# Deconvolution to restore cryo-EM maps with anisotropic resolution

## TL;DR

This paper introduces a new computational method to improve 3D cryo-EM maps with uneven resolution.

## Contribution

The novel contribution is the AR-Decon deconvolution approach for enhancing anisotropic cryo-EM maps.

## Key findings

- AR-Decon improves the quality of cryo-EM maps with anisotropic resolution.
- The method was validated using synthetic and experimental datasets.
- AR-Decon performs well compared to machine-learning-based alternatives.

## Abstract

With technological advancements in recent years, single particle cryogenic electron microscopy (cryo-EM) has become a major methodology for structural biology. Structure determination by single particle cryo-EM is premised on randomly orientated particles embedded in thin layer of vitreous ice to resolve high-resolution structural information in all directions. Otherwise, preferentially distributed particle orientations will lead to anisotropic resolution of the structure. Here we established a deconvolution approach, named AR-Decon, to computationally improve the quality of three-dimensional maps with anisotropic resolutions reconstructed from datasets with preferred orientations. We have tested and validated the procedure with both synthetic and experimental datasets and compared its performance with alternative machine-learning based methods.