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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction.

Yuguang Li1, Zhen Tian1,2, Xiaofei Nan1

  • 1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, Henan, China.

Briefings in Bioinformatics
|March 4, 2025
PubMed
Summary
This summary is machine-generated.

We developed HSSPPI, a novel computational method for predicting protein-protein interaction sites (PPIs). By modeling proteins hierarchically and considering both spatial and sequential information, HSSPPI significantly improves prediction accuracy.

Keywords:
feature fusehierarchical graphprotein–protein interaction sitessequential formspatial form

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Biochemistry

Background:

  • Protein-protein interactions (PPIs) are crucial for biological functions.
  • Accurate prediction of PPI sites is challenging and experimental methods are costly.
  • Existing computational methods often overlook the hierarchical structure of proteins, considering only spatial conformation or primary sequence.

Purpose of the Study:

  • To propose a novel computational method, HSSPPI, for predicting protein-protein interaction sites.
  • To address the limitations of current methods by incorporating hierarchical and spatial-sequential protein modeling.
  • To enhance the accuracy and efficiency of PPI site prediction.

Main Methods:

  • Developed HSSPPI, a network architecture for PPI prediction.
  • Represented proteins as hierarchical graphs (atom-level and residue-level).
  • Designed a spatial-sequential block to capture complex interaction relationships from spatial and sequential data.

Main Results:

  • HSSPPI demonstrated superior performance on benchmark datasets compared to existing models.
  • The hierarchical protein modeling approach proved effective for PPI prediction.
  • The model exhibited strong feature extraction capabilities by integrating spatial and sequential information.

Conclusions:

  • HSSPPI offers an effective approach for predicting protein-protein interaction sites.
  • Hierarchical and spatial-sequential modeling enhances prediction accuracy.
  • The method provides a valuable computational tool for biological research.