Deconvolution improves cryo-EM maps

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 a thin layer of vitreous ice to resolve high-resolution structure in all directions. In practice, preferentially distributed particle orientations and/or other imperfections in imaging and data processing deteriorate quality of obtained cryo-EM map. Here we present a deconvolution approach, named AR-Decon, that computationally improves the quality of cryo-EM maps. We tested and validated the procedure, compared its performance with that of machine learning based density modification method, and benchmarked its performance with a wide range of deposited maps. Our results show that AR-Decon is robust and is a generally applicable post-processing procedure for single particle cryo-EM.