Predicting Antibody-Antigen Affinity with a Dual-Level Representation Model.

MOTIVATION: Protein language models (pLMs) are critical for modeling antibody-antigen interactions, yet sequence-based affinity prediction remains a key challenge, particularly when structural data are scarce. Existing methods often struggle to fully exploit sequence information, limiting their applicability across diverse antibody formats such as single-domain antibodies (sdAbs). RESULTS: We propose DLP-Affinity, a dual-level deep learning framework for accurate sequence-based affinity prediction. It leverages two complementary modules: Residue-to-Residue (R2R) to capture local interface contacts, and Global Stochastic Projection Embedding (GSPE) to represent global protein properties. Utilizing a fine-tuned protein language model, our approach achieves state-of-the-art performance on the general AB-Bind dataset (reducing mean absolute error by up to 20.9%) and delivers highly competitive results on the sdAb-DB dataset. This provides a robust tool for sequence-based antibody affinity prediction. AVAILABILITY AND IMPLEMENTATION: The source code and datasets for DLP-Affinity are freely available at https://github.com/Zy-Wang-bit/DLP_Affinity and archived on Zenodo at https://doi.org/10.5281/zenodo.18437656. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.