With the advancement in cellular biology, the use of antimicrobial peptides (AMPs) against many drug-resistant pathogens has increased. AMPs have a broad range of activity and can work as antibacterial, antifungal, antiviral, and sometimes even as anticancer peptides. The traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab experiments. Such experiments are both time-consuming and expensive. With the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such problems. This study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and non-AMPs. The proposed methodology uses machine learning algorithms to predict such sequences. A dataset was formulated based on 1902 samples of AMPs and 3997 samples of non-AMPs. Machine learning algorithms are trained on a fixed number of succinct coefficients retaining sequence and composition information of primary structures. The features are extracted using position relative incidence and statistical moments. System performance is validated via various validation tests including a 10-fold cross-validation approach. An overall accuracy of 95.43% was achieved. A comparison of results with existing methodologies shows that the proposed methodology outperformed existing methodologies in terms of prediction accuracy.

Antimicrobial peptides; multidrug-resistant; antiviral; antibacterial; cytokine; classification