Bioinformatics (Oxford, England) · 2026
Phys-AbGAT: a physics-informed multi-task graph attention network for robust antibody-antigen binding affinity prediction.
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Yang Yifei, Xu Linnan, Lu Junbiao, Li Xiaoyan, Zheng Wei, Gao Jianzhao
Аннотация
MOTIVATION: Accurate prediction of antibody-antigen binding affinity is essential for therapeutic antibody design. Existing methods often model the dissociation constant (KD) and Gibbs free energy (ΔG) independently, overlooking their thermodynamic relationship and complex paratope-epitope spatial interactions.
RESULTS: We introduce Phys-AbGAT, a physics-informed multi-task graph attention network that represents the antibody-antigen interface as a spatial graph. It integrates ESM2 evolutionary embeddings with radial basis functions and employs a thermodynamically motivated auxiliary loss to encourage coupling between log10(KD) and ΔG. On an independent 42-complex benchmark, Phys-AbGAT achieved Pearson correlation coefficients of 0.569 for log10(KD) and 0.573 for ΔG. In matched-set comparisons, Phys-AbGAT achieved numerically lower RMSE and higher PCC than four baseline methods. The differences remained significant after Holm correction for MVSF-AB and PPA-Pred2, but not for AREA-AFFINITY or CSM-AB. Higher attention scores were assigned to several aromatic paratope residues, providing model-level hypotheses for structural inspection. These results support Phys-AbGAT as an interpretable multi-task geometric framework for joint prediction of two affinity-related quantities.
AVAILABILITY AND IMPLEMENTATION: Code and data are available at https://github.com/cliffgao/Phys-AbGAT.
RESULTS: We introduce Phys-AbGAT, a physics-informed multi-task graph attention network that represents the antibody-antigen interface as a spatial graph. It integrates ESM2 evolutionary embeddings with radial basis functions and employs a thermodynamically motivated auxiliary loss to encourage coupling between log10(KD) and ΔG. On an independent 42-complex benchmark, Phys-AbGAT achieved Pearson correlation coefficients of 0.569 for log10(KD) and 0.573 for ΔG. In matched-set comparisons, Phys-AbGAT achieved numerically lower RMSE and higher PCC than four baseline methods. The differences remained significant after Holm correction for MVSF-AB and PPA-Pred2, but not for AREA-AFFINITY or CSM-AB. Higher attention scores were assigned to several aromatic paratope residues, providing model-level hypotheses for structural inspection. These results support Phys-AbGAT as an interpretable multi-task geometric framework for joint prediction of two affinity-related quantities.
AVAILABILITY AND IMPLEMENTATION: Code and data are available at https://github.com/cliffgao/Phys-AbGAT.