Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

Vilas, José Luis; Tabassum, Nazia; Mota, Javier; Maluenda, David; Jiménez-Moreno, Amaya; Majtner, Tomáš; Carazo, José Marı́a; Acton, Scott T.; Sorzano, C.O.S.

doi:10.1016/j.sbi.2018.11.004

Cited by 16 publications

(16 citation statements)

References 178 publications

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“…It is well known when the solution of global optimum is not guaranteed, initial values can drastically impact the final solution 28 , 29 for which strategies of setting best initial values are being actively developed. In this light, our finding implies that the pre-processing may have set better initial values to predestine the investigated classification algorithms to escape from some, if not all, local minimums 30 . Given so, it explains why the pre-processor can benefit RELION, ISAC, CL2D 10 (Supplementary Table 1 , 2 and Supplementary Figs.…”

Section: Discussionmentioning

confidence: 85%

Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

et al. 2020

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2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.

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Section: Discussionmentioning

confidence: 85%

Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

et al. 2020

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“…Finally, a number of other errors, such as incorrect parameter estimation (for example in determination of the CTF, or contrast transfer function), optical aberrations, and temporal variations, (e.g. uncorrected beam-induced motion), all contribute to further degradation of the signal, resulting in data with an extremely low signal-to-noise ratio (SNR) (Liao & Frank, 2010;Penczek, 2010;Vilas et al, 2018b).…”

Section: The Independent 3d Refinement Of Two Halves Of a Split Datasmentioning

confidence: 99%

“…The success of electron cryo-microscopy (cryo-EM) as a structure determination technique is underpinned by the process of single-particle analysis, which entails the three-dimensional (3D) reconstruction of macromolecular electron density from thousands or millions of 2D projection images of single particles (Elmlund & Elmlund, 2015;Carazo et al, 2015;Vilas et al, 2018b;Lyumkis, 2019).…”

Section: Introductionmentioning

confidence: 99%

Mitigating Local Over-fitting During Single Particle Reconstruction with SIDESPLITTER

Ramlaul¹,

Palmer

Aylett

2019

Preprint

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Abbreviations2D/3D -2/3-Dimensional; Cryo-EM -Electron Cryo-Microscopy; EM -Electron microscopy; FSC -Fourier shell correlation; LAFTER -Local Agreement Filter for Transmission EM Reconstructions; SNR -Signal to noise ratio. AbstractSingle 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.

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“…Technological developments are enabling cryogenic electron microscopy (cryo-EM) reconstruction of macromolecules to increasingly high resolutions ( Frank, 2017 , Elmlund et al, 2017 ), providing a viable alternative to crystallography for larger (>100 kDa) complexes. These structures are determined by single-particle analysis, which entails the three-dimensional (3D) reconstruction of the molecule’s electron scattering density from thousands or millions of individual projection images of randomly oriented particles ( Elmlund and Elmlund, 2015 , Carazo et al, 2015 , Vilas et al, 2018b , Lyumkis, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…uncorrected beam-induced motion), also contribute noise. The overall result is an extremely low signal-to-noise ratio (SNR) ( Liao and Frank, 2010 , Penczek, 2010 , Vilas et al, 2018b ).…”

Section: Introductionmentioning

confidence: 99%

Mitigating local over-fitting during single particle reconstruction with SIDESPLITTER

Ramlaul¹,

Palmer

Nakane

et al. 2020

Journal of Structural Biology

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Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

Cited by 16 publications

References 178 publications

Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

Mitigating Local Over-fitting During Single Particle Reconstruction with SIDESPLITTER

Mitigating local over-fitting during single particle reconstruction with SIDESPLITTER

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