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Updated: May 29, 2026

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling
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Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

StruCloze: A Unified Framework for Backmapping and Inpainting Biomolecule Structures.

Junjie Zhu1, Zirui Fan1, Zhengxin Li1

  • 1State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

Journal of Chemical Theory and Computation
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

StruCloze reconstructs detailed atomistic biomolecular structures from coarse-grained (CG) models and fills missing regions. This deep learning tool enhances protein and nucleic acid structure prediction and modeling.

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: May 29, 2026

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling
10:27

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Atomistic resolution is crucial for understanding biomolecular function.
  • Coarse-grained (CG) models are vital for simulating large biomolecular systems.
  • Reconstructing atomistic detail from CG models and handling missing regions are significant challenges.

Purpose of the Study:

  • To develop a deep learning framework, StruCloze, for reconstructing atomistic structures from CG models.
  • To enable inpainting of missing or disordered regions in biomolecular structures.
  • To provide a general solution bridging reduced representations with full atomistic detail.

Main Methods:

  • Developed StruCloze, a deep learning framework utilizing a masked learning strategy.
  • Pretrained on diverse CG levels and biomolecule types (proteins, nucleic acids).
  • Fine-tuning for specific representations to optimize performance.

Main Results:

  • Achieved state-of-the-art accuracy in reconstructing protein and nucleic acid structures from CG models.
  • Demonstrated superior transferability and speed compared to existing methods.
  • Excelled at inpainting missing structural regions.

Conclusions:

  • StruCloze offers a general and accurate solution for atomistic structure reconstruction and inpainting.
  • The framework facilitates structural refinement and integrative modeling.
  • Enables rapid local structure prediction and analysis of biomolecular dynamics.