PCLT-PPI: Predicting Multi-Type Interactions Between Proteins Based on Point Cloud Structure and Local Topology Preservation

Protein-protein interactions (PPIs) play a crucial role in cellular biochemical reactions. Computationally mining PPI can help us better understand cellular regulatory mechanisms. Most existing methods focus on the linear structure of proteins, ignoring the influence of native spatial structure on their properties. Furthermore, when neural networks are used to learn protein embeddings, the nonlinear transformations may change the topological relationships between proteins. To address the above issues, we propose a PPI prediction method based on protein point cloud structure and local topology preservation, naming it PCLT-PPI. It extracts structural features from protein point cloud structures and relational features through graph neural networks. Throughout the process, PCLT-PPI maintains the local topology of proteins in their origin and embedding spaces. Experimental results show that, under three test set partition modes (Random, BFS, DFS) and four evaluation metrics (F1, AUC, AUPR, Hamming Loss), PCLT-PPI performs better than several state-of-the-art PPI prediction methods, especially when predicting protein PPIs that are not visible during training, exhibiting stronger robustness and higher generalization ability. The results also demonstrate that point cloud structure and local topology preservation can improve PPI prediction performance, which may provide a reference for subsequent related research.