Jiang, F.; Zhao, R.; Liang, F.; Zhang, Y.; Cui, T.; Zhao, X.; Wang, X.; Xu, m.; Shuai, Y.; Luo, T.; Yao, H.; Xu, C.; Wang, Z.; Zeng, W.; Jiang, X.; Tang, Z.; Zhang, W.; Heng, P. A.; Li, Y.; Wang, X.

Understanding functional diversity across protein isoforms remains a long-standing challenge with broad biological and translational implications, yet most computational methods are developed and benchmarked on a single reference protein per gene, limiting their ability to resolve isoform-specific functional differences. This challenge is compounded by the scarcity of isoform-resolved annotations and benchmarks. Here, we present an isoform-centric, structure-aware framework for the protein family (Pfam) domain and Gene Ontology (GO) term prediction. We implemented this framework in 3DisoDeepPF, which combines a dense graph combining sequence and structure similarity with multimodal representations, and evaluated 3DisoDeepPF in both conventional and isoform-resolved settings. Across conventional canonical benchmarks, 3DisoDeepPF showed strong performance relative to representative methods in both GO and Pfam prediction tasks. In an isoform-specific breast cancer atlas, 3DisoDeepPF remained stable under homology-controlled evaluation and detected Pfam changes among isoforms from the same gene. Additionally, 3DisoDeepPF provides evidence-tracing utilities that trace predicted labels to associated protein nodes, enabling supporting traceability and biological plausibility assessment.