Auto-sharpening cryo-EM or crystallographic maps with auto_sharpen

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Auto-sharpening cryo-EM or crystallographic maps with auto_sharpen



The routine auto_sharpen will automatically identify optimal sharpening/blurring/map adjustment for the input map and will write out an optimized version of the map.


How auto_sharpen works:

Auto-sharpen adjusts the resolution dependence of the map to maximize the clarity of the map. You can choose to use map kurtosis or the adjusted surface area of the map (default) for this purpose.

Kurtosis is a standard statistical measure that reflects the peakiness of the map.

The adjusted surface area is a combination of the surface area of contours in the map at a particular threshold and of the number of distinct regions enclosed by the top 30% (default) of those contours. The threshold is chosen by default to be one where the volume enclosed by the contours is 20% of the non-solvent volume in the map. The weighting between the surface area (to be maximized) and number of regions enclosed (to be minimized) is chosen empirically (default region_weight=20).

Several resolution-dependent functions are tested, and the one that gives the best kurtosis (or adjusted surface area) is chosen. In each case the map is transformed to obtain Fourier coefficients. The amplitudes of these coefficients are then adjusted, keeping the phases constant. The available functions for modifying the amplitudes are:

No sharpening (map is left as is)

Sharpening b-factor applied over entire resolution range (b_sharpen
applied to achieve an effective isotropic overall b-value of b_iso).

Sharpening b-factor applied up to resolution specified with the
resolution=xxx keyword, then not applied beyond this resolution (with
transition specified by the keyword k_sharpen, b_iso_to_d_cut).  If
blurring (sharpening with value less than zero) is applied,
the blurring is applied over the entire resolution range.

Resolution-dependent sharpening factor with three parameters.
First the resolution-dependence of the map is removed by normalizing the
amplitudes.  Then a scale factor S is to the data, where
log10(S) is determined by coefficients b[0],b[1],b[2] and a resolution
d_cut (typically d_cut is the nominal resolution of the map).
The value of log10(S) varies smoothly from 0 at resolution=infinity, to b[0]
at d_cut/2, to b[1] at d_cut, and to b[1]+b[2] at the highest resolution
in the map.  The value of b[1] is limited to being no larger than b[0] and the
value of b[1]+b[2] is limited to be no larger than b[1].

You can also choose to specify the sharpening/blurring parameters for your map and they will simply be applied to the map. For example you can apply a sharpening B-value (b_sharpen) to sharpen the map, or you can specify a target overall B-value (b_iso) to obtain after sharpening.

Box of density in sharpening

Normally phenix.auto_sharpen will determine the optimal sharpening by examining the density in a box cut out of your map, then apply this to the entire map.

Local sharpening

You can choose to apply autosharpening locally if you want. In this case the auto-sharpening parameters are determined in many boxes cut out of the map, and corresponding sharpened maps are calculated. The map that is produced is a weighted map where the density at a particular point comes most from the sharpened map based on a box near that point.

Half-map-based sharpening

You can identify the sharpening parameters using two half-maps if you want. The resolution-dependent correlation of density in the two half-maps is used to identify the optimal resolution-dependent weighting of the map. This approach requires a target resolution which is used to set the overall fall-off with resolution for an ideal map. That fall-off for an ideal map is then multiplied by an estimated resolution-dependent correlation of density in the map with the true map (the estimation comes from the half-map correlations).

Model-based sharpening

You can identify instead the sharpening parameters using your map and a model. This approach requires a guess of the RMSD between the model and the true model. The resolution-dependent correlation of model and map density is used as in the half-map approach above to identify the weighting of Fourier coefficients.

Using crystallographic maps

You can use phenix.auto_sharpen with a crystallographic map (represented as map coefficients).

Shifting the map to the origin

Most crystallographic maps have the origin at the corner of the map ( grid point [0,0,0]), while most cryo-EM maps have the orgin in the middle of the map. An output map with the origin shifted to the corner of the map is optionally written out.

Output files from auto_sharpen

sharpened_map.ccp4: Sharpened map.

shifted_sharpened_map.ccp4: Sharpened map, shifted to place the origin on grid point (0,0,0) and sharpened

sharpened_map_coeffs.mtz: Sharpened map, shifted to place the origin on grid point (0,0,0) and sharpened, represented as map coefficients.


Standard run of auto_sharpen:

Running auto_sharpen is easy. From the command-line you can type:

phenix.auto_sharpen resolution=2.6

where is a CCP4, mrc or other related map format, and you specify the nominal resolution of the map.

Possible Problems

Specific limitations and problems:


Additional information

List of all available keywords