When Frozen Features Help or Harm: Correspondence and Auxiliary Supervision in Antibody–Antigen Prediction

Frozen protein representations and relational auxiliary losses provide different routes for augmenting an interaction predictor. We separate the benefit of an added representation from sensitivity to its residue correspondence, and distinguish contact supervision from partner-ranking supervision. On a SAbDab-derived development set, aligned features outperform features permuted throughout downstream training and evaluation by 0.00820 macro pair AUPRC for ESM-IF1 and 0.00319 for ProteinMPNN, with positive differences in all ten paired seeds. A prespecified evaluation of the frozen checkpoints on a 217-complex test partition yields a mixed confirmation: ProteinMPNN retains a 0.00359 effect (95% seed-bootstrap interval [0.00156, 0.00588]; nine positive seeds), whereas ESM-IF1 falls to 0.00226 (interval [-0.000007, 0.004901]; six positive seeds). A prespecified 185-complex sensitivity subset excludes prior exposure through an unrelated baseline evaluation without replacing the primary partition. Thus, strong development-set correspondence sensitivity need not transfer with the same magnitude. Separately, a four-arm AsEP development study shows that removing partner ranking improves pair AUPRC by 0.0214 and epitope MCC by 0.0369, both in all ten seeds. The node advantage appears at matched epochs before learning rates diverge; gradient diagnostics do not identify a unique mechanism. These results distinguish correspondence sensitivity, baseline-relative benefit, and auxiliary-objective utility, while making the different evidential status of development findings and held-out confirmation explicit.