Automated map sharpening by maximization of detail and connectivity

By T. C. Terwilliger, O. Sobolev, Pavel V. Afonine, Paul D. Adams. 2018

250 citations recorded by the source corpus.

Synopsis A procedure for optimizing the sharpening of a map based on maximizing the level of detail and connectivity of the map is developed and applied to 361 pairs of deposited cryo-EM maps and associated models. Abstract We present an algorithm for automatic map sharpening that is based on optimization of detail and connectivity of the sharpened map. The detail in the map is reflected in the surface area of an iso-contour surface that contains a fixed fraction of the volume of the map, where a map with high level of detail has a high surface area. The connectivity of the sharpened map is reflected in the number of connected regions defined by the same iso-contour surfaces, where a map with high connectivity has a small number of connected regions. By combining these two measures in a metric we term “adjusted surface area”, we can evaluate map quality in an automated fashion. We use this metric to choose optimal map sharpening parameters without reference to a model or other interpretations of the map. Map sharpening by optimization of adjusted surface area can be carried out for a map as a whole or it can be carried out locally, yielding a locally-sharpened map. To evaluate the performance of various approaches, we use a simple metric based on map-model correlation that can reproduce visual choices of optimally-sharpened maps. The map-model correlation is calculated using a model with B-factors (atomic displacement factors, ADP) set to zero. We use this model-based metric to evaluate map sharpening, use it to evaluate map sharpening approaches and find that optimization of adjusted surface area can be an effective tool for map sharpening.

Subfields: Ophthalmology

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Citations in the corpus

Real-space refinement in PHENIX for cryo-EM and crystallography (2018, methodology): “In two independent refinements, the first half map was taken either as deposited or modified with phenix.auto_sharpen (Terwilliger et al., 2018, submitted) to automatically optimally sharpen or blur the map.”
New tools for the analysis and validation of cryo-EM maps and atomic models (2018, methodology): “Indeed, applying an automated sharpening procedure (phenix.auto_sharpen; Terwilliger et al., 2018) results in a map with significantly enhanced details (Fig.”
Current approaches for the fitting and refinement of atomic models into cryo-EM maps using CCP-EM (2018, methodology): “…in features in the map being revealed (Brunger et al., 2009; Nicholls et al., 2012), noting that other approaches towards map modification have been employed with a similar objective both in the context of MX (Afonine et al., 2015) and cryo-EM (Jakobi et al., 2017; Terwilliger et al., 2018).”
Structural basis for loading and inhibition of a bacterial T6SS phospholipase effector by the VgrG spike (2020, methodology): “For Tle1, a partial initial model based on the primary sequence was built using phenix.map_to_model (Terwilliger et al , 2018a).”