PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction
Fig 2
Performance of PepLM-GNN against other baseline methods on four independent test datasets.
The four test datasets include: Test1440 (1440 positive peptide-protein pairs and 1440 negative pairs, sourced from the RCSB PDB database, January 2023-July 2024), LEADS-PEP (52 positive pairs and 52 negative pairs, a classic benchmark for evaluating peptide-protein docking performance), Test251 (249 positive pairs and 249 negative pairs), and Test167 (255 positive pairs and 255 negative pairs, derived from the RCSB PDB database, October-December 2022). PepLM-GNN’, DeepGNHV, Deep-GNN-esm are only applied to test subsets with available structural data. All performance metrics represent the mean values averaged across the five models derived from five-fold cross-validation on each independent test set.