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Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction

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Punjani, Ali, Zhang, Haowei, Fleet, David J.·2020·Nature Methods
DOI 10.1038/s41592-020-00990-8

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Research works printed but not linked: 37

  1. Cheng, Y. Single-particle Cryo-EM at crystallographic resolution. Cell 161, 450–457 (2015). · DOI · Paper page
  2. Cheng, Y. Membrane protein structural biology in the era of single particle cryo-EM. Curr. Opin. Struct. Biol. 52, 58–63 (2018). · DOI
  3. Renaud, J.-P. et al. Cryo-EM in drug discovery: achievements, limitations and prospects. Nat. Rev. Drug Discov. 17, 471–492 (2018). · DOI · Paper page
  4. Scapin, G., Potter, C. S. & Carragher, B. Cryo-EM for small molecules discovery, design, understanding, and application. Cell Chem. Biol. 25, 1318–1325 (2018). · DOI
  5. Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. CryoSPARC: Algorithms for rapid unsupervised cryo-em structure determination. Nat. Methods 14, 290–296 (2017). · DOI · Paper page
  6. Grigorieff, N. FREEALIGN: High resolution refinement of single particle structures. J. Struct. Biol. 157, 117–125 (2007). · DOI · Paper page
  7. Scheres, S. H. W. & Bayesian, A. view on cryo-em structure determination. J. Mol. Biol. 415, 406–418 (2012). · DOI · Paper page
  8. Bell, J. M., Chen, M., Baldwin, P. R. & Ludtke, S. J. High resolution single particle refinement in EMAN2.1. Methods 100, 25–34 (2016). · DOI
  9. Grant, T., Rohou, A. & Grigorieff, N. cisTEM, user-friendly software for single-particle image processing. eLife 7, e35383 (2018). · DOI · Paper page
  10. Zivanov, J. et al. New tools for automated high-resolution cryo-em structure determination in RELION-3. eLife 7, e42166 (2018). · DOI · Paper page
  11. Chen, S. et al. High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy. Ultramicroscopy 135, 24–35 (2013). · DOI · Paper page
  12. Harauz, G. & van Heel, M. Exact filters for general geometry three dimensional reconstruction. Optik 73, 146–156 (1986).
  13. Scheres, S. H. W. & Chen, S. Prevention of overfitting in cryo-EM structure determination. Nat. methods 9, 853–854 (2012). · DOI · Paper page
  14. Paulsen, C. E., Armache, J. P., Gao, Y., Cheng, Y. & Julius, D. Structure of the TRPA1 ion channel suggests regulatory mechanisms. Nature 520, 511–517 (2015). · DOI · Paper page
  15. Golub, G. M., Heath, M. & Wahba, G. Generalized cross-validation as as a method for choosing a good ridge parameter. Technometrics 21, 215–223 (1979). · DOI · Paper page
  16. Wahba, G. A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem. Ann. Stat. 13, 1378–1402 (1985). · DOI
  17. Lehtinen, J. et al. Noise2Noise: learning image restoration without clean data. Proc. Mach. Learn. Res. 7, 4620–4631 (2018).
  18. 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 · Paper page
  19. Chen, Y. et al. Structure of the stra6 receptor for retinol uptake. Science 353, aad8266 (2016). · DOI · Paper page
  20. Kim, J. et al. Structure and drug resistance of the Plasmodium falciparum transporter PfCRT. Nature https://doi.org/10.1038/s41586-019-1795-x (2019). · DOI
  21. Xu, H. et al. Structural basis of Nav1.7 inhibition by a gating-modifier spider toxin. Cell https://doi.org/10.1016/j.cell.2018.12.018 (2019). · DOI
  22. Stagg, S., Noble, A., Spilman, M. & Chapman, M. Reslog plots as an empirical metric of the quality of cryo-EM reconstructions. J. Struct. Biol. 185, 418–426 (2014). · DOI
  23. Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014). · DOI · Paper page
  24. Vilas, J. L. et al. Monores: automatic and accurate estimation of local resolution for electron microscopy maps. Structure 26, 337–344 (2018). · DOI
  25. Felsberg, M. & Sommer, G. The monogenic signal. IEEE Trans. Signal Process. 49, 3136–3144 (2001). · DOI · Paper page
  26. Ramlaul, K., Palmer, C. M. & Aylett, C. H. S. A local agreement filtering algorithm for transmission EM reconstructions. J. Struct. Biol. 205, 30–40 (2019). · DOI
  27. Ramlaul, K., Palmer, C. M., Nakane, T. & Aylett, C. H. S. Mitigating local over-fitting during single particle reconstruction with SIDESPLITTER. J. Struct. Biol. 211, 107545 (2020). · DOI
  28. Vonesch, C., Wang, L., Shkolnisky, Y. and Singer, A. Fast wavelet-based single particle reconstruction in cryo-EM. Proc. IEEE Int. Symp. Biomed. Imaging https://doi.org/10.1109/ISBI.2011.5872791 (2011). · DOI
  29. Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. A Bayesian adaptive basis algorithm for single particle reconstruction. J. Struct. Biol. 179, 56–67 (2012). · DOI
  30. Hofmann, S. et al. Conformation space of a heterodimeric ABC exporter under turnover conditions. Nature 571, 580–583 (2019). · DOI
  31. Rogala, K. B. et al. Structural basis for the docking of mTORC1 on the lysosomal surface. Science 366, 468–475 (2019). · DOI · Paper page
  32. Gilman, M. S. A. et al. Structure of the respiratory syncytial virus polymerase complex. Cell 179, 193–204 (2019). · DOI
  33. Nguyen, A. H. et al. Structure of an endosomal signaling GPCR-G protein-β-arrestin megacomplex. Nat. Struct. Mol. Biol. 26, 1123–1131 (2019). · DOI
  34. Bepler, T., Noble, A. & Berger, B. Topaz-Denoise: general deep denoising models for cryoEM. Nat. Commun. 11, 5208 (2020). · DOI · Paper page
  35. Tegunov, D. & Cramer, P. Real-time cryo-electron microscopy data preprocessing with warp. Nat. Methods 16, 1146–1152 (2019). · DOI · Paper page
  36. Buchholz, T.-O., Jordan, M., Pigino, G. & Jug, F. cryoCARE: content-aware image restoration for cryo-transmission electron microscopy data. Preprint at arXiv https://arxiv.org/abs/1810.05420 (2018). · arXiv
  37. Potamianos, A. & Maragos, P. A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation. Signal Process. 37, 95–120 (1994). · DOI

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Metadata

Authors
Punjani, A., Zhang, H., & Fleet, D. J.
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2020-11-30
Volume
17
Issue
12
Pages
1214-1221
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-020-00990-8
OpenAlex
W3108495343
Cited by
1,683
References
6
FWCI
61.85
Type
journal article
Language
en
Open access
green
Licence
Funders
Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (USRA, RGPIN 2015-05630)

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Punjani, A., Zhang, H., & Fleet, D. J. (2020). Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nature Methods. https://doi.org/10.1038/s41592-020-00990-8

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