Mitigating local over-fitting during single particle reconstruction with SIDESPLITTER

Single particle analysis of cryo-EM images enables macromolecular structure determination at resolutions approaching the atomic scale. Experimental images are extremely noisy, however, and during iterative refinement it is possible to stably incorporate noise into the reconstructed density. Such “over-fitting” can lead to misinterpretation of the structure, and thereby flawed biological results. Several strategies are routinely used to prevent the spurious incorporation of noise within reconstructed volumes, the most common being independent refinement of two sides of a split dataset. In this study, we show that over-fitting remains an issue within regions of low local signal-to-noise in reconstructed volumes refined using the half-set strategy. We propose a modified filtering process during refinement through the application of a local signal-to-noise filter, SIDESPLITTER, which we show to be capable of reducing over-fitting in both idealised and experimental settings, while maintaining independence between the two sides of a split refinement. SIDESPLITTER can also improve the final resolution in refinements of structures prone to severe over-fitting, such as membrane proteins in detergent micelles.

[1]  J M Carazo,et al.  Three-dimensional reconstruction methods in Single Particle Analysis from transmission electron microscopy data. , 2015, Archives of biochemistry and biophysics.

[2]  R. Henderson,et al.  Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. , 2003, Journal of molecular biology.

[3]  Joachim Frank,et al.  Advances in the field of single-particle cryo-electron microscopy over the last decade , 2017, Nature Protocols.

[4]  M. Heel,et al.  Exact filters for general geometry three dimensional reconstruction , 1986 .

[5]  Dominika Elmlund,et al.  High-resolution cryo-EM: the nuts and bolts. , 2017, Current opinion in structural biology.

[6]  Dmitry Lyumkis,et al.  Challenges and opportunities in cryo-EM single-particle analysis , 2019, The Journal of Biological Chemistry.

[7]  Collaborative Computational,et al.  The CCP4 suite: programs for protein crystallography. , 1994, Acta crystallographica. Section D, Biological crystallography.

[8]  M. Baker,et al.  Outcome of the First Electron Microscopy Validation Task Force Meeting , 2012, Structure.

[9]  David J. Anderson,et al.  Ventromedial hypothalamic neurons control a defensive emotion state , 2015, eLife.

[10]  Guanghui Yang,et al.  Sampling the conformational space of the catalytic subunit of human γ-secretase , 2015, bioRxiv.

[11]  N Grigorieff,et al.  Resolution measurement in structures derived from single particles. , 2000, Acta crystallographica. Section D, Biological crystallography.

[12]  Shaoxia Chen,et al.  Prevention of overfitting in cryo-EM structure determination , 2012, Nature Methods.

[13]  P. Penczek.  Resolution measures in molecular electron microscopy. , 2010, Methods in enzymology.

[14]  A. Steven,et al.  One number does not fit all: mapping local variations in resolution in cryo-EM reconstructions. , 2013, Journal of structural biology.

[15]  N. Grigorieff,et al.  Noise bias in the refinement of structures derived from single particles. , 2004, Ultramicroscopy.

[16]  Hstau Y Liao,et al.  Definition and estimation of resolution in single-particle reconstructions. , 2010, Structure.

[17]  José María Carazo,et al.  MonoRes: Automatic and Accurate Estimation of Local Resolution for Electron Microscopy Maps. , 2018, Structure.

[18]  J. Frank,et al.  The ribosome at improved resolution: new techniques for merging and orientation refinement in 3D cryo-electron microscopy of biological particles. , 1994, Ultramicroscopy.

[19]  J M Carazo,et al.  Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead. , 2018, Current opinion in structural biology.

[20]  José María Carazo,et al.  Automatic local resolution-based sharpening of cryo-EM maps , 2018, bioRxiv.

[21]  Kailash Ramlaul,et al.  A Local Agreement Filtering Algorithm for Transmission EM Reconstructions , 2019, Journal of structural biology.

[22]  Jianpeng Ma,et al.  The crystal structure of murine p97/VCP at 3.6A. , 2003, Journal of structural biology.

[23]  R. Henderson,et al.  High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy☆ , 2013, Ultramicroscopy.

[24]  Randy J. Read,et al.  Acta Crystallographica Section D Biological , 2003 .

[25]  R. Bracewell.  Strip Integration in Radio Astronomy , 1956 .

[26]  Sjors H.W. Scheres,et al.  A Bayesian View on Cryo-EM Structure Determination , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[27]  Hemant D. Tagare,et al.  The Local Resolution of Cryo-EM Density Maps , 2013, Nature Methods.

[28]  Rafael Ayala,et al.  Structure and dynamics of the yeast SWR1-nucleosome complex , 2018, Science.

[29]  M. van Heel,et al.  Fourier shell correlation threshold criteria. , 2005, Journal of structural biology.

[30]  David J. Fleet,et al.  cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination , 2017, Nature Methods.

[31]  Dominika Elmlund,et al.  Cryogenic electron microscopy and single-particle analysis. , 2015, Annual review of biochemistry.

[32]  Sjors H.W. Scheres,et al.  RELION: Implementation of a Bayesian approach to cryo-EM structure determination , 2012, Journal of structural biology.

[33]  J. Jensen.  Sur les fonctions convexes et les inégalités entre les valeurs moyennes , 1906 .

[34]  Cryo-EM structure of the human L-type amino acid transporter 1 in complex with glycoprotein CD98hc , 2019 .

[35]  D. Wigley,et al.  Mechanism for nuclease regulation in RecBCD , 2016, eLife.