Bacterial protein function prediction via multimodal deep learning

Bacterial proteins are specialized with extensive functional diversity for survival in diverse and stressful environments. A significant portion of these proteins remains functionally uncharacterized, limiting our understanding of bacterial survival mechanisms. Hence, we developed Deep Expression STructure (DeepEST), a multimodal deep learning framework designed to accurately predict protein function in bacteria by assigning Gene Ontology (GO) terms. DeepEST comprises two modules: a multi-layer perceptron that takes gene expression and location as input features, and a protein structure-based predictor. Within DeepEST, we integrated these modules through a learnable weighted linear combination and introduced a novel masked loss function to fine-tune the structure-based predictor for bacterial species. We showed that DeepEST strongly outperforms existing protein function prediction methods relying solely on amino acid sequence or protein structure. Moreover, DeepEST predicts GO terms for unclassified hypothetical proteins across 25 human bacterial pathogens, facilitating the design of experimental setups for characterization studies.