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March 30, 2026BioinformaticsOpen Access

Enhancing Mutation Impact Prediction in Protein Protein Interactions through Interpretable Graph-Based Multi-Level Feature Interactions

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Authors

SWShiwei WuNXN. S. XuXXXiaohui Xin

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Overview

Graph-based model enhances binding affinity prediction in protein interactions, suggesting reliable protein engineering applications.

Key Points

  • The aim is to improve mutation-induced changes prediction in protein-protein interactions by employing a novel model.
  • Introduced IGMI, a graph-based model encoding multi-level feature interactions.
  • Utilized 1D sequences, 2D contact maps, and 3D structures to enhance accuracy.
  • Analyzed both local and long-range mutation effects across benchmark datasets.
  • IGMI achieved superior accuracy and robustness compared to existing methods.
  • The model revealed biologically plausible interaction patterns, distinguishing direct and indirect effects.
  • Generalizable affinity-related patterns were learned, supporting protein engineering applications.

Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/69c9c5c5f8fdd13afe0bddc4https://doi.org/10.1093/bioinformatics/btag150
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1MutPPI+: a multimodal framework for predicting mutation effects on protein–protein interactions via mutation-path-based data augmentation2026
  2. 2Multi-level Interaction Modeling for Protein Mutational Effect Prediction2024 · 3 citations
  3. 3DDMut-PPI: predicting effects of mutations on protein–protein interactions using graph-based deep learning2024 · 62 citations
  4. 4Interpretable multilevel interaction modeling for robust protein–protein affinity2026
  5. 5Macon: Enhance Protein Mutation Representation using Contrastive Learning with Effect Prediction on Protein–protein Interactions2025