2025/07/01 by J. Zhu, Zirui Fan, Zhengxin Li +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · Materials Science · #Artificial intelligence #Biomolecule #Cell Image Analysis Techniques #Computer science #Enzyme Structure and Function #Image (mathematics) #Inpainting #Materials science #Nanotechnology #Protein Structure and Dynamics
paper · pdf · doi:10.1101/2025.06.26.661889
openalex publication_date 2025/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14
Abstract Atomistic resolution is essential for understanding biomolecular structure and function, yet coarse-grained (CG) models remain indispensable for simulating large and dynamic systems. Reconstructing accurate all-atom structures from CG representations, particularly across varied CG schemes and biomolecular types, remains a fundamental challenge. Moreover, flexible or disordered regions may well suffer from failure in structure modeling, making inpainting missing regions another challenging task. Here, we present StruCloze, a deep learning framework for reconstructing atomistic structures from CG models and inpainting missing regions for both proteins and nucleic acids. StruCloze generalizes across various CG levels and biomolecule types on single pretraining, with minimal fine-tuning required for optimal performance on specific representations. It achieves state-of-the-art accuracy in reconstructing both protein and nucleic acid structures, demonstrates superior transferability and speed compared to existing methods. Leveraging masked learning strategy, StruCloze also excels at inpainting structurally missing regions in structures, offering a practical tool for structural refinement and integrative modeling. Our framework provides a general solution for bridging reduced or incomplete representations with full atomistic detail of biomolecular structures, enabling rapid local structure prediction and further analysis on system dynamics.