Speaker
Description
We present a method 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 features detection. DeepRes is fully automatic and parameter free and avoids issues of most current methods, such as their insensitivity to enhancements due to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes of local quality after applying enhancement processes, like isotropic filters or substantially more complex procedures, such as local sharpening or denoising, that may be very difficult to follow by current methods. The comparison with traditional local resolution indicators is also addressed. The comparison between current methods and DeepRes also allows detecting over-sharpening in CryoEM maps.
