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SGLEPocket: A Spatial Gating and Local Feature Enhancement Network for Protein-Ligand Binding Pocket Prediction.

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This study introduces a novel U-shaped network for predicting protein ligand-binding pockets, improving drug discovery. The new model enhances feature capture and interpretability compared to existing methods.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Drug discovery and design

Background:

  • Accurate prediction of protein ligand-binding pockets is essential for understanding biological processes and advancing drug discovery.
  • Current methods using 3D voxels and convolutions struggle with long-range information and global modeling.
  • Limitations in capturing detailed pocket features hinder the precision of existing prediction techniques.

Purpose of the Study:

  • To develop a novel U-shaped network architecture for enhanced protein-ligand binding pocket prediction.
  • To address limitations in capturing long-range semantic information and global protein features.
  • To improve the precise characterization of pocket detail features through adaptive filtering and local enhancement.

Main Methods:

  • Proposed a novel U-shaped network integrating the Mamba module and a Local Feature Enhancement (LFE) module in the encoder.
  • Incorporated a Spatial Enhanced Mamba Gate (SEMG) module at skip connections for multiscale feature fusion and redundant information filtering.
  • Utilized extensive protein-ligand datasets for experimental validation.

Main Results:

  • The proposed U-shaped network demonstrated superior performance in protein-ligand binding pocket prediction.
  • The integration of Mamba and LFE modules enabled efficient global modeling and adaptive local feature enhancement.
  • The SEMG module effectively filtered redundant information and improved multiscale feature fusion.
  • Experimental results showed improved performance and interpretability over existing methods.

Conclusions:

  • The novel U-shaped network architecture effectively predicts protein-ligand binding pockets.
  • The proposed method overcomes limitations of existing approaches in capturing global and local protein features.
  • This advancement holds significant potential for accelerating drug discovery and design processes.