Explainable protein-protein binding affinity prediction via fine-tuning protein language models

Predicting protein-protein binding affinity from sequence alone remains a bottleneck for antibody optimization, biologics design and large-scale affinity modelling. Structure-based methods achieve high accuracy but cannot scale when complex structures are unavailable. Here we present a framework that reframes affinity prediction as metric learning: two proteins are projected into a shared latent space in which cosine similarity directly correlates with experimental binding affinity, and the protein language model encoder is adapted through parameter-efficient fine-tuning (PEFT). On the PPB-Affinity benchmark, the model achieves Pearson r = 0.89 on a random split, generalises to evolutionarily distant proteins (r = 0.61 at <30% sequence identity) and surpasses structure-based deep learning baselines across biological subgroups, without any three-dimensional input. On the strictly de-overlapped AB-Bind dataset, few-shot adaptation with 30% of assay data (Pearson r = 0.756, RMSE = 0.688) outperforms methods trained on 90% of data; consistent gains are observed across nine diverse AbBiBench deep-mutational-scanning assays with 10-30% labelled variants. Residue-level explainability reveals that the model concentrates importance on interface-localised residues aligned with experimentally validated interaction hotspots across enzyme-inhibitor, and antibody-antigen systems. Together, these results establish a scalable, explainable and data-efficient route to protein-protein binding affinity prediction and therapeutic antibody optimisation from sequence alone.