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 develop Deep Expression STructure (DeepEST), a multimodal deep learning framework that predicts protein function in bacteria by assigning Gene Ontology (GO) terms. DeepEST comprises two modules: a multi-layer perceptron that uses gene expression and gene location, and a protein structure-based module that uses predicted structures and amino acid sequences. Within DeepEST, we integrate these modules through a learnable weighted linear combination and introduce a novel masked loss function to fine-tune the structure-based predictor for bacterial species. These choices are well suited for bacteria, in which functionally related genes frequently colocalize and are cotranscribed within operons, allowing transcription dynamics to serve as informative, condition-dependent signals. We show that DeepEST outperforms existing protein function prediction methods on a 25-species benchmark, relying solely on amino acid sequence or protein structure. Averaged over the 25 species, DeepEST reaches a protein-centric F_max of 0.689 and a term-centric micro-AUPRC of 0.756, a relative improvement of 4.4% and 11.6% over the second-best method, the structure-based predictor DeepFRI. Moreover, DeepEST predicts GO terms for unclassified hypothetical proteins across 25 human bacterial pathogens, supporting their experimental characterization. DeepEST bridges organism-specific data integration and structure-based transfer learning, providing a method tailored for bacterial protein function prediction in settings with structural and multi-condition expression data.