AF-RECAL: Multi-Modal Structural Recalibration of AlphaFold 3 Hallucinations in Intrinsically Disordered Human Disease Targets

AlphaFold 3 (AF3) has transformed structural bioinformatics through generative diffusion modeling of biomolecular assemblies. However, intrinsically disordered regions (IDRs)—which comprise over 30% of the human proteome and mediate essential cell signaling, oncogenesis, and phase separation—violate the classic thermodynamic energyminimization assumptions inherent to static structure prediction. In the absence of explicit solvent and physiological binding partners, AF3's diffusion trunk exhibits a pronounced compaction bias, collapsing unconstrained polypeptide chains into artificial, rigid secondary structures (α-helices) with spuriously elevated confidence (pLDDT ≥ 70–90).Here, we present AF-RECAL, an automated multi-modal machine learning meta-evaluator designed to audit and recalibrate AlphaFold 3 confidence metrics. Evaluating 53,035 residues across 118 full-length human disease proteins against experimental DisProt ground truth, we observe that AF3 assigns confident structure (pLDDT ≥ 70) to 29.86% of all verified disordered residues (2,649 / 8,871), consistent with independent literature reporting structural mismatches in 22% of IDR residues at pLDDT ≥ 80 (23.47% in our cohort). AF-RECAL addresses these errors by cross-examining three complementary information streams: (1) AF3 internal spatial geometry via Predicted Aligned Error (PAE) multi-scale matrices and a local-to-long-range contact density ratio; (2) evolutionary sequence representations and masked-languagemodel Shannon entropy from Meta's ESM-2 (650M parameter) protein language model; and (3) windowed biophysical polymer chemistry.Under strict ≤ 30% sequence identity clustering (114 independent clusters, eliminating homology leakage across isoforms and paralogs), AF-RECAL achieves an out-of-fold AUROC of 0.7721 and a Precision-Recall AUC (PR-AUC) of 0.4063, improving under full Leave-One-Cluster-Out (LOCO) cross-validation across all 114 clusters to 0.7888 AUROC and 0.4205 PR-AUC (mean per-cluster PR-AUC: 0.5131 ± 0.3437). Adding 3D spatial geometry to an identical ESM-2 650M + chemistry baseline yields an empirical +27.6% relative gain in PR-AUC (0.3183 → 0.4063, reaching0.4205 under LOCO-CV); under cluster-level paired bootstrap testing across 114 clusters, this difference exhibits p = 0.1380 (95% CI: [-0.0246, 0.2083]), establishing a strong positive empirical trend. AF-RECAL achieves an uncorrected nominal PR-AUC gain over metapredict v3 (0.3014, p = 0.0520) and native AF3 (0.2889, p = 0.0320). A verified 100iteration Y-randomization permutation test confirms biological learning over chance (z = +75.11σ AUROC, z = +130.83σ PR-AUC, p = 0.0000), and probability reliability analysis demonstrates a Brier calibration score of 0.1653, outperforming metapredict v3 (0.2253). Across operational high-confidence regimes (70.6% of residues), AF-RECAL achieves 84.58% accuracy, providing structural biologists a practical, post-hoc quality filter to flag spurious structural features before downstream virtual screening and experimental validation.