 27(1):293–315. https://doi.org/10.1002/pro.3330

    Article  CAS  PubMed  Google Scholar 

  128. Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99. https://doi.org/10.1016/s0022-2836(63)80023-6

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors thank Dr. T. Barrett for useful discussions allowing improvement of the manuscript. This work was funded by MRC grant MR/K012401/1 to E.V.O. The authors apologize for not completing the coverage of all methods owing to space constraints.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elena V. Orlova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Ignatiou, A., Macé, K., Redzej, A., Costa, T.R.D., Waksman, G., Orlova, E.V. (2024). Structural Analysis of Protein Complexes by Cryo-Electron Microscopy. In: Journet, L., Cascales, E. (eds) Bacterial Secretion Systems . Methods in Molecular Biology, vol 2715. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3445-5_27

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3445-5_27

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3444-8

  • Online ISBN: 978-1-0716-3445-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics