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HALO: Hybrid Attention Model for Subcellular Localization.

Shafayat Ahmed1, Nazifa Ahmed Moumi2, Liqing Zhang3

  • 1Virginia Tech, Blacksburg, VA, USA, shafayatpiyal@vt.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
Summary

We developed HALO, a hybrid model for subcellular localization prediction. It combines protein language models and structural data for accurate protein function and therapeutic target identification.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Subcellular localization is crucial for understanding protein functions, interactions, and cellular mechanisms.
  • Accurate prediction aids in identifying potential therapeutic targets.

Purpose of the Study:

  • To develop a flexible and accurate framework for subcellular localization prediction.
  • To integrate sequence-based and structure-based features for improved performance.

Main Methods:

  • Proposed HALO (Hybrid Attention model for subcellular LOcalization), integrating protein language model (PLM) embeddings (e.g., ESM) with AlphaFold structural data.
  • Utilized a graph attention network (GAT) to combine sequence, structural, and biochemical features.
  • Implemented two modes: sequence-only and hybrid (sequence + structure).

Main Results:

  • HALO achieved competitive performance across key metrics on datasets with minimal homology.
  • The hybrid mode effectively complements PLM predictions, especially in low-confidence regions.
  • Demonstrated adaptability by flexibly combining different data modalities.

Conclusions:

  • HALO offers an adaptable framework for accurate and generalizable subcellular localization prediction.
  • By integrating sequence and structural information, HALO overcomes limitations of single-modality approaches.
  • This approach enhances understanding of protein functions and cellular mechanisms.