Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40575124.
- Also identified by DOI 10.1016/j.patter.2025.101262 and PMC identifier 12191761.
- Licence recorded as CC BY-NC-ND.
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Abstract
We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs.