ABAG-Rank: Improving Model Selection of AlphaFold Antibody–Antigen Complexes by Learning to Rank

MOTIVATION: AlphaFold has transformed structural biology with an unprecedented accuracy in modelling protein structures and their interactions with biomolecules, with AlphaFold3 (AF3) achieving state-of-the-art performance. However, AF3 and other methods often struggle to accurately predict the structure of protein complexes that lack strong co-evolutionary information, such as antibody-antigen (Ab-Ag) complexes. One of the fundamental issues is that AF3 often generates accurate predictions, but fails to reliably distinguish them from the much larger set of incorrect ones. RESULTS: To address this, we propose ABAG-Rank, a deep neural network that provides an efficient and robust solution for model selection of Ab-Ag interactions from a pool of structural ensembles predicted with AlphaFold. Built on the permutation-invariant DeepSets architecture, ABAG-Rank can process variable-sized ensembles of structural decoys and is directly applicable to prediction settings in which the number of candidates may vary. We train a model on a redundancy-reduced set of all known antibody-antigen complexes and find that simple geometric descriptors, along with confidence scores from AlphaFold, provide rich information about interface quality without requiring intensive physics-based calculations. Our experiments demonstrate that ABAG-Rank significantly outperforms AF3 internal scoring and the ranking performance of existing deep learning baselines. AVAILABILITY AND IMPLEMENTATION: Source code can be found at: https://github.com/tadteo/ABAG-Rank or on Zenodo at https://doi.org/10.5281/zenodo.21132090. SUPPLEMENTARY MATERIAL: Supplementary data are available at Bioinformatics online.