Skip to main content
SpringerLink
Log in
Book cover

cryoEM pp 189–223Cite as

Automated Modeling and Validation of Protein Complexes in Cryo-EM Maps

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2215))

Abstract

The resolving power of cryo-EM experiments has dramatically improved in recent years. However, many cryo-EM maps may still not achieve a resolution that is sufficiently high to allow model building directly from the map. Instead, it is common practice to fit an initial atomic model to the map and refine this model. Depending on the resolution and whether the structure suffers from inherent flexibility or experimental limitations, different methods can be applied, to obtain high-quality, well-fitted atomic model of the macromolecular assembly represented by the map, and to assess its properties. In this review, we describe some of these methods, with the main focus on those that have been developed in our group over the last decade.

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

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Moult J, Fidelis K, Kryshtafovych A et al (2018) Critical assessment of methods of protein structure prediction (CASP)-round XII. Proteins Struct Funct Bioinforma 86:7–15

    Article  CAS  Google Scholar 

  2. Webb B, Sali A (2014) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 47:5.6.1–5.6.32

    Article  Google Scholar 

  3. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF chimera?A visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  PubMed  Google Scholar 

  4. Wriggers W, Milligan RA, McCammon JA (1999) Situs: a package for docking crystal structures into low-resolution maps from Electron microscopy. J Struct Biol 125:185–195

    Article  CAS  PubMed  Google Scholar 

  5. Topf M, Lasker K, Webb B et al (2008) Protein structure fitting and refinement guided by Cryo-EM density. Structure 16:295–307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tama F, Miyashita O, Brooks CL (2004) Flexible multi-scale fitting of atomic structures into low-resolution Electron density maps with elastic network Normal mode analysis. J Mol Biol 337:985–999

    Article  CAS  PubMed  Google Scholar 

  7. Cardone G, Heymann JB, Steven AC (2013) One number does not fit all: mapping local variations in resolution in cryo-EM reconstructions. J Struct Biol 184:226–236

    Article  PubMed  Google Scholar 

  8. Herzik MA, Fraser JS, Lander GC (2019) A multi-model approach to assessing local and global Cryo-EM map quality. Structure 27:344–358.e3

    Article  CAS  PubMed  Google Scholar 

  9. Roseman AM (2000) Docking structures of domains into maps from cryo-electron microscopy using local correlation. Acta Crystallogr D Biol Crystallogr 56:1332–1340

    Article  CAS  PubMed  Google Scholar 

  10. Vasishtan D, Topf M (2011) Scoring functions for cryoEM density fitting. J Struct Biol 174:333–343

    Article  CAS  PubMed  Google Scholar 

  11. Joseph AP, Lagerstedt I, Patwardhan A et al (2017) Improved metrics for comparing structures of macromolecular assemblies determined by 3D electron-microscopy. J Struct Biol 199:12–26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hryc CF, Jeong H-H, Fei X et al (2017) Subunit conformational variation within individual GroEL oligomers resolved by Cryo-EM. Proc Natl Acad Sci 114:8259–8264

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Estevez PA, Tesmer M, Perez CA et al (2009) Normalized mutual information feature selection. IEEE Trans Neural Netw 20:189–201

    Article  PubMed  Google Scholar 

  14. Studholme C, Hill DLG, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32:71–86

    Article  Google Scholar 

  15. Kryshtafovych A, Malhotra S, Monastyrskyy B et al (2019) Cryo-EM targets in CASP13: overview and evaluation of results. Proteins 87:1128–1140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Joseph AP, Malhotra S, Burnley T et al (2016) Refinement of atomic models in high resolution EM reconstructions using flex-EM and local assessment. Methods 100:42–49

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Farabella I, Vasishtan D, Joseph AP et al (2015) TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits. J Appl Crystallogr 48:1314–1323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Afonine PV, Poon BK, Read RJ et al (2018) Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr Sect Struct Biol 74:531–544

    Article  CAS  Google Scholar 

  19. Emsley P, Lohkamp B, Scott WG et al (2010) Features and development of Coot. Acta Crystallogr D Biol Crystallogr 66:486–501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Song Y, DiMaio F, Wang RY-R et al (2013) High-resolution comparative modeling with RosettaCM. Structure 21:1735–1742

