Back to Search
Start Over
Ranking protein-protein models with large language models and graph neural networks
- Publication Year :
- 2024
-
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<br />Comment: 14 pages. Detailed protocol to use our DeepRank-GNN-esm software to analyse models of protein-protein complexes
- Subjects :
- Quantitative Biology - Biomolecules
Computer Science - Artificial Intelligence
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2407.16375
- Document Type :
- Working Paper