Sequence-based Prediction of Antimicrobial Peptides with CatBoost Classifier

Antimicrobial resistance is one of the most serious issue for human health. Compared to existing antibiotics, antimicrobial peptides have the advantage of efficient killing microbes and other pathogens without inducing drug resistance. Large-scale experimental methods to characterize AMPs require wet-lab resources and longer time. In silico prediction of AMP, on the other hand, is an attractive strategy to lower the cost and time in the discovery of new AMPs. In this study, we proposed a CatBoost model for AMP prediction. We included various features for numerical representation of peptides, and then employed a systematic approach to select 130 important features for our machine learning models. The CatBoost model achieves an accuracy, F1-score, MCC, and AUC of 0.758, 0.750, 0.518, and 0.831, respectively, for cross validation. For an independent test based on 188 peptide sequences, the proposed model achieves an accuracy, MCC, and AUC of 0.814, 0.632, and 0.884, respectively, all of which are the best compared to five state-of-art methods. Our model improves the MCC of five existing methods by 2.6% to 21.1%, and improves the AUC of them by 1.3% to 13.3%, respectively. The results demonstrate that our CatBoost model is capable of yielding reliable results, and can be of great help in discovering novel AMPs.