Predicting Antibody–Antigen Mutation ΔΔ G via Side-Specific Protein Language Models and Paired Geometric Graph Learning
Mutation-induced changes in binding free energy (ΔΔG) at antibody–antigen interfaces are important for antibody optimization, mutational scanning, and viral immune escape assessment. However, computational prediction remains challenging because antibodies and antigens have distinct sequence backgrounds, mutation effects are often localized at interfaces, and related complexes may remain across training and evaluation partitions. We present AbAgMut-GNN as a task-oriented paired graph framework that coordinates established sequence and geometric learning components around explicit comparison of wild-type (WT) and mutant (MUT) antibody–antigen complexes. AntiBERTy and ESM2 provide frozen residue-level embeddings for antibody and antigen chains, respectively, while mutation-centered, interface-aware, paired-residue, and contact-delta representations capture local perturbations and interaction remodeling. We evaluate AbAgMut-GNN under four complementary internal settings, including the PDB-based split, the complex-cluster split, the antibody-family preserving validation split, and the antigen-cluster-preserving validation split. Under the complex-cluster split, AbAgMut-GNN achieves Pearson correlation coefficients of 0.5841 on AB-Bind and 0.5480 on SKEMPI v2.0. External validation on SARS-CoV-2 and influenza antibody–antigen systems further shows useful mutation-effect correlation trends, although absolute-error performance varies across target systems. Contact-masking and residue-class enrichment analyses indicate that model-derived importance patterns are associated with biologically relevant interface interactions. Overall, AbAgMut-GNN is best viewed as a task-oriented computational tool for trend-level mutation ranking and pre-experimental candidate prioritization rather than as a high-precision substitute for quantitative biophysical measurement.