ArticleBriefings in bioinformatics2025
BridgeNet: a high-efficiency framework integrating sequence and structure for protein and enzyme function prediction.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Understanding the relationship between protein sequences and structures is essential for accurate protein property prediction. We propose BridgeNet, a pre-trained deep learning framework that integrates sequence and structural information through a novel latent environment matrix, enabling seamless alignment of these two modalities. The model's modular architecture-comprising sequence encoding, structural encoding, and a bridge module-effectively captures complementary features without requiring explicit structural inputs during inference. Extensive evaluations on tasks such as enzyme classification, Gene Ontology annotation, coenzyme specificity prediction, and peptide toxicity prediction demonstrate its superior performance over state-of-the-art models. BridgeNet provides a scalable and robust solution, advancing protein representation learning and enabling applications in computational biology and structural bioinformatics.
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