تقرير
Ranking protein-protein models with large language models and graph neural networks
| العنوان: | Ranking protein-protein models with large language models and graph neural networks |
|---|---|
| المؤلفون: | Xu, Xiaotong, Bonvin, Alexandre M. J. J. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence |
| الوصف: | 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 Comment: 14 pages. Detailed protocol to use our DeepRank-GNN-esm software to analyse models of protein-protein complexes |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.16375 |
| رقم الأكسشن: | edsarx.2407.16375 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |