3D Poses Recovery in Single-Particle Cryo-EM from Learned Pairwise Projection Distances

Single-particle cryo-electron microscopy (cryo-EM) is a technology that allows the observation and the high-resolution 3D structure determination of biomolecules. In this project, the goal is to estimate the angles at which we imaged the 2D projections from a given 3D protein. We developed deep learning models to estimate the angles from learned pairwise projection distances. We designed a two-step method: 1) distance estimation using a Siamese neural network to learn the distance between pairs of projections, and 2) angle recovery that includes a minimization scheme in order to estimate the angles at which each projection was taken. The current results obtained are discussed depending on the different combinations of approaches used and experimental conditions.