CoBALT: Coupling Antigen–Antibody Binding-Site Prediction and Affinity Ranking through a Contact-Energy Bottleneck

Antigen–antibody (Ag–Ab) recognition involves binding-interface organization and collective intermolecular energetics, captured from complementary perspectives by binding-site prediction (BSP) and binding-affinity ranking (BAR). Although intrinsically connected, the two tasks operate at different resolutions: BSP localizes residue-level interfaces, whereas BAR evaluates complex-level affinity. Unifying these perspectives requires reconciling their distinct signals under scarce joint annotations. We introduce CoBALT, a unified framework that treats BSP and BAR as complementary readouts of a shared Ag–Ab interaction landscape. From partner-conditioned residue representations, CoBALT constructs a contact–energy bottleneck that factorizes pairwise interactions into a contact field localizing binding interfaces and an energy field encoding affinity-relevant local contributions. To learn from disjoint annotations, we develop an alternating multitask scheme that trains the shared bottleneck on separate BSP pairs and BAR candidate sets, enabling cross-task transfer without dual-annotated pairs. In five-fold cross-validation, CoBALT achieves epitope and paratope AUPRCs of 65.60% and 88.58%, respectively, while improving BAR Spearman correlation from 0.65 to 0.70 and Precision@1 from 50.77% to 56.99% over the strongest baselines. These gains largely persist under antigen-homology-aware holdouts, demonstrating that the contact–energy bottleneck effectively connects binding-site detection with affinity ranking.