OccuPy 0.1.5

OccuPy: Estimation of local scale in cryo-EM maps

OccuPy

A fast and simple python module and program to estimate local scaling of cryo-EM maps, to approximate relative occupancy and/or resolution, and optionally also equalise the map according to occupancy while suppressing solvent amplification.

Estimation of local scale/occupancy

The primary purpose of OccuPy is to estimate the local map scale of cryo-EM maps. What does the 'local scale' mean? In simple terms, think of it as the range of pixels values. In well-resolved regions, contrast is high, and we expect very bright and very dark pixels. If that region has decreased resolution or occupancy, we expect decreased contrast and a narrower range of pixel values. The limit is solvent, which has Gaussian distribution. OccuPy was built to estimate this 'scale' under the assumption that structural variability (flexibility) is negligible (in which case it is a good approximation for occupancy), and to modify the estimated scale while not modifying solvent noise. In essence, OccuPy locates the region that exhibits the highest range of pixel values, and utilizes this to place all other regions on a nominal scale between 0 and 1.

Disclaimer

OccuPy does not sharpen maps. It tries not to.

OccuPy does not estimate the local resolution, but might correlate with it. See more here.

Why estimate local scale?

The local scale contains information about both resolution and occupancy. With this in mind, OccuPy is designed to estimate the local scale

• extremely fast
• without half-sets
• without GPUs
• without masks

The reason for this is that it is intended to be compatible with the expectation maximization (fast) maximum likelihood classifiers (no half-sets) based on prior alignments (no GPUs), and be compatible with unbiased discovery of macromolecular heterogeneity and/or components (no masks). In this context, it will provide ways to weight data and/or provide a displacement vector to emphasize macromolecular resolution and/or occupancy during gradient descent. Basically, it needs to be fast enough to run repeatedly without delaying processing, and simple enough to use that it needs no input other than a cryo-EM map.

It is here implemented as a command-line tool using open-source python libraries, to facilitate visualization of partial occupancy and the relative resolution of cryo-EM reconstructions.

Modification of partial occupancies

OccuPy can also amplify confidently estimated partial occupancy (local scale) in the input map by adding the --amplify or --attenuate option. To modify, one must also specify --gamma, which in simple terms is the power of the modification, analogous to a traditional gamma correction factor. --gamma 1 means to do nothing, and higher values signify stronger modification. Values higher than about 30 are largely pointless, as values in the range 2-5 are typically useful. A very high (limiting) value of --gamma 30 (or more) leads to

• amplification: full occupancy at all non-solvent points.
• attenuation: no occupancy at all non-solvent points where estimated scale was less than 100%.

NOTE 1: --gamma values less than 1 are not permitted, as it simply inverts the relationship between amplification and attenuation.

NOTE 2: Local scale should only be modified when it approximates occupancy. OccuPy will try to remove resolution-dependent effects when modification is used, but this relies on appropriate low-pass filtering as decribed here.

Solvent suppression

Map scale amplification by inverse filtering would result in an extremely noisy output if solvent was permitted to be amplified. To mitigate this, OccuPy estimates a solvent model which limits the amplification of regions where the map scale is estimated as near-solvent. One can aid this estimation by providing a mask that covers non-solvent, permitting OccuPy to better identify solvent. Usually, this is not necessary. OccuPy will warn you (intently) if it detects that the solvent model may be bad, but it is not guaranteed that it will. You can use the option --plot to get a figure displaying the solvent model. See also the section on [troubleshooting] (#troubleshooting). If a solvent mask i needed, it need not be precise or accurate, since OccuPy will still be able to amplify map scale outside this region if it is confident about it. Thus, a mask supplied to OccuPy using the option --solvent-definition is not a solvent mask in the traditional sense, and the estimation of the solvent model does NOT affect the estimated map scaling in any way (it only affects the optional scale modification and/or solvent suppression).

The suppression of solvent is not contingent on amplification - one can choose to supress solvent regions or not, irrespective of scale modification. This acts as automatic solvent masking, to the extent that OccuPy can reliably detect it.

Expected input

OccuPy expects an input map that has not been solvent-flattened (there should be some solvent somewhere in the map, where more is better). OccuPy may also work poorly where the map has been post-processed or altered by machine-learning, sharpening, or manual alterations. It has been designed to work in a classification setting, and
as such does not require half-maps, an accurate resolution estimate, or solvent mask. It will likely benefit if you are able to supply these things, but does not need it.

Installation

OccuPy can be installed from the Python Package Index (PyPI) using pip

pip install occupy

But one may also pip install from the cloned repo

$ git clone https://github.com/bforsbe/OccuPy.git
$ cd occupy 
$ pip install -e . 

Usage

OccuPy is a command-line tool

$ OccuPy --version
OccuPy: 0.1.5rc4.dev1+gfa0f2e9.d20220905

but the tools and functions are available from within a python environment as well (but this is not intended use outside of development)

In[1]: import occupy

In[2]: occupy.occupancy.estimate_confidence?                                                                                            
Signature:
occupy.occupancy.estimate_confidence(
    data,
    solvent_paramters,
    hedge_confidence=None,
    n_lev=1000,
)
Docstring:
Estimate the confidence of each voxel, given the data and the solvent model

The estimate is based on the relative probability of each voxel value pertaining to non-solvent or solvenr model

:param data:                input array
:param solvent_paramters:   solvent model parameters, gaussian (scale, mean, var)
:param hedge_confidence:    take the estimated confidence to this power to hedge
:param n_lev:               how many levels to use for the histogram
:return:
File:      ~/Documents/Occ/occupy/occupy/occupancy.py
Type:      function

In[3]:

Examples of use

Estimating and modifying local map scale

In its basic form, OccuPy simply estimates the map scale, writes it out along with a chimeraX-command script to visualise the results easily

$ occupy -i map.mrc 
$ ls  
map.mrc    scale_res_map.mrc    chimX_map.cxc    log_map.txt

The output scale here includes effects due to resolution, and is thus named as such. A log file is written with additional details.

