Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.

Accurately identifying virulence-associated proteins secreted by Gram-negative pathogens is essential for elucidating bacterial pathogenic mechanisms and developing novel antimicrobial interventions. However, traditional experimental approaches for effector identification are time-consuming and labor-intensive. Recent advances in machine learning (ML), ranging from handcrafted features to context-aware embeddings derived from protein language models, have significantly improved secreted effector prediction. Here, we provide a systematic overview of ML-based methods for secreted effector prediction, surveying available database resources, negative dataset construction strategies, feature representation approaches, and model architectures spanning classical machine learning to deep learning. We discuss fundamental challenges, including data scarcity and class imbalance, evaluation bias, and model interpretability. Finally, we outline future directions encompassing multimodal data integration, meta-learning to address data limitations, and uncertainty quantification to enhance predictive robustness.