CryoLike : a Python package for cryo-electron microscopy image-to-structure likelihood calculations
Year: 2025
Type: software-paper
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, and building upon previously developed Fourier-Bessel image-representation methods, we created CryoLike, computationally efficient software for evaluating image-to-structure (or image-to-volume) likelihoods across large image data sets, packaged in a user-friendly Python workflow.
Institutions: Flatiron Institute, New York University
Language: English
Subfield: Structural Biology
Domain: Life Sciences
Open Access status: green
Funder: Simons Foundation
OpenAlex ID: W4416411817