Ranking protein-protein models with large language models and graph neural networks

• URL: http://arxiv.org/abs/2407.16375v1
• Date: Tue, 23 Jul 2024 10:51:35 GMT
• Title: Ranking protein-protein models with large language models and graph neural networks
• Authors: Xiaotong Xu, Alexandre M. J. J. Bonvin,
• Abstract summary: DeepRank-GNN-esm is a graph-based deep learning algorithm for ranking modelled PPI structures. Here, we detail the use of our software with examples.
• Score: 49.1574468325115
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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

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