HALO: Hybrid Attention Model for Subcellular Localization
Year: 2025
Type: conference-paper
Abstract: Subcellular localization prediction is critical for understanding protein functions and interactions, providing insights into cellular mechanisms and potential therapeutic targets. We propose HALO (Hybrid Attention model for subcellular LOcalization), a framework that integrates semantic embeddings from fine-tuned protein language models (e.g., ESM) with structural information derived from AlphaFold. HALO uses a graph attention network (GAT) to incorporate biochemical, structural, and sequence-derived features into a unified representation, while dynamically balancing their contributions. Crucially, the design allows HALO to operate in two modes: (i) a sequence-only mode, where predictions are made from the fine-tuned protein language model (PLM) when structural data are unavailable, and (ii) a hybrid mode, where structural adjacency and biochemical features complement PLM predictions, especially in low-confidence regions. We evaluate HALO on multiple datasets with minimal homology between training and test sets, where it achieves competitive performance across key metrics. By flexibly combining sequence-based and structure-informed predictions, HALO addresses the limitations of relying on a single modality and offers an adaptable framework for accurate and generalizable subcellular localization.
Institution: Virginia Tech
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4417312679
FWCI:
Cites:
Cited by:
Related to: