iASMP: An interpretable in silico predictive tool focusing on species-specific antimicrobial peptides.

Antimicrobial peptides (AMP), a crucial part of the innate immune system, have been exploited as promising candidates for antibacterial agents. Many researchers are devoting their efforts to developing novel antimicrobial peptides in recent decades. In this term, many computational approaches have been developed to identify potential AMP accurately. However, finding peptides specific to a particular bacterial species is challenging. Streptococcus mutans is a pathogen with an apparent cariogenic effect, and it is of great significance to study AMP that inhibit S. mutans for the prevention and treatment of caries. In this study, we proposed a sequence-based machine learning model, namely iASMP, to identify potential anti-S. mutans peptides exactly. After collecting peptides that had potency against S. mutans (named ASMP), the performances of models were compared by utilizing multiple feature descriptors and different classification algorithms. Among the baseline predictors, the model integrating the ET algorithm and the hybrid features exhibited optimal results. The feature selection method was utilized to remove redundant feature information to improve the model performance further. Finally, the proposed model achieved the maximum ACC of 0.962 on the training dataset and performed on the testing dataset with an ACC of 0.750. The results demonstrated that iASMP had excellent predictive performance and was suitable for identifying potential ASMP. Furthermore, we also visualized the selected features and rationally explained the impact of individual features on the model output.