ProtJEPA: A Multimodal Joint-Embedding Predictive Architecture for Protein Biological World Modeling with Multi-TeacherModality-Attentive Fusion

Abstract

Over 99.9% of known protein sequences lack experimentally validated functional annotations. We present ProtJEPA, a multimodal Joint-Embedding Predictive Architecture that trains a sequence-only student encoder to predict joint embeddings spanning ten biological modalities--sequence, structure, knowledge graph, protein interactions, literature, localization, tissue expression, GO function, anatomy, and disorder--requiring only sequence at inference. The key innovation is target whitening, which eliminates severe anisotropy in joint targets (mean cosine 0.984 to 0.086) and prevents representation collapse without covariance regularization. On 1,828 held-out dark proteins with zero primary Pfam family overlap with training, ProtJEPA achieves 58.07% Hit@10 on zero-shot GO retrieval (+2.80 pp, p = 0.020), 69.99% enzyme class accuracy (+9.64 pp, p < 0.001), and +11.87 pp subcellular localization at 1% labels (p < 0.001). Under realistic dark-protein deployment conditions where relational modalities are unavailable, ProtJEPA significantly outperforms naive concatenation of remaining modalities. Cross-domain evaluations on drug-target interaction and disorder prediction confirm transfer beyond training modalities, with the T1-only < ESMC < ProtJEPA ordering replicated across six independent tasks. Ablations establish that Phase 1 aggregator pretraining and target whitening are each independently load-bearing.