Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance

Protein-protein interactions (PPIs) are pivotal in cellular processes, influencing a wide range of functions, from metabolism to immune responses. Despite the advancements in experimental techniques for PPI detection, their inherent limitations, such as high false-positive rates and significant resource demands, necessitate the development of computational approaches. This study presents a novel computational model named MFPIC (Multi-Feature Protein Interaction Classifier) for predicting PPIs, integrating enhanced sequence-based features, including a novel spaced conjoint triad (SCT) and amino acid pairwise distance (AAPD), with existing methods such as position-specific scoring matrices (PSSM) and AAindex-based features. The SCT captures complex sequence motifs by considering non-adjacent amino acid interactions, while AAPD provides critical spatial information about amino acid residues within protein sequences. The proposed model was evaluated across three benchmark datasets-<i>Saccharomyces cerevisiae</i>, <i>Helicobacter pylori</i>, and human proteins-demonstrating superior performance in comparison to state-of-the-art models. The results underscore the efficacy of integrating diverse and complementary features, achieving significant improvements in predictive accuracy, with the model achieving 95.90%, 99.33%, and 90.95% accuracy on the <i>Saccharomyces cerevisiae</i>, <i>Helicobacter pylori</i>, and human dataset, respectively. This approach not only enhances our understanding of PPI mechanisms but also offers valuable insights for the development of targeted therapeutic strategies.