Does Register-Length Flexibility Help Peptide–MHC-II Classification? A Controlled Ablation on the Chicken Allele BLB2

Discrete latent-alignment models are usually given a fixed-length candidate structure to select over. We ask what happens when that support is widened to match documented heterogeneity in the target structure itself, using peptide–MHC class II (MHC-II) ligand classification as a test bed. Most MHC-II predictors represent the binding core as a canonical nine-residue window and treat only its position as a latent register variable. The chicken allele Gaga-BLB2*002:01 is a useful test case because a structural study establishes that it can anchor a decamer core (Halabi et al., 2021), while a separate computational motif-deconvolution study reports its ligand pool splits nearly evenly between 9-residue and 10-residue annotated cores (Racle et al., 2023), a split our own compiled dataset reproduces (2,134 9-mers, 2,050 10-mers among 4,184 positives). We train a register variable, supervised by an auxiliary cross-entropy loss against the annotated core alongside the primary classification loss, to range jointly over both candidate lengths, and compare it under a parameter-matched ablation that masks the 10-mer candidates out. Holding architecture, data, and training procedure fixed across five outer-fold held-out evaluations (single seed, one allele), the two variants are statistically indistinguishable on ranking metrics (pooled ROC-AUC 0.920 for both), and the restricted model is, if anything, slightly higher on PR-AUC (0.597 versus 0.583; a paired bootstrap places the gap at 0.013, 95% CI [0.006, 0.021]). We cannot attribute that gap to candidate-length support alone, since masking also removes auxiliary supervision from the 10-mer-annotated half of positives. Only the flexible model can agree with a length-flexible annotation at all: it matches the annotated core length on 74.3% of held-out positives (a 51.0% majority-class floor) and reproduces the exact annotated core substring on 30.7%. We also find that a large share of what our dataset labels "BLB2-positive, 9-mer core" may in fact be a co-expressed allele's ligands misassigned by the motif-deconvolution pipeline, a hypothesis Halabi et al. themselves supply and that predicts the largest effect we observe: 9-mer-annotated positives score far lower (PR-AUC ≈0.34–0.35) than 10-mer-annotated positives (PR-AUC ≈0.58–0.59) at matched prevalence. Register flexibility, in this architecture and on this single allele, is therefore better read as a lever on whether a model's internal explanation can engage with heterogeneous annotations at all, not as a classification-accuracy improvement. Part of the negative result may itself be an artifact of imperfect training labels rather than of the inductive bias being tested.