Ranking protein-protein models with large language models and graph neural networks
<|reference_start|>Ranking protein-protein models with large language models and graph neural networks: 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<|reference_end|>
@article{xu2024ranking,
    title={Ranking protein-protein models with large language models and graph
  neural networks},
    author={Xiaotong Xu and Alexandre M.J. J. Bonvin},
    journal={arXiv preprint arXiv:2407.16375},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16375},
    primaryClass={q-bio.BM cs.AI}
}