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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level

Shiwei Wu1, Nan Xu2,3, Xiaohui Xin1

  • 1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.

Bioinformatics (Oxford, England)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

We developed IGMI, a novel graph-based model that accurately predicts how mutations affect protein-protein interactions (PPIs) by integrating diverse data. This interpretable tool enhances protein engineering and therapeutic design.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Structural Bioinformatics

Background:

  • Predicting mutation-induced changes in protein-protein interaction (PPI) binding affinity (ΔΔG) is crucial but challenging.
  • Existing methods often use simple feature concatenation, failing to capture complex interdependencies across sequence and structure data.
  • There's a need for models that explicitly integrate multi-level and multi-dimensional biological information.

Purpose of the Study:

  • To introduce IGMI, an interpretable graph-based model for accurate prediction of mutation effects on PPI binding affinity.
  • To explicitly model multi-level feature interactions across sequence, contact maps, and 3D structures.
  • To improve the estimation of both local and long-range mutation effects.

Main Methods:

  • Developed IGMI, a graph-based deep learning model.
  • Integrated 1D sequences, 2D contact maps, and 3D structures at residue and atom levels.
  • Explicitly modeled cross-dimensional and cross-scale feature dependencies.

Main Results:

  • IGMI consistently outperformed state-of-the-art methods in accuracy, robustness, and interpretability across benchmark datasets.
  • The model successfully distinguished direct interface perturbations from indirect structural reorganizations.
  • Analyses confirmed IGMI learns generalizable affinity-related patterns, not just split-specific information.

Conclusions:

  • IGMI offers a reliable and interpretable framework for predicting mutation-induced affinity changes.
  • The model's ability to capture complex interactions supports applications in protein engineering.
  • IGMI advances the design of targeted therapeutics by understanding mutation impacts.