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Mapping Dysfunctional Protein-Protein Interactions in Disease
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DHG-EPI: A dual-stream hypergraph learning framework with multi-level gating for essential protein identification.

Wei Liu1, Jun Chen1, Yixiao Qian1

  • 1College of Information and Artificial Intelligence, Yangzhou University, Yangzhou, Jiangsu, 225009, China.

Computational Biology and Chemistry
|June 13, 2026
PubMed
Summary

This study introduces DHG-EPI, a novel deep learning framework for identifying essential proteins by capturing high-order network structures and protein heterogeneity. The model enhances understanding of cellular survival and aids drug target discovery.

Keywords:
Deep learningEssential proteinHypergraph neural networksMulti-modal fusionProtein–protein interaction

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Identifying essential proteins is crucial for understanding cell survival and discovering drug targets.
  • Current deep learning methods for essential protein identification often overlook high-order network structures and protein heterogeneity.

Purpose of the Study:

  • To propose DHG-EPI, a dual-stream hypergraph learning framework with multi-level gating to address limitations in existing essential protein identification methods.
  • To improve the accuracy and adaptability of essential protein prediction by incorporating high-order topological information and semantic features.

Main Methods:

  • Developed a dual-stream architecture: a topological stream using hypergraphs from triangle motifs and a semantic stream integrating Gene Ontology, subcellular localization, and protein complex data.
  • Employed hypergraph convolution for feature extraction and a dual gating mechanism (node-level vector gating and branch-level gated attention) for adaptive information aggregation.
  • Validated the framework on DIP, Krogan, and BioGRID datasets.

Main Results:

  • DHG-EPI significantly outperformed existing advanced methods in essential protein identification across multiple benchmark datasets.
  • Functional enrichment analysis of identified essential proteins revealed their high enrichment in critical biological processes like cell cycle and DNA replication.

Conclusions:

  • DHG-EPI offers a robust and adaptive approach for essential protein identification, surpassing current state-of-the-art methods.
  • The framework demonstrates practical value in elucidating key cellular survival mechanisms and holds potential for drug target discovery.