Harnessing Terminal Signal-Aware Deep Learning for Accurate Multi-Class Secreted Effector Identification

Gram-negative bacterial secreted effectors are translocated through specialized secretion systems to manipulate host cellular processes, and their accurate identification is crucial for understanding bacterial pathogenesis. Recent deep learning methods have significantly advanced this field, yet current approaches primarily rely on global sequence representations, overlooking the biological significance of terminal regions where secretion signals reside. Moreover, severe class imbalance among different secreted effector types remains a critical challenge for multi-class prediction. Here, we propose TermSE, a terminal signal-aware framework for multi-class secreted effector identification. TermSE explicitly captures N-terminal and C-terminal sequence features through convolutional neural networks applied to protein language model embeddings, and integrates them with global sequence representations for multi-view sequence characterization. To address class imbalance, TermSE employs a cosine-normalized classifier combined with weighted sampling to mitigate feature magnitude bias and ensure sufficient learning from minority classes. Extensive experiments demonstrate that TermSE outperforms existing methods in both cross-validation and independent test settings, with robust generalization across varying sequence identity levels. Furthermore, interpretability analysis confirms that TermSE learns to focus on biologically meaningful terminal patterns specific to each secreted effector type. These results highlight the potential of TermSE as an effective and interpretable tool for secreted effector discovery.