Aims: EffecTri aims to develop a comprehensive, multi-class prediction framework to accurately identify bacterial effector proteins secreted by Type III, IV, and VI secretion systems. Current methodologies often employ binary classifications, overlooking the complexity and interactions among multiple effector classes.

Methods: EffecTri integrates deep contextual embeddings from Evolutionary Scale Modeling and handcrafted descriptors, including Amino Acid Composition and Dipeptide Composition. The performance of the model was rigorously evaluated through comparative descriptor analyses and optimized feature combinations, complemented by Uniform Manifold Approximation and Projection visualization for interpretability.

Results: EffecTri outperformed traditional machine learning methods, achieving a weighted F1-score of 0.850 on an independent test dataset. The fusion of Evolutionary Scale Modeling embeddings with handcrafted descriptors demonstrated superior predictive performance, clearly distinguishing effector classes in UMAP visualizations.

Conclusion: EffecTri represents a robust, interpretable, and accurate computational tool, enhancing the multi-class identification of bacterial secretory effectors and contributing valuable insights into bacterial pathogenic mechanisms.