  Protein-protein interactions (PPIs) are associated with various diseases,
including cancer, infections, and neurodegenerative disorders. Obtaining
three-dimensional structural information on these PPIs serves as a foundation
to interfere with those or to guide drug design. Various strategies can be
followed to model those complexes, all typically resulting in a large number of
models. A challenging step in this process is the identification of good models
(near-native PPI conformations) from the large pool of generated models. To
address this challenge, we previously developed DeepRank-GNN-esm, a graph-based
deep learning algorithm for ranking modelled PPI structures harnessing the
power of protein language models. Here, we detail the use of our software with
examples. DeepRank-GNN-esm is freely available at
https://github.com/haddocking/DeepRank-GNN-esm
