Ranking protein-protein models with large language models and graph neural networks
This work addresses a critical bottleneck in structural biology for drug design, but it is incremental as it builds on previous methods.
The paper tackles the challenge of identifying near-native protein-protein interaction conformations from large pools of generated models, detailing the use of DeepRank-GNN-esm, a graph-based deep learning algorithm that leverages protein language models for ranking.
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