PepDDG: Peptide–Protein Binding ΔΔ𝐺 Prediction via Information Channel Decomposition

Predicting mutation-induced changes in peptide–protein binding affinity ($\Delta \Delta G$) is central to therapeutic peptide optimization, but peptide-specific predictors remain limited by scarce labels, target overlap in supervised benchmarks, and costly molecular simulations. We introduce PepDDG, a zero-shot, training-free predictor that ranks peptide mutations by decomposing binding perturbations into three complementary information channels: energetic perturbation, geometric environment, and evolutionary compatibility. Each channel is computed from a wild-type complex structure, transformed into rank space, and combined by non-parametric Borda aggregation, without fitting fusion weights or fine-tuning neural predictors. PepDDG is motivated by rank-covariance analysis showing cross-channel fusion can improve rank correlation when channels retain complementary signal, whereas increasingly expensive refinement of a single energetic channel has diminishing returns. Empirically, the channels are individually moderate but capture distinct mutation regimes, and rank fusion consistently outperforms single-channel variants and training-free baselines. On our curated benchmark of 332 peptide-chain mutations across 33 targets, PepDDG reaches Spearman $\rho =0.619$; the calibrated PepDDG-Cal variant reaches $\rho =0.691$. Performance remains strong on short and cyclic peptide subsets, with $\rho =0.813$ and $0.872$, respectively. These results support complementary evidence fusion on PDB-derived complex structures as a practical alternative to costly same-channel physical refinement for peptide $\Delta \Delta G$ ranking; a separate diagnostic shows that predicted structures can be used when no crystal structure is provided at inference time.