PROTEUS: Predicting How Post-Translational Modifications Alter Drug Binding Affinity

Post-translational modifications (PTMs) dynamically reshape the binding landscapes of protein drug targets, yet no computational method predicts how specific PTMs alter drug binding affinity. We introduce PROTEUS, a multimodal deep learning framework that fuses protein language model embeddings, 3D protein contact graphs, PTM-site annotations, and drug molecular graphs to directly predict ΔΔG---the change in binding free energy caused by a PTM. A Normal-Inverse-Gamma evidential head provides calibrated epistemic and aleatoric uncertainty without ensemble overhead. To enable systematic study of this previously unaddressed prediction task, we construct PTM-BIND-Bench, the first benchmark linking PTM states to quantitative binding affinity changes: 831 curated protein-PTM-drug triples spanning 48 kinases, 120 inhibitors, and 5 PTM types. Under protein-level GroupKFold cross-validation, PROTEUS achieves Spearman ρ = 0.527 and concordance index = 0.715, outperforming all baselines with non-overlapping 95% CIs. On 10 experimentally measured ΔΔG values, the model attains Pearson r = 0.967 with 100% direction accuracy. Zero-shot evaluation on 17 novel drugs yields 88.9% direction accuracy, and split conformal prediction provides distribution-free 90% intervals of ±0.64 kcal/mol.