The EmDataBank map validation reports are prepared according to the recommendations of the EM Validation Task Force (EM VTF; Henderson et al., 2012)

1. Experimental information

This section reports experimental details about the map structure determination.

(image of EM experimental details table)

2. Map visualisation

The map visualisation section contains visualisations of the map. These are intended to permit inspection of the internal detail of the map and identification of artifacts. The items in this section are generated using the EMDB Validation-Analysis (To be published) which makes use of TEMPy (Farabella et al., 2015) for map analysis.

These artifacts can include but are not limited to

Streaking

May indicate insufficient representation in particular orientations
Mask artifacts

Can indicate whether masks were used and the types of masks applied during processing

2.1. Orthogonal projections

The images show the map projected in three orthogonal directions, in greyscale.

(image of orthongonal projections)

2.2. Central slices

The images show the central slice of the map in three orthogonal projections, in greyscale.

(image of central slices)

2.3. Largest variance slices

The images show the largest variance slices of the map in three orthogonal directions, in greyscale. The index of the slice in the relevant axis is given below the image.

(image of largest variance slices)

2.4. Orthogonal surface views

The images show the 3D surface of the map at the recommended contour level. In conjunction with slice images, these can indicate whether an appropriate contour level has been used. These images are generated using ChimeraX (Goddard et al., 2018). (image of orthogonal surface views)

2.5. Mask visualisation

This section shows the 3D surface view of the primary map at 50% transparency in yellow overlaid with the specified mask at 0% transparency in blue. A mask typically either encompasses the whole structure and indicates the removal of noise from the peripheries of the map, or seperates out a domain, a functional unit, a monomer or an area of interest from the larger structure. These images are generated using ChimeraX (Goddard et al., 2018).

(image of mask views)

3. Map analysis

The map analysis section contains statistical analysis of the EM volume. The information is given as a set of graphs.

3.1. Map value distribution

The map value distribution is plotted in 128 intervals along the x-axis. The y axis is logarithmic. A spike at around 0 usually indicates that the volume has been masked.

(image of map value distribution graph)

3.2. Volume estimate by contour

The volume estimate graph shows how the enclosed volume varies with contour level. The specified contour level is shown as a vertical line and the intersection between the line and the curve gives the volume of the enclosed surface at the given threshold.

If the molecular weight of the sample is provided by the author, the volume corresponding to the molecular weight is also indicated as a horizontal line. Ideally the horizontal and vertical lines will intersect at a single point on the volume estimate curve.

Volume curve calculation: a density value of 1.5 g/cm³ has been used to provide a rough estimate of the molecular volume, based on the molecular weight. The density of a biological sample can vary to large degree from as low as 1.2 g/cm³ for some proteins to close to 2 g/cm³ for nucleic acids with CsCl salts. The unit for molecular weight is kDa, and for the volume, nm³.

The volume estimate graph should be treated as experimental. Some reasons why the sample and map based weights may not agree are:

  • Molecular weight is given for a fraction of the sample, i.e. one repeating unit, when the sample includes many.

  • Weight is given for a larger unit than what is in the EM volume, e.g. a whole fiber

  • The sample has a heavier or lighter density than average.

  • No correction is attempted for stained samples.

  • A contour level that does not correspond to the estimated volume was provided by the author.

(image of volume estimate graph)

3.3. Rotationally averaged power spectrum

The rotationally averaged power spectrum (RAPS) may provide insight into the data processing steps leading to the map, in terms of:

  • CTF correction

  • Temperature factor correction

  • Low and/or high-pass filtering

  • Masking artifacts

The RAPS plot is only generated for cubic volumes.

(image of rotationally averaged power spectrum graph)

4. FSC validation

Fourier-Shell Correlation (FSC) is the most commonly used method to estimate the resolution for single particle and subtomogram averaging methods. The shape of the curve depends on the imposed symmetry, mask and whether or not the two 3D reconstructions used were processed from a common reference. The author-reported resolution is drawn as a vertical black line. Curves are displayed for 3 sigma 1-bit and 1/2-bit in addition to lines showing the 0.143 gold standard cut off, 0.333 cut off and legacy 0.5 cutoff.

4.1. Resolution estimate

The table contains global resolution estimates for the map.

(image of resolution estimates table)

4.2. Calculated FSC

This FSC information is calculated from half maps provided by the depositor. As we request un-masked, un-processed half maps, the curves may be significantly different from Author provided FSC.

(image of calculated fsc graph)

4.3. Author provided FSC

This FSC information was provided by the depositor

(image of author provided fsc graph)

References

  • R. Henderson, A. Sali, M. L. Baker, B. Carragher, B. Devkota, K. H. Downing, E. H. Egelman, Z. Feng, J. Frank, N. Grigorieff, W. Jiang, S. J. Ludtke, O. Medalia, P. A. Penczek, P. B. Rosenthal, M. G. Rossmann, M. F. Schmid, G. F. Schröder, A. C. Steven, D. L. Stokes, J. D. Westbrook, W. Wriggers, H. Yang, J. Young, H. M. Berman, W. Chiu, G. J. Kleywegt, C. L. Lawson, Outcome of the First Electron Microscopy Validation Task Force Meeting, Structure, 20:205–214, 2012. CrossRef

  • Goddard TD, Huang CC, Meng EC, Pettersen EF, Couch GS, Morris JH, Ferrin TE. UCSF ChimeraX: Meeting modern challenges in visualization and analysis., Protein Sci., 27(1):14–25, 2018 CrossRef

  • Farabella, I., Vasishtan, D., Joseph, A.P., Pandurangan, A.P., Sahota, H. & Topf, M. TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits. Appl. Cryst. 48:1314–1323, 2015 CrossRef