Quantifying the Provenance-to-Function Gap in Antidiabetic Peptide Prediction: Homology-Aware Evaluation and the ADP-Hybrid Baseline

Abstract

Antidiabetic peptides (ADPs) are short bioactive sequences of therapeutic interest, and sequence-based classifiers prioritise experimental candidates. A classifier is useful only if its accuracy transfers to unseen sequences, which depends on how the benchmark was assembled and partitioned. We term the distance between what such a classifier is scored on and the function it is meant to predict the provenance-to-function gap, and we quantify it. We re-evaluate the two-layer ADP benchmark of Basith et al. under a protocol that groups homologous peptides rather than splitting them at random. Of the 877 ADPs, 218 attribute to a precursor protein by exact substring containment; within that subset the second-layer label coincides exactly with precursor identity, all 140 human-insulin fragments carrying the type-1 label and all 76 bovine milk-protein fragments the type-2 label, without exception. Accuracy tracks identity to the training set, rising from a Matthews correlation coefficient (MCC) of 0.39–0.43 below 50% identity to 0.92–0.96 between 70% and 90%, and peptide length alone reaches MCC 0.619 on held-out data. Auditing sixteen further peptide benchmarks shows the coupling is not confined to this resource: fragment families share a label more often than chance in eleven of fourteen testable datasets, and not in three, so the property is common rather than universal. Rebuilding the published architecture on identical folds shows its advantage over a single classifier is a function of the split: present under random partitioning, absent once homologues are separated. ADP-Hybrid, one tree-ensemble classifier per layer, reaches MCC 0.843 on layer 1 against a published 0.841 at 21–38 times the inference throughput, and 0.801 against 0.858 on layer 2. We release the protocol and the controls that expose these properties.