    Article  CAS  PubMed  Google Scholar 

  21. Waterhouse A, Bertoni M, Bienert S et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46:W296–W303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Atherton J, Yu I-MI-M, Cook A et al (2017) The divergent mitotic kinesin MKLP2 exhibits atypical structure and mechanochemistry. elife 6:e27793

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637

    Article  CAS  PubMed  Google Scholar 

  24. Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27:343–350

    Article  CAS  PubMed  Google Scholar 

  25. Topf M, Baker ML, John B et al (2005) Structural characterization of components of protein assemblies by comparative modeling and electron cryo-microscopy. J Struct Biol 149:191–203

    Article  CAS  PubMed  Google Scholar 

  26. Pintilie GD, Zhang J, Goddard TD et al (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 170:427–438

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wu X, Milne JLS, Borgnia MJ et al (2003) A core-weighted fitting method for docking atomic structures into low-resolution maps: application to cryo-electron microscopy. J Struct Biol 141:63–76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kim SJ, Fernandez-Martinez J, Nudelman I et al (2018) Integrative structure and functional anatomy of a nuclear pore complex. Nature 555:475–482

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kuzu G, Keskin O, Nussinov R et al (2016) PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps. Acta Crystallogr Sect Struct Biol 72:1137–1148

    Article  CAS  Google Scholar 

  30. Chandramouli P, Topf M, Ménétret J-F et al (2008) Structure of the mammalian 80S ribosome at 8.7 Å resolution. Structure 16:535–548

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Taylor DJ, Devkota B, Huang AD et al (2009) Comprehensive molecular structure of the eukaryotic ribosome. Structure 17:1591–1604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kawabata T (2008) Multiple subunit fitting into a low-resolution density map of a macromolecular complex using a Gaussian mixture model. Biophys J 95:4643–4658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tjioe E, Lasker K, Webb B et al (2011) MultiFit: a web server for fitting multiple protein structures into their electron microscopy density map. Nucleic Acids Res 39:W167–W170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pandurangan AP, Vasishtan D, Alber F et al (2015) γ-TEMPy: simultaneous fitting of components in 3D-EM maps of their assembly using a genetic algorithm. Structure 23:2365–2376

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhang S, Vasishtan D, Xu M et al (2010) A fast mathematical programming procedure for simultaneous fitting of assembly components into cryoEM density maps. Bioinformatics 26:261–268

    Article  CAS  Google Scholar 

  36. Zeev-Ben-Mordehai T, Vasishtan D, Hernández Durán A et al (2016) Two distinct trimeric conformations of natively membrane-anchored full-length herpes simplex virus 1 glycoprotein B. Proc Natl Acad Sci 113:4176–4181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Baker ML, Jiang W, Wedemeyer WJ et al (2006) Ab initio modeling of the Herpesvirus VP26 Core domain assessed by CryoEM density. PLoS Comput Biol 2:12

    Article  CAS  Google Scholar 

  38. Sachse C, Chen JZ, Coureux P-D et al (2007) High-resolution Electron microscopy of helical specimens: a fresh look at tobacco mosaic virus. J Mol Biol 371:812–835

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lukoyanova N, Kondos SC, Farabella I et al (2015) Conformational changes during pore formation by the Perforin-related protein Pleurotolysin. PLoS Biol 13:e1002049

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Chen Z, Chapman MS (2001) Conformational disorder of proteins assessed by real-space molecular dynamics refinement. Biophys J 80:1466–1472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Pandurangan AP, Topf M (2012) RIBFIND: a web server for identifying rigid bodies in protein structures and to aid flexible fitting into cryo EM maps. Bioinformatics 28:2391–2393

    Article  CAS  PubMed  Google Scholar 

  42. Pandurangan AP, Topf M (2012) Finding rigid bodies in protein structures: application to flexible fitting into cryoEM maps. J Struct Biol 177:520–531

    Article  CAS  PubMed  Google Scholar 

  43. Zhou R, Yang G, Guo X et al (2019) Recognition of the amyloid precursor protein by human γ-secretase. Science 363:eaaw0930

    Article  CAS  PubMed  Google Scholar 

  44. Yang G, Zhou R, Zhou Q et al (2019) Structural basis of notch recognition by human γ-secretase. Nature 565:192

    Article  CAS  PubMed  Google Scholar 

  45. Clare DK, Vasishtan D, Stagg S et al (2012) ATP-triggered conformational changes delineate substrate-binding and -folding mechanics of the GroEL Chaperonin. Cell 149:113–123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Trabuco LG, Villa E, Mitra K et al (2008) Flexible fitting of atomic structures into Electron microscopy maps using molecular dynamics. Structure 16:673–683