To modify all confident partial scale regions (local partial occupancy), use --amplify and/or --attenuate along with --gamma as described above. Because the input is modified and not just estimated, there is now additional output map(s).

$ occupy -i map.mrc  --amplify --gamma 4 
$ ls  
map.mrc    scale_occ_map.mrc    ampl_4.0_map.mrc    chimX_map.cxc    log_map.txt

Because modification was requested, the occupy has tried to remove resolution-dependent factors withing the local scale (which should not be changed, to make it a better approximation of occupancy (which should be modified). Hence, the name of the file reflects this.

To supress (flatten) solvent content use --exclude-solvent

$ occupy -i map.mrc --exclude-solvent 
$ ls  
map.mrc    scale_res_map.mrc    solExcl_map.mrc    chimX_map.cxc    log_map.txt

These can also be combined, of course

$ occupy -i map.mrc --exclude-solvent --attenuate --amplify --gamma 4
$ ls  
map.mrc    scale_occ_map.mrc    ampl_4.0_solExcl_map.mrc   attn_4.0_solExcl_map.mrc    chimX_map.cxc    log_map.txt

Visualising the local scale

The easiest method of visualizing the estimated local scale is to use the chimeraX command script output by OccuPy. This will

1. Color the input (and any output) map by the estimated scale
2. Provide a color key
3. Provide two useful commands within the chimeraX-session:
   1. scale_color <color this map> <by this value> to color one map by the values of another according to the color key. To re-color according to scale, use scale_color <map> #2 since the .cxc always defines the scale estimate as #2. This is useful after introducing clipping planes.
   2. occupy_level <level> to set the input and output maps on the same level for easy comparison of how modification affected the input map.

Troubleshooting

Finding more information

For brief information regarding input options, please use

$ occupy --help 

For extensive information regarding input options, please use

$ occupy --help-all 

The modified map is similar to the input map

1. The modification is effected by the power given to --gamma , where values larger than 1 mean to modify. Larger values mean to modify more, and typically values between 2 and 10 are useful.
2. The modification is suppressed if the estimated solvent model decreases confidence in partial occupancies. If there isn't enough solvent for the fitting of the solvent model, it will typically be too wide and prevent modification of lower-scale components. You can check this by using the --plot option and inspecting the output. You can also use --solvent-def <mask.mrc> where the mask is a conventional solvent-mask. This will allow these regions to be omitted during solvent fitting. This mask does not need to be perfect, and does not limit the modification to areas inside it.

There is a sphere of noise surrounding the amplified map

1. If the confidence is over-estimated, low-scale components will be permitted to be amplified. You can hedge the confidence by using --hedge-confidence <val>, where <val> is a power, meaning that higher values hedge more. 10 is a reasonable value to try.
2. Another possible reason for the confidence being over-estimated is that the solvent model mean and/or variance is under-estimated. A typical reason for this is that the solvent has been flattened, such that the solvent is not gaussian. OccuPy was not designed for this type of reconstruction, since such flattening is typically enforced using a mask which has thus already delineated solvent vs non-solvent.
3. If the map is not solvent-flattened, and confidence-hedging does not alleviate solvent-amplification surrounding the main map component, use --solvent-def <mask.mrc> where the mask is a conventional solvent-mask. This will allow these regions to be omitted during solvent fitting. This mask does not need to be perfect, and does not limit the modification to areas inside it.

The estimated scale looks like my local resolution

OccuPy estimates the scale. The scale decreases due to both lower resolution and lower occupancy. Since it is not possible to trivially separate these factors, the current approach to estimate occupancy as separate from lower resolution is to low-pass filter the input before estimating the local scale. This is turned on by default when amplifying or attenuating the map, to minimize over-amplification of low-scale components that are simply low resolution. If one is just estimating scale but wants to reduce resolution-dependent effects, the low-pass filtration before scale-estimation can be activated by either --lp-scale or --occupancy. It should then be combined with --lowpass/-lp or --resolution/-r to specify the worst resolution among the components for which occupancy is to be estimated. For membrane proteins, see here.

In the absence of variable occupancy, local scale does actually approximate the local resolution. If you would like to include resolution-dependent factors during amplification or attenuation (which is not recommended), you can do so by using --raw_scale, which does the opposite of --lp-scale.

My membrane or detergent looks funny

Membranes are lower in resolution due to their amorphous nature which cannot be coherently averaged. Because this reduces the local scale, membranes are estimated at low scale. The estimated scale of membranes are thus not a measure of relative density or occupancy. The low-pass filtration intended to reduce influence of resolution-dependent scale factors is only an approximate measure, so that the precise meaning of the local scale of membrane and another amorphous regions (as estimated by OccuPy) is not well-defined. The lower scale generally reflects intuition however, and permits weak attenuation to de-emphasize these regions to make visualization easier.