Towards Foundation Models for Cryo-ET Subtomogram Analysis
This work addresses the problem of scalable and robust structural determination in cryo-electron tomography for researchers in structural biology, representing a novel step towards foundation models in this domain.
The paper tackled the challenges of scarce annotations, severe noise, and poor generalization in cryo-ET subtomogram analysis by developing a foundation model approach, achieving state-of-the-art performance on classification, alignment, and averaging tasks across 24 datasets with strong generalization to unseen data.
Cryo-electron tomography (cryo-ET) enables in situ visualization of macromolecular structures, where subtomogram analysis tasks such as classification, alignment, and averaging are critical for structural determination. However, effective analysis is hindered by scarce annotations, severe noise, and poor generalization. To address these challenges, we take the first step towards foundation models for cryo-ET subtomograms. First, we introduce CryoEngine, a large-scale synthetic data generator that produces over 904k subtomograms from 452 particle classes for pretraining. Second, we design an Adaptive Phase Tokenization-enhanced Vision Transformer (APT-ViT), which incorporates adaptive phase tokenization as an equivariance-enhancing module that improves robustness to both geometric and semantic variations. Third, we introduce a Noise-Resilient Contrastive Learning (NRCL) strategy to stabilize representation learning under severe noise conditions. Evaluations across 24 synthetic and real datasets demonstrate state-of-the-art (SOTA) performance on all three major subtomogram tasks and strong generalization to unseen datasets, advancing scalable and robust subtomogram analysis in cryo-ET.