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Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information

Author

Listed:
  • Xintao Song

    (Shandong University
    BioMap Research
    Electrical and Mathematical Sciences and Engineering (CEMSE) Division)

  • Lei Bao

    (Hubei University of Medicine)

  • Chenjie Feng

    (Ningxia Medical University)

  • Qiang Huang

    (Shandong University)

  • Fa Zhang

    (Beijing Institute of Technology)

  • Xin Gao

    (Electrical and Mathematical Sciences and Engineering (CEMSE) Division)

  • Renmin Han

    (Shandong University
    BioMap Research)

Abstract

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

Suggested Citation

  • Xintao Song & Lei Bao & Chenjie Feng & Qiang Huang & Fa Zhang & Xin Gao & Renmin Han, 2024. "Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49858-x
    DOI: 10.1038/s41467-024-49858-x
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    References listed on IDEAS

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