The article presents a computational method for predicting protein-protein interactions (PPIs) based solely on sequence information. The method combines a support vector machine (SVM) with a kernel function and a conjoint triad feature to describe amino acids. Over 16,000 diverse PPI pairs were used to construct the universal model, which outperforms other sequence-based PPI prediction methods. The method effectively maps different types of PPI networks, suggesting its potential for exploring networks of newly discovered proteins with unknown biological functions. The authors also demonstrate that supplementary experimental information can enhance the prediction accuracy. The study highlights the importance of PPI networks in understanding biological systems and their applications in drug discovery.The article presents a computational method for predicting protein-protein interactions (PPIs) based solely on sequence information. The method combines a support vector machine (SVM) with a kernel function and a conjoint triad feature to describe amino acids. Over 16,000 diverse PPI pairs were used to construct the universal model, which outperforms other sequence-based PPI prediction methods. The method effectively maps different types of PPI networks, suggesting its potential for exploring networks of newly discovered proteins with unknown biological functions. The authors also demonstrate that supplementary experimental information can enhance the prediction accuracy. The study highlights the importance of PPI networks in understanding biological systems and their applications in drug discovery.