Skip to main page content
U.S. flag

An official website of the United States government

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Nov;204(2):283-290.
doi: 10.1016/j.jsb.2018.09.002. Epub 2018 Sep 4.

New software tools in EMAN2 inspired by EMDatabank map challenge

James M Bell  1 , Muyuan Chen  2 , Tunay Durmaz  2 , Adam C Fluty  2 , Steven J Ludtke  3
Affiliations

New software tools in EMAN2 inspired by EMDatabank map challenge

James M Bell et al. J Struct Biol. 2018 Nov.

Abstract

EMAN2 is an extensible software suite with complete workflows for performing high-resolution single particle analysis, 2-D and 3-D heterogeneity analysis, and subtomogram averaging, among other tasks. Participation in the recent CryoEM Map Challenge sponsored by the EMDatabank led to a number of significant improvements to the single particle analysis process in EMAN2. A new convolutional neural network particle picker was developed, which dramatically improves particle picking accuracy for difficult data sets. A new particle quality metric capable of accurately identifying "bad" particles with a high degree of accuracy, no human input, and a negligible amount of additional computation, has been introduced, and this now serves as a replacement for earlier human-biased methods. The way 3-D single particle reconstructions are filtered has been altered to be more comparable to the filter applied in several other popular software packages, dramatically improving the appearance of sidechains in high-resolution structures. Finally, an option has been added to perform local resolution-based iterative filtration, resulting in local resolution improvements in many maps.

Keywords: 3-D reconstruction; CryoEM; EMDatabank map challenge; Image processing; Single particle analysis; Structural biology.

PubMed Disclaimer

Figures

Figure 1:
Figure 1:. B-factor Sharpening.
A. Guinier plot of the spherically averaged Fourier amplitude calculated using the canonical EMAN2 “structure factor” approach (blue) and our latest “flattening” correction (green). B. TRPV1 (EMPIAR-5778) reconstruction obtained using “structure factor” amplitude correction. C. TRPV1 reconstruction after iterative, “flattening” amplitude correction with a tophat filter.
Figure 2.
Figure 2.. Particle Picking.
A. Workflow of CNN based particle picking. Top: examples of background, good and bad particle references. Bottom: Input test micrograph and output of the trained CNNs. B. A comparison of template matching to the CNN picker. Left: Template matching based particle picking, using 24 class averages as references. Right: Results of CNN based particle picking, with 20 manually selected raw references of each type (good, background and bad). C. Left: Precision-recall curves of CNN1 on a test set, with the threshold of CNN2 fixed at −5. Right: Precision- recall curves of CNN2 with the threshold of CNN1 fixed at 0.2
Figure 3:
Figure 3:. Bad Particle Classification.
A. Particle/projection FRCs integrated over 4 bands and clustered via K-means algorithm with K=4. The cluster with the lowest sum of integrated FRC values are marked as “bad” while particles in the remaining 3 are labeled “good.” B. Example beta-galactosidase particles (EMPIAR 10012 and 10013) and projections from bad (left) and good (right) clusters. C. Defocus vs. low-resolution integrated FRC. D. Left. Iteration-to-iteration comparison of integrated particle/projection FRC values at low (left) and intermediate (right) spatial frequencies.
Figure 4:
Figure 4:. Resolution enhancements obtained from bad particle removal.
A. Beta galactosidase map refined using all 19,829 particles, B. using only the “good” particle subset (15,211), and C. using only the “bad” particle subset (4,618). D. Gold standard FSC curves for each of the three maps in A-C. E. FSC between the maps in A-C and a 2.2Å PDB model (5A1A) of beta galactosidase. Note that the “good” particle subset has a better resolution both by “gold standard” FSC and by comparison to the ground-truth, indicating that the removed particles had been actively degrading the map quality.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Bell JM, Chen M, Baldwin PR, Ludtke SJ, 2016. High resolution single particle refinement in EMAN2.1. Methods 100, 25–34. 10.1016/j.ymeth.2016.02.018 - DOI - PMC - PubMed
    1. Bharat TAM, Russo CJ, Löwe J, Passmore LA, Scheres SHW, 2015. Advances in Single-Particle Electron Cryomicroscopy Structure Determination applied to Sub-tomogram Averaging. Structure 23, 1743–1753. 10.1016/j.str.2015.06.026 - DOI - PMC - PubMed
    1. Borgnia M, Shi D, Zhang P, Milne J, 2004. Visualization of alpha-helical features in a density map constructed using 9 molecular images of the 1.8 MDa icosahedral core of pyruvate dehydrogenase. J. Struct. Biol 147, 136–145. 10.1016/j.jsb.2004.02.007 - DOI - PubMed
    1. 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. 10.1016/j.jsb.2013.08.002 - DOI - PMC - PubMed
    1. Chen M, Dai W, Sun SY, Jonasch D, He CY, Schmid MF, Chiu W, Ludtke SJ, 2017. Convolutional neural networks for automated annotation of cellular cryo-electron tomograms. Nat. Methods 14, 983–985. 10.1038/nmeth.4405 - DOI - PMC - PubMed

Publication types

LinkOut - more resources