Trans-GP: Uncertainty-Calibrated Antibody-Antigen Binding Classification Using Protein Language Models

Accurate and reliable prediction of antibody-antigen binding interactions informed by affinity measurements remains an important challenge in chemical information modeling, with growing concern over the reliability and calibration of confidence estimates in data-driven predictions. Here, we present Trans-GP, a sequence-driven framework that integrates frozen protein language model embeddings with a Gaussian process classifier to jointly perform affinity-informed binary binding classification and quantitative uncertainty calibration. Across multiple benchmark data sets, including SAbDab, SKEMPI2.0, and ABbind, Trans-GP achieves competitive predictive performance while consistently improving calibration quality relative to conventional neural network models. By providing statistically well-calibrated probabilistic confidence estimates, Trans-GP supports reliable screening and prioritization of antibody candidates in chemical information workflows.