RobPicker: A Meta Learning Framework for Robust Identification of Macromolecules in Cryo-ET

Ramtin Hosseini, Youwei Liang, Digvijay Singh, Hamidreza Rahmani, Sang Keun Choe, Joan Lee, Min Xu, Eran Segal, James Zou, James Williamson, Danielle Grotjahn, Elizabeth Villa, Pengtao Xie

Submission Track: Short papers presenting ongoing research or work submitted to other venues (up to 5 pages, excluding references)

Keywords: cryo-ET, particle picking, meta learning, robust deep learning, bi-level optimization, macromolecule identification

TL;DR: Training a robust segmentation model to identify small and rare macromolecules in cryo-ET 3D images

Abstract: Accurate particle picking of macromolecules of all kinds, shapes, and sizes in cryogenic electron tomograms (cryo-ET) is critical for understanding the molecular architecture of biological systems in their native state. Current deep learning methods have shown potential in identifying macromolecules from tomograms, but they are vulnerable to issues of noise in the training dataset obtained through human or automatic labeling and imbalanced distribution of macromolecule species. To address these limitations, we developed RobPicker, a meta-learning framework that effectively mitigates these issues by automatically learning deep neural networks to correct label errors and give greater emphasis to underrepresented macromolecule species. In evaluations across diverse cryo-ET datasets with noisy labels and imbalanced species distributions, RobPicker substantially outperforms state-of-the-art methods, particularly in identifying small and rare macromolecules. The efficiency and robustness of RobPicker can also be used for rapid fine-tuning of the tilt-series alignment, leading to improved tomogram reconstruction and enabling high-resolution cellular structural biology analysis.

Submission Number: 25