SPIN-BO: Source-Guided Protein–Protein Interaction Discovery for New Targets

Protein engineering often requires optimizing a common mutational library against multiple interaction partners, but experimentally mapping a complete interaction landscape for every new partner is costly. Existing landscapes from related partners offer valuable prior knowledge, yet transferring this knowledge is challenging because shared mutational trends coexist with partner-specific effects. We introduce SPIN-BO, a source-guided Bayesian optimization framework for discovering high-response variants against a new interaction partner under a limited target-assay budget. SPIN-BO reuses previous interaction landscapes through two complementary transfer mechanisms. First, an adaptively calibrated source-response mean uses sparse target assays to learn how the source profiles should be weighted and combined for the new partner. Second, a source-supervised protein-pair representation, learned from pretrained protein language model embeddings, defines the geometry of a target-specific Gaussian-process residual, enabling sparse target measurements to generalize across the mutant library while capturing target-specific structure not explained by transferred source patterns. As target assays accumulate, SPIN-BO continually recalibrates how source landscapes contribute to prediction, updates predictive uncertainty, and selects the next variants to test. Controlled semi-synthetic experiments show that response-level and representation-level transfer provide complementary benefits across different source–target relationships. Across 54 retrospective discovery tasks from the human bZIP interaction network, SPIN-BO achieves a mean optimum-success rate of 95.0% at a budget of 200 target assays, compared with 73.3% for target-only Bayesian optimization. These results show that previously measured interaction landscapes can be systematically reused to accelerate discovery for new interaction partners.