Cited by 30Advanced Electron Microscopy Techniques and ApplicationsEnzyme Structure and FunctionProtein Structure and Dynamics

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Cryo-EM structure and B-factor refinement with ensemble representation

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Abstract

Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. Typically, atomic models of the constituent molecules are fitted into these maps, followed by a density-guided refinement. We introduce TEMPy-ReFF, a method for atomic structure refinement in cryo-EM density maps. Our method represents atomic positions as components of a Gaussian mixture model, utilising their variances as B-factors, which are used to derive an ensemble description. Extensively tested on a substantial dataset of 229 cryo-EM maps from EMDB ranging in resolution from 2.1-4.9 Å with corresponding PDB and CERES atomic models, our results demonstrate that TEMPy-ReFF ensembles provide a superior representation of cryo-EM maps. On a single-model basis, it performs similarly to the CERES re-refinement protocol, although there are cases where it provides a better fit to the map. Furthermore, our method enables the creation of composite maps free of boundary artefacts. TEMPy-ReFF is useful for better interpretation of flexible structures, such as those involving RNA, DNA or ligands.

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Research works printed but not linked: 62

  1. van Zundert, GydoC. P. Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, the Netherlands & Bonvin, AlexandreM. J. J. Fast and sensitive rigid-body fitting into cryo-EM density maps with PowerFit. AIMS Biophys. 2, 73–87 (2015). · DOI
  2. Nicholls, R. A., Tykac, M., Kovalevskiy, O. & Murshudov, G. N. Current approaches for the fitting and refinement of atomic models into cryo-EM maps using CCP-EM. Acta Crystallogr. D Struct. Biol. 74, 492–505 (2018). · DOI
  3. 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, 561–570 (2012). · DOI
  4. Singharoy A. et al. Molecular dynamics-based refinement and validation for sub-5 Å cryo-electron microscopy maps. Elife. 5, e16105 (2016). · DOI
  5. Chen, J. Z., Fürst, J., Chapman, M. S. & Grigorieff, N. Low-resolution structure refinement in electron microscopy. J. Struct. Biol. 144, 144–151 (2003). · DOI
  6. Topf, M. et al. Protein structure fitting and refinement guided by cryo-EM density. Structure 16, 295–307 (2008). · DOI
  7. Kawabata, T. Multiple subunit fitting into a low-resolution density map of a macromolecular complex using a gaussian mixture model. Biophys. J. 95, 4643–4658 (2008). · DOI
  8. Kawabata, T. Gaussian-input Gaussian mixture model for representing density maps and atomic models. J. Struct. Biol. 203, 1–16 (2018). · DOI
  9. Igaev, M., Kutzner, C., Bock, L. V., Vaiana, A. C. & Grubmüller, H. Automated cryo-EM structure refinement using correlation-driven molecular dynamics. Elife 8, https://doi.org/10.7554/eLife.43542 (2019). · DOI
  10. Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D Struct. Biol. 74, 531–544 (2018). · DOI
  11. Lopéz-Blanco, J. R. & Chacón, P. iMODFIT: efficient and robust flexible fitting based on vibrational analysis in internal coordinates. J. Struct. Biol. 184, 261–270 (2013). · DOI
  12. Tama, F., Miyashita, O. & Brooks, C. L. 3rd Normal mode based flexible fitting of high-resolution structure into low-resolution experimental data from cryo-EM. J. Struct. Biol. 147, 315–326 (2004). · DOI
  13. Wang R. Y. R. et al. Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta. Elife. 5, e17219 (2016). · DOI · Paper page
  14. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010). · DOI · Paper page
  15. Croll, T. I. ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps. Acta Crystallogr. D Struct. Biol 74, 519–530 (2018). · DOI
  16. Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012). · DOI
