Detecting antimicrobial peptides by exploring the mutual information of their sequences.

The rise of antibiotic resistance in pathogenic bacteria is a growing concern for every part of the world. The present study shows the prediction efficiency of mutual information for the classification of antimicrobial peptides. The proven role of antimicrobial peptides (AMPs) to fight against multidrug-resistant pathogens and AMP's low toxic properties laid the foundation of computational methods to play their role in detecting AMPs from non-AMPs. Mutual information vectors (MIV) were created for AMP/non-AMP sequences and then fed to different machine learning classifiers out of which a random forest (RF) classifier showed best results for predicting AMPs. Random forest classifiers were evaluated on benchmark datasets by 10-fold cross-validation. The proposed MIV-RF method showed better prediction accuracy, MCC (Matthews correlation coefficient), and AUC-ROC (Area Under The Curve-Receiver Operating Characteristics) than available methods for detecting AMPs.Communicated by Ramaswamy H. Sarma.