DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images

Abstract

How to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryo-EM images. Here, we present DynaMight, a new software tool that estimates a continuous space of conformations in a cryo-EM data set by learning 3D deformations of a Gaussian pseudo-atomic model of a consensus structure for every particle image. Inversion of the learnt deformations is then used to obtain an improved reconstruction of the consensus structure. We illustrate the performance of DynaMight for several experimental cryo-EM data sets. We also show how error estimates on the deformations may be obtained by independently training two variational autoencoders (VAEs) on half sets of the cryo-EM data, and how regularisation of the 3D deformations through the use of atomic models may lead to important artefacts due to model bias. DynaMight is distributed as free, open-source software, as part of RELION-5.