  17. Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015). · DOI
  18. Klaholz, B. P. Deriving and refining atomic models in crystallography and cryo-EM: the latest Phenix tools to facilitate structure analysis. Acta Crystallogr. D Biol. Crystallogr. 75, 878–881 (2019). · DOI
  19. Nakane, T., Kimanius, D., Lindahl, E. & Scheres, S. H. Characterisation of molecular motions in cryo-EM single-particle data by multi-body refinement in RELION. Elife 7, https://doi.org/10.7554/eLife.36861 (2018). · DOI
  20. Farrell, D. P. et al. Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM. IUCrJ 7, 881–892 (2020). · DOI
  21. Lukoyanova, N. et al. Conformational changes during pore formation by the perforin-related protein pleurotolysin. PLoS Biol. 13, e1002049 (2015). · DOI
  22. Farabella, I. et al. TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits. J. Appl. Crystallogr. 48, 1314–1323 (2015). · DOI
  23. Sachse, C. et al. High-resolution electron microscopy of helical specimens: a fresh look at tobacco mosaic virus. J. Mol. Biol. 371, 812–835 (2007). · DOI
  24. Mendez, J. H. & Stagg, S. M. Assessing the quality of single particle reconstructions by atomic model building. J. Struct. Biol. 204, 276–282 (2018). · DOI
  25. Herzik, M. A. Jr, Fraser, J. S. & Lander, G. C. A multi-model approach to assessing local and global Cryo-EM map quality. Structure 27, 344–358.e3 (2019). · DOI
  26. Pintilie, G., Chen, D. H., Haase-Pettingell, C. A., King, J. A. & Chiu, W. Resolution and probabilistic models of components in CryoEM maps of mature P22 bacteriophage. Biophys. J. 110, 827–839 (2016). · DOI
  27. Nierzwicki, Ł. & Palermo, G. Molecular dynamics to predict Cryo-EM: capturing transitions and short-lived conformational states of biomolecules. Front. Mol. Biosci. 8, 641208 (2021). · DOI
  28. Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000). · DOI · Paper page
  29. Lawson, C. L. et al. EMDataBank unified data resource for 3DEM. Nucleic Acids Res. 44, D396–D403 (2016). · DOI
  30. Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D Biol. Crystallogr. 75, 861–877 (2019). · DOI · Paper page
  31. Bonomi, M. et al. Bayesian weighing of electron cryo-microscopy data for integrative structural modeling. Structure 27, 175–188.e6 (2019). · DOI
  32. Liu, D. C. & Nocedal, J. On the limited memory BFGS method for large scale optimization. Math. Program 45, 503–528 (1989). · DOI
  33. Rieping, W., Habeck, M. & Nilges, M. Inferential structure determination. Science 309, 303–306 (2005). · DOI
  34. Joseph, A. P. et al. Refinement of atomic models in high resolution EM reconstructions using Flex-EM and local assessment. Methods 100, 42–49 (2016). · DOI
  35. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010). · DOI · Paper page
  36. Prisant, M. G., Williams, C. J., Chen, V. B., Richardson, J. S. & Richardson, D. C. New tools in MolProbity validation: CaBLAM for CryoEM backbone, UnDowser to rethink “waters,” and NGL Viewer to recapture online 3D graphics. Protein Sci. 29, 315–329 (2020). · DOI
  37. Liebschner, D. et al. CERES: a cryo-EM re-refinement system for continuous improvement of deposited models. Acta Crystallogr. D Biol. Crystallogr. 77, 48–61 (2021). · DOI
  38. Ellinghaus, T. L., Marcellino, T., Srinivasan, V., Lill, R. & Kühlbrandt, W. Conformational changes in the yeast mitochondrial ABC transporter Atm1 during the transport cycle. Sci. Adv. 7, eabk2392 (2021). · DOI
  39. McGreevy, R., Teo, I., Singharoy, A. & Schulten, K. Advances in the molecular dynamics flexible fitting method for cryo-EM modeling. Methods 100, 50–60 (2016). · DOI
  40. Malhotra S. et al. RIBFIND2: identifying rigid bodies in protein and nucleic acid structures. Nucleic Acids Res. 51, gkad721 (2023). · DOI
  41. Pandurangan, A. P. & Topf, M. Finding rigid bodies in protein structures: application to flexible fitting into cryoEM maps. J. Struct. Biol. 177, 520–531 (2012). · DOI
