IceBreaker: Software for high-resolution single-particle cryo-EM with non-uniform ice

Mateusz Olek,Kevin Cowtan,Donovan Webb,Yuriy Chaban,Peijun Zhang

doi:10.1016/j.str.2022.01.005

Mateusz Olek, Kevin Cowtan + Show 3 more

Open Access

https://doi.org/10.1016/j.str.2022.01.005

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Abstract

SummaryDespite the abundance of available software tools, optimal particle selection is still a vital issue in single-particle cryoelectron microscopy (cryo-EM). Regardless of the method used, most pickers struggle when ice thickness varies on a micrograph. IceBreaker allows users to estimate the relative ice gradient and flatten it by equalizing the local contrast. It allows the differentiation of particles from the background and improves overall particle picking performance. Furthermore, we introduce an additional parameter corresponding to local ice thickness for each particle. Particles with a defined ice thickness can be grouped and filtered based on this parameter during processing. These functionalities are especially valuable for on-the-fly processing to automatically pick as many particles as possible from each micrograph and to select optimal regions for data collection. Finally, estimated ice gradient distributions can be stored separately and used to inspect the quality of prepared samples.

Highlights

Advancements in cryoelectron microscopy instrumentation, detector development, and data processing algorithms have allowed reconstructions to be obtained at atomic resolution (Nakane et al, 2020)
We present a software tool, IceBreaker, for the ice thickness estimation and digital ice gradient removal on the cryoelectron microscopy (cryo-EM) micrographs
The presented software, IceBreaker, aims to overcome the issues caused by the varying ice gradient

Summary

SUMMARY

Despite the abundance of available software tools, optimal particle selection is still a vital issue in single-particle cryoelectron microscopy (cryo-EM). IceBreaker allows users to estimate the relative ice gradient and flatten it by equalizing the local contrast. It allows the differentiation of particles from the background and improves overall particle picking performance. We introduce an additional parameter corresponding to local ice thickness for each particle. Particles with a defined ice thickness can be grouped and filtered based on this parameter during processing. These functionalities are especially valuable for on-the-fly processing to automatically pick as many particles as possible from each micrograph and to select optimal regions for data collection. Estimated ice gradient distributions can be stored separately and used to inspect the quality of prepared samples

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