Automated map sharpening by maximization of detail and connectivity.

Oleg V. Sobolev,Thomas C. Terwilliger,Paul D. Adams,Pavel V. Afonine

doi:10.1107/s2059798318004655

Abstract

An algorithm for automatic map sharpening is presented that is based on optimization of the 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 termed the `adjusted surface area', map quality can be evaluated in an automated fashion. This metric was used to choose optimal map-sharpening parameters without reference to a model or other interpretations of the map. Map sharpening by optimization of the 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, a simple metric based on map-model correlation that can reproduce visual choices of optimally sharpened maps was used. The map-model correlation is calculated using a model with B factors (atomic displacement factors; ADPs) set to zero. This model-based metric was used to evaluate map sharpening and to evaluate map-sharpening approaches, and it was found that optimization of the adjusted surface area can be an effective tool for map sharpening.

Highlights

Current methods for single-particle reconstruction of cryoEM maps are capable of yielding maps with resolutions that are often better than 4.5 Aand are sometimes as high as 2 Aor better
The key idea in this work is that a map that is optimally sharpened will have more detail, leading to a high surface area of an iso-contour surface, yet at the same time it will have a high degree of connectivity, leading to a low number of regions enclosed by the same iso-contour surface
The adjusted surface area of a map reflects these factors and does not require a model or any other prior interpretation of a map, and maximizing it leads to improved maps

Summary

Introduction

Current methods for single-particle reconstruction of cryoEM maps are capable of yielding maps with resolutions that are often better than 4.5 Aand are sometimes as high as 2 Aor better (see Kuhlbrandt, 2014; Baldwin et al, 2018). The level of noise in the images used in reconstruction and in the resulting maps is highly resolution-dependent. For this reason, it is standard practice to represent a map as a Fourier series, estimate the signal and noise in the reconstruction as a function of resolution, and use this information to weight the Fourier terms as a function of resolution to maximize the interpretability of the final sharpened (or blurred) map (Rosenthal & Henderson, 2003). As the various errors in reconstruction are difficult to estimate accurately, other approaches for resolution-dependent weighting have been considered.

Methods

Results

Conclusion