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Trabuco LG, Villa E, Schreiner E et al (2009) Molecular dynamics flexible fitting: a practical guide to combine cryo-electron microscopy and X-ray crystallography. Methods 49:174–180

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Alford RF, Leaver-Fay A, Jeliazkov JR et al (2017) The Rosetta all-atom energy function for macromolecular modeling and design. J Chem Theory Comput 13:3031–3048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. DiMaio F, Tyka MD, Baker ML et al (2009) Refinement of protein structures into low-resolution density maps using Rosetta. J Mol Biol 392:181–190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Tama F, Miyashita O, Brooks CL III (2004) Normal mode based flexible fitting of high-resolution structure into low-resolution experimental data from cryo-EM. J Struct Biol 147:315–326

    Article  CAS  PubMed  Google Scholar 

  51. Lopéz-Blanco JR, Chacón P (2013) iMODFIT: efficient and robust flexible fitting based on vibrational analysis in internal coordinates. J Struct Biol 184:261–270

    Article  PubMed  Google Scholar 

  52. Ahmed A, Whitford PC, Sanbonmatsu KY et al (2012) Consensus among flexible fitting approaches improves the interpretation of cryo-EM data. J Struct Biol 177:561–570

    Article  PubMed  Google Scholar 

  53. Pandurangan AP, Shakeel S, Butcher SJ et al (2014) Combined approaches to flexible fitting and assessment in virus capsids undergoing conformational change. J Struct Biol 185:427–439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Newcombe J, Chatzidaki A, Sheppard TD et al (2018) Diversity of nicotinic acetylcholine receptor positive allosteric modulators revealed by mutagenesis and a revised structural model. Mol Pharmacol 93:128–140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Locke J, Joseph AP, Peña A et al (2017) Structural basis of human kinesin-8 function and inhibition. Proc Natl Acad Sci 114:E9539–E9548

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Canutescu AA, Dunbrack RL (2003) Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci 12:963–972

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang C, Bradley P, Baker D (2007) Protein–protein docking with backbone flexibility. J Mol Biol 373:503–519

    Article  CAS  PubMed  Google Scholar 

  58. Mandell DJ, Coutsias EA, Kortemme T (2009) Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat Methods 6:551–552

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Scaiola A, Leibundgut M, Boehringer D et al (2019) Structural basis of translation inhibition by cadazolid, a novel quinoxolidinone antibiotic. Sci Rep 9:5634

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Verdonk ML, Cole JC, Hartshorn MJ et al (2003) Improved protein–ligand docking using GOLD. Proteins Struct Funct Bioinforma 52:609–623

    Article  CAS  Google Scholar 

  61. Dominguez C, Boelens R, Bonvin AMJJ (2003) HADDOCK: a protein−protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737

    Article  CAS  PubMed  Google Scholar 

  62. Wang Z, Sun H, Yao X et al (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18:12964–12975

    Article  CAS  PubMed  Google Scholar 

  63. Houston DR, Walkinshaw MD (2013) Consensus docking: improving the reliability of docking in a virtual screening context. J Chem Inf Model 53:384–390

    Article  CAS  PubMed  Google Scholar 

  64. Catarinella M, Grüner T, Strittmatter T et al (2009) BTB-1: a small molecule inhibitor of the mitotic motor protein Kif18A. Angew Chem Int Ed 48:9072–9076

    Article  CAS  Google Scholar 

  65. Kucukelbir A, Sigworth FJ, Tagare HD (2014) Quantifying the local resolution of cryo-EM density maps. Nat Methods 11:63–65

    Article  CAS  PubMed  Google Scholar 

  66. Shen M, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Uziela K, Menéndez Hurtado D, Shu N et al (2017) ProQ3D: improved model quality assessments using deep learning. Bioinformatics 33:1578–1580

    CAS  PubMed  Google Scholar 

  68. Elofsson A, Joo K, Keasar C et al (2018) Methods for estimation of model accuracy in CASP12. Proteins Struct Funct Bioinforma 86:361–373

