TY - JOUR
T1 - 3DDF-VAE
T2 - Dual-frequency variational autoencoder with pose-consistency validation for rare cryo-EM conformation discovery
AU - Chen, Yuanbo
AU - Li, Fuwei
AU - Dong, Hao
AU - Wang, Xinsheng
AU - Zhang, Fa
AU - Hu, Bin
AU - Wan, Xiaohua
N1 - Publisher Copyright:
© 2026 Elsevier Inc.
PY - 2026/6
Y1 - 2026/6
N2 - Revealing the 3D conformational variability of biomolecules is crucial for understanding their function, while cryo-EM reconstruction of rare states remains difficult due to data imbalance and structural detail loss in existing generative models. We present a dual-stage pipeline consisting of a generative stage and a validation stage. In the generative stage, we employ a 3D dual-frequency variational autoencoder (3DDF-VAE) that separately models low- and high-frequency components of protein density maps to enhance global coherence and local structural detail. In the validation stage, a pose-consistency projection strategy evaluates the generated maps by comparison with the original 2D particles. Experiments on integrin αVβ8, T50S ribosome, and SARS-CoV-2 spike datasets demonstrate that our method produces high-quality density maps, identifies rare conformations, and reconstructs plausible intermediates, while ablation studies confirm the benefits of frequency separation and parameter optimization. This integrated generative–validation framework improves resolution, enhances rare conformation detection, and offers a data-driven approach to explore conformational heterogeneity in complex biomolecular systems.
AB - Revealing the 3D conformational variability of biomolecules is crucial for understanding their function, while cryo-EM reconstruction of rare states remains difficult due to data imbalance and structural detail loss in existing generative models. We present a dual-stage pipeline consisting of a generative stage and a validation stage. In the generative stage, we employ a 3D dual-frequency variational autoencoder (3DDF-VAE) that separately models low- and high-frequency components of protein density maps to enhance global coherence and local structural detail. In the validation stage, a pose-consistency projection strategy evaluates the generated maps by comparison with the original 2D particles. Experiments on integrin αVβ8, T50S ribosome, and SARS-CoV-2 spike datasets demonstrate that our method produces high-quality density maps, identifies rare conformations, and reconstructs plausible intermediates, while ablation studies confirm the benefits of frequency separation and parameter optimization. This integrated generative–validation framework improves resolution, enhances rare conformation detection, and offers a data-driven approach to explore conformational heterogeneity in complex biomolecular systems.
KW - Conformational heterogeneity
KW - Frequency separation
KW - Rare conformation
KW - Single-particle analysis
KW - Variational autoencoder
UR - https://www.scopus.com/pages/publications/105033601610
U2 - 10.1016/j.jsb.2026.108311
DO - 10.1016/j.jsb.2026.108311
M3 - Article
C2 - 41819391
AN - SCOPUS:105033601610
SN - 1047-8477
VL - 218
JO - Journal of Structural Biology
JF - Journal of Structural Biology
IS - 2
M1 - 108311
ER -