Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference.

ABSTRACT: Characterizing the conformational ensemble of biomolecular systems is key to understand their functions. Cryoelectron microscopy (cryo-EM) captures two-dimensional snapshots of biomolecular ensembles, giving in principle access to thermodynamics. However, these images are very noisy and show projections of the molecule in unknown orientations, making it very difficult to identify the biomolecule's conformation in each individual image. Here, we introduce cryo-EM simulation-based inference (cryoSBI) to infer the conformations of biomolecules and the uncertainties associated with the inference from individual cryo-EM images. CryoSBI builds on simulation-based inference, a merger of physics-based simulations and probabilistic deep learning, allowing us to use Bayesian inference even when like

SUBMITTER: Dingeldein L 

PROVIDER: S-EPMC12168013 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature