April 10, 2023

Xu Lab Publishes in PNAS

Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. The automatic detection of macromolecular complexes is an open and challenging problem in cellular cryoelectron tomography. Existing computational methods rely on known structural templates or manually labeled training datasets. In this paper, members of Xu Lab introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation of five experimental cryo-ET datasets shows that an unsupervised deep learning-based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection is an important step towards systematic unbiased recognition of macromolecular complexes in situ.

X. Zeng, A. Kahng, L. Xue, J. Mahamid, Y.W. Chang and M. Xu, 2023. High-throughput cryo-ET structural pattern mining by deep iterative unsupervised clustering. Proceedings of the National Academy of Sciences, 120 (15) e2213149120.