  42. Mulvaney, T. et al. CASP15 cryo-EM protein and RNA targets: refinement and analysis using experimental maps. Proteins 91, 1935–1951 (2023). · DOI
  43. Sedzicki, J. et al. Mechanism of cyclic β-glucan export by ABC transporter Cgt of Brucella. Nat. Struct. Mol. Biol. 29, 1170–1177 (2022). · DOI
  44. Zhao, Z. et al. Near-atomic architecture of Singapore grouper iridovirus and implications for giant virus assembly. Nat. Commun. 14, 2050 (2023). · DOI
  45. Pettersen, E. F. et al. UCSF chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004). · DOI
  46. Chen, Y. et al. Allosteric transcription stimulation by RNA polymerase II super elongation complex. Mol. Cell 81, 3386–3399.e10 (2021). · DOI
  47. Hoffmann, N. A. et al. Molecular structures of unbound and transcribing RNA polymerase III. Nature 528, 231–236 (2015). · DOI
  48. Lawson, C. L. et al. Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Nat. Methods 18, 156–164 (2021). · DOI
  49. Farnung, L., Ochmann, M. & Cramer, P. Nucleosome-CHD4 chromatin remodeler structure maps human disease mutations. Elife 9, https://doi.org/10.7554/eLife.56178 (2020). · DOI
  50. Gao, Y. et al. Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science 368, 779–782 (2020). · DOI
  51. Heymann, J. B. Single-particle reconstruction statistics: a diagnostic tool in solving biomolecular structures by cryo-EM. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 75, 33–44 (2019). · DOI
  52. Chen, Z. & Chapman, M. S. Conformational disorder of proteins assessed by real-space molecular dynamics refinement. Biophys. J. 80, 1466–1472 (2001). · DOI
  53. Robin, X. et al. Continuous Automated Model EvaluatiOn (CAMEO)-perspectives on the future of fully automated evaluation of structure prediction methods. Proteins 89, 1977–1986 (2021). · DOI
  54. Bishop C. M. Pattern Recognition and Machine Learning. Springer New York. Accessed September 26, 2023. https://link.springer.com/book/9780387310732.
  55. Murshudov, G. N. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr. D Biol. Crystallogr. 67, 355–367 (2011). · DOI
  56. Nguyen, H., Roe, D. R. & Simmerling, C. Improved generalized born solvent model parameters for protein simulations. J. Chem. Theory Comput. 9, 2020–2034 (2013). · DOI
  57. Cardone, G., Heymann, J. B. & Steven, A. C. One number does not fit all: Mapping local variations in resolution in cryo-EM reconstructions. J. Struct. Biol. 184, 226–236 (2013). · DOI
  58. Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014). · DOI
  59. Lawson, C. L. et al. Emdatabank.org: unified data resource for CryoEM. Nucleic Acids Res. 39, D456–D464 (2011). · DOI
  60. Pettersen, E. F. et al. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci 30, 70–82 (2021). · DOI
  61. Cragnolini, T. et al. TEMPy2: a Python library with improved 3D electron microscopy density-fitting and validation workflows. Acta Crystallogr. D Biol. Crystallogr. 77, 41–47 (2021). · DOI
  62. Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018). · DOI · Paper page

Metadata

Authors
Beton, J. G., Mulvaney, T., Cragnolini, T., & Topf, M.
Affiliations
Leibniz Institute of Virology (LIV) (DE)
Corresponding author
Topf, Maya
Venue
Nature Communications
Published
2024-01-10
Volume
15
Issue
1
Pages
444-444
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4390708062
Cited by
30
References
6
Citations by year
2026: 12 · 2025: 15 · 2024: 2
FWCI
5.28
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron)
Funders
  • Wellcome Trust (209250/Z/17/Z))
  • Leibniz-Gemeinschaft
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
cryo-EM density maps, Debye-Waller factor, Gaussian mixture model, ensemble representations, composite map
MeSH terms
Adenosine Monophosphate, Humans, RNA, Seizures, Normal Distribution, Artifacts, Cryoelectron Microscopy

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