CryoLike: A python package for cryo-electron microscopy image-to-structure likelihood calculations

Abstract

Extracting conformational heterogeneity from cryo-electron microscopy (cryo-EM) images is particularly challenging for flexible biomolecules, where traditional 3D classification approaches often fail. Over the past few decades, advancements in experimental and computational techniques have been made to tackle this challenge, especially Bayesian-based approaches that provide physically interpretable insights into cryo-EM heterogeneity. To reduce the computational cost for Bayesian approaches, we introduce CryoLike, a computationally efficient algorithm for evaluating image-to-structure (or image-to-volume) likelihoods across large image datasets, which is built on Fourier-Bessel representations of the images and packaged in a user-friendly Python workflow.