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A Point Cloud-Based Deep Learning Model for Protein Docking Decoys Evaluation

Author

Listed:
  • Ye Han

    (College of Information Technology, Jilin Agricultural University, Changchun 130012, China)

  • Simin Zhang

    (College of Information Technology, Jilin Agricultural University, Changchun 130012, China)

  • Fei He

    (College of Information of Science and Technology, Northeast Normal University, Changchun 130012, China)

Abstract

Protein-protein docking reveals the process and product in protein interactions. Typically, a protein docking works with a docking model sampling, and then an evaluation method is used to rank the near-native models out from a large pool of generated decoys. In practice, the evaluation stage is the bottleneck to perform accurate protein docking. In this paper, PointNet, a deep learning algorithm based on point cloud, is applied to evaluate protein docking models. The proposed architecture is able to directly learn deep representations carrying the geometrical properties and atomic attributes from the 3D structural data of protein decoys. The experimental results show that the informative representations can benefit our proposed method to outperform other algorithms.

Suggested Citation

  • Ye Han & Simin Zhang & Fei He, 2023. "A Point Cloud-Based Deep Learning Model for Protein Docking Decoys Evaluation," Mathematics, MDPI, vol. 11(8), pages 1-13, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1817-:d:1120931
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    References listed on IDEAS

    as
    1. Zixuan Cang & Lin Mu & Guo-Wei Wei, 2018. "Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-44, January.
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