Antimicrobial peptides (AMPs) are compounds, which have inhibitory activity against microorganisms. In the last decades, AMPs have become powerful alternative agents that have met the need for novel anti-infectives to overcome increasing antibiotic resistance problems. Moreover, recent epidemics and pandemics are increasing the popularity of AMPs, due to the urgent necessity for effective antimicrobial agents in combating the new emergence of microbial diseases. AMPs inhibit a wide range of microorganisms through diverse and special mechanisms by targeting mainly cell membranes or specific intracellular components. In addition to extraction from natural sources, AMPs are produced in various hosts using recombinant methods. More recently, the synthetic analogues of AMPs, designed with some modifications, are predicted to overcome the limitations of stability, toxicity and activity associated with natural AMPs. AMPs have potential applications as antimicrobial agents in food, agriculture, environment, animal husbandry and pharmaceutical industries. In this review, we have provided an overview of the structure, classification and mechanism of action of AMPs, as well as discussed opportunities for their current and potential applications.

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.