Abstract
In the current field of medical research, particularly in the development of targeted medications for cancer and neurodegenerative diseases, tasks are often accomplished through protein-protein interactions (PPI). Consequently, mastering intracellular protein interactions is becoming increasingly important. This study developed three innovative deep learning models: SecPPIS, DisPPIS, and AngPPIS specifically designed to predict features related to proteins' secondary structures, spatial distances, and spatial angles, respectively. Our models underwent comprehensive training and testing, assessing their practicality through their performance in real-world applications. Compared with existing technologies our models demonstrated superior performance levels. These achievements provide effective technical support for the study of protein interactions and related drug development.