FusionProt: Fusing Sequence and Structural Information for Unified Protein Representation Learning

Accurate protein representations that integrate sequence and three-dimensional (3D) structure are critical to many biological and biomedical tasks. Most existing models either ignore structure or combine it with sequence through a single, static fusion step. Here we present FusionProt, a unified model that learns representations via iterative, bidirectional fusion between a protein language model and a structure encoder. A single learnable token serves as a carrier, alternating between sequence attention and spatial message passing across layers. FusionProt is evaluated on Enzyme Commission (EC), Gene Ontology (GO), and mutation stability prediction tasks. It improves Fmax by a median of 1.3 points (up to 2.0) across EC and GO benchmarks, and boosts AUROC by 3.6 points over the strongest baseline on mutation stability. Inference cost remains practical, with only ~2-5% runtime overhead. Beyond state-of-the-art performance, we further demonstrate FusionProt’s practical relevance through representative biological case studies, suggesting that the model captures biologically relevant features.