Abstract
In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a 'local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed.
Highlights
Single-particle cryo-electron microscopy has become a powerful technique for the three-dimensional (3D) structure determination of biological molecules
The method was subsequently applied to different experimental maps and the results were compared using current methods of estimating local resolution (ResMap, MonoRes and BlocRes)
We have introduced a new approach aimed at estimating the local quality of a map using principles totally different from any other method previously used in the field, in which we have used deep learning
Summary
Single-particle cryo-electron microscopy (cryoEM) has become a powerful technique for the three-dimensional (3D) structure determination of biological molecules. From the point of view of the microscope, we may define the resolution based on optics considerations (for instance, the Rayleigh criterion determines the minimum spatial separation between two points so that the two points can still be separated). From the point of view of signal processing, we may define the resolution in terms of some form of signal and noise comparison [for instance, the frequency at which there is more signal than noise; this is used in both the ResMap (Kucukelbir et al, 2014) and MonoRes (Vilas et al, 2018) approaches]. From the algorithmic reproducibility point of view, we may define the resolution as the maximum frequency at which the correlation between two bandpass-filtered versions of two reconstructions performed with the same algorithm but applied to two independent data sets is above a given threshold
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