    Article  CAS  Google Scholar 

  69. Joseph AP, Polles G, Alber F et al (2017) Integrative modelling of cellular assemblies. Curr Opin Struct Biol 46:102–109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Bullock JMA, Schwab J, Thalassinos K et al (2016) The importance of non-accessible crosslinks and solvent accessible surface distance in modeling proteins with restraints from crosslinking mass spectrometry. Mol Cell Proteomics 15:2491–2500

    Article  CAS  PubMed Central  Google Scholar 

  71. Bullock JMA, Thalassinos K, Topf M (2018) Jwalk and MNXL web server: model validation using restraints from crosslinking mass spectrometry. Bioinformatics 34:3584–3585

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Bullock JMA, Sen N, Thalassinos K et al (2018) Modeling protein complexes using restraints from crosslinking mass spectrometry. Structure 26:1015–1024.e2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Barad BA, Echols N, Wang RY-R et al (2015) EMRinger: side chain–directed model and map validation for 3D cryo-electron microscopy. Nat Methods 12:943–946

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Afonine PV, Klaholz BP, Moriarty NW et al (2018) New tools for the analysis and validation of cryo-EM maps and atomic models. Acta Crystallogr Sect Struct Biol 74:814–840

    Article  CAS  Google Scholar 

  75. Chen VB, Arendall WB, Headd JJ et al (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66:12–21

    Article  CAS  PubMed  Google Scholar 

  76. Atherton J, Jiang K, Stangier MM et al (2017) A structural model for microtubule minus-end recognition and protection by CAMSAP proteins. Nat Struct Mol Biol 24:931–943

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Richardson JS, Williams CJ, Hintze BJ et al (2018) Model validation: local diagnosis, correction and when to quit. Acta Crystallogr Sect Struct Biol 74:132–142

    Article  CAS  Google Scholar 

  78. Webb B, Sali A (2014) Protein structure modeling with MODELLER. In: Kihara D (ed) Protein structure prediction. Springer, New York, NY, pp 1–15

    Google Scholar 

  79. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Peng J, Xu J (2011) Raptorx: exploiting structure information for protein alignment by statistical inference. Proteins Struct Funct Bioinforma 79:161–171

    Article  CAS  Google Scholar 

  81. Schaarschmidt J, Monastyrskyy B, Kryshtafovych A et al (2018) Assessment of contact predictions in CASP12: co-evolution and deep learning coming of age. Proteins Struct Funct Bioinforma 86:51–66

    Article  CAS  Google Scholar 

  82. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Burnley T, Palmer CM, Winn M (2017) Recent developments in the CCP-EM software suite. Acta Crystallogr Sect Struct Biol 73:469–477

    Article  CAS  Google Scholar 

  84. Jones A, Bland-Hawthorn J, Shopbell P (1995) Towards a general definition for spectroscopic resolution, In: Astronomical data analysis software and systems IV. ASP Conf Ser 77:503

    Google Scholar 

  85. Liao HY, Frank J (2010) Definition and estimation of resolution in single-particle reconstructions. Structure 18:768–775

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Chacón P, Wriggers W (2002) Multi-resolution contour-based fitting of macromolecular structures. J Mol Biol 317:375–384

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We are grateful for funding from the Wellcome Trust (209250/Z/17/Z and 208398/Z/17/Z) and the Medical Research Council Doctoral Training Programme (UCL). We thank the Topf group and CCP-EM team for their help with software development.

Author information

Authors and Affiliations

  1. Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London, UK

    Tristan Cragnolini, Aaron Sweeney & Maya Topf

Authors
  1. Tristan Cragnolini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aaron Sweeney
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maya Topf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maya Topf .

Editor information

Editors and Affiliations

  1. Howard Hughes Medical Institute Departments of Biological Chemistry and Physiology, University of California, Los Angeles, Los Angeles, CA, USA

    Tamir Gonen

  2. Chemical Engineering, School for Engineering of Matter, Transport and Energy; Center for Applied Structural Discovery Biodesign Institute, Arizona State University, Tempe, AZ, USA

    Brent L. Nannenga

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Cragnolini, T., Sweeney, A., Topf, M. (2021). Automated Modeling and Validation of Protein Complexes in Cryo-EM Maps. In: Gonen, T., Nannenga, B.L. (eds) cryoEM. Methods in Molecular Biology, vol 2215. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0966-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0966-8_9

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0965-1

  • Online ISBN: 978-1-0716-0966-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation