Automated model-free analysis of cryo-EM volume ensembles with SIREn.

ABSTRACT: Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly realized by the advent of machine learning models that allow 100s-1,000s of 3D density maps to be generated from a single dataset. How to identify distinct structural states within these volume ensembles and quantify their relative occupancies remain open questions. Here, we present an approach to inferring variable regions directly from a volume ensemble based on the statistical co-occupancy of voxels, as well as a 3D-convolutional neural network that predicts binarization thresholds for volumes in an unbiased and automated manner. We show that these tools recapitulate known h

SUBMITTER: Kinman LF 

PROVIDER: S-EPMC11